Computational Tools for Linguists

Computational Tools for Linguists
Inderjeet Mani Georgetown University

Topics Computational tools for
manual and automatic annotation of linguistic data exploration of linguistic hypotheses Case studies Demonstrations and training Inter-annotator reliability Effectiveness of annotation scheme Costs and tradeoffs in corpus preparation

Outline Topics Concordances Data sparseness Chomsky’s Critique Ngrams
Mutual Information Part-of-speech tagging Annotation Issues Inter-Annotator Reliability Named Entity Tagging Relationship Tagging Case Studies metonymy adjective ordering Discourse markers: then TimeML

Corpus Linguistics Use of linguistic data from corpora to test linguistic hypotheses => emphasizes language use Uses computers to do the searching and counting from on-line material Faster than doing it by hand! Check? Most typical tool is a concordancer, but there are many others! Tools can analyze a certain amount, rest is left to human! Corpus Linguistics is also a particular approach to linguistics, namely an empiricist approach Sometimes (extreme view) opposed to the rationalist approach, at other times (more moderate view) viewed as complementary to it Cf. Theoretical vs. Applied Linguistics

Empirical Approaches in Computational Linguistics
Empiricism – the doctrine that knowledge is derived from experience Rationalism: the doctrine that knowledge is derived from reason Computational Linguistics is, by necessity, focused on ‘performance’, in that naturally occurring linguistic data has to be processed Naturally occurring data is messy! This means we have to process data characterized by false starts, hesitations, elliptical sentences, long and complex sentences, input that is in a complex format, etc. The methodology used is corpus-based linguistic analysis (phonological, morphological, syntactic, semantic, etc.) carried out on a fairly large scale rules are derived by humans or machines from looking at phenomena in situ (with statistics playing an important role)

Example: metonymy Metonymy: substituting the name of one referent for another George W. Bush invaded Iraq A Mercedes rear-ended me Is metonymy involving institutions as agents more common in print news than in fiction? “The X Vreporting” Let’s start with: “The X said” This pattern will provide a “handle” to identify the data

Exploring Corpora Datasets Metonymy Test using Corpora
Metonymy Test using Corpora _MST.html

‘The X said’ from Concordance data
Words Freq Freq/ M Words Fiction 1870 1.7M 60 35 Fiction 2000 1.5M 219 146 Print News 1.9M 915 481 The preference for metonymy in print news arises because of the need to communicate Information from companies and governments.

Chomsky’s Critique of Corpus-Based Methods
1. Corpora model performance, while linguistics is aimed at the explanation of competence If you define linguistics that way, linguistic theories will never be able to deal with actual, messy data Many linguists don’t find the competence-performance distinction to be clear-cut. Sociolinguists have argued that the variability of linguistic performance is systematic, predictable, and meaningful to speakers of a language. Grammatical theories vary in where they draw the line between competence and performance, with some grammars (such as Halliday’s Systemic Grammar) organized as systems of functionally-oriented choices.

Chomsky’s Critique (concluded)
2. Natural language is in principle infinite, whereas corpora are finite, so many examples will be missed Excellent point, which needs to be understood by anyone working with a corpus. But does that mean corpora are useless? Introspection is unreliable (prone to performance factors, cf. only short sentences), and pretty useless with child data. Also, insights from a corpus might lead to generalization/induction beyond the corpus– if the corpus is a good sample of the “text population” 3. Ungrammatical examples won’t be available in a corpus Depends on the corpus, e.g., spontaneous speech, language learners, etc. The notion of grammaticality is not that clear Who did you see [pictures/?a picture/??his picture/*John’s picture] of? ARG/ADJUNCT example

Which Words are the Most Frequent?
Common Words in Tom Sawyer (71,730 words), from Manning & Schutze p.21 Will these counts hold in a different corpus (and genre, cf. Tom)? What happens if you have 8-9M words? (check usage demo!)

Data Sparseness Many low-frequency words Fewer high-frequency words.
Word Frequency Number of words of that frequency 1 3993 2 1292 3 664 4 410 5 243 6 199 7 172 8 131 9 82 10 91 11-50 540 51-100 99 >100 102 Many low-frequency words Fewer high-frequency words. Only a few words will have lots of examples. About 50% of word types occur only once Over 90% occur 10 times or less. So, there is merit to Chomsky’s 2nd objection Frequency of word types in Tom Sawyer, from M&S 22.

Zipf’s Law: Frequency is inversely proportional to rank
turned 51 200 10200 you’ll 30 300 9000 name 21 400 8400 comes 16 500 8000 group 13 600 7800 lead 11 700 7700 friends 10 800 begin 9 900 8100 family 8 1000 brushed 4 2000 sins 2 3000 6000 could 4000 applausive 1 Empirical evaluation of Zipf’s Law on Tom Sawyer, from M&S 23.

Illustration of Zipf’s Law (Brown Corpus, from M&S p. 30)
logarithmic scale See also

Tokenizing words for corpus analysis
1. Break on Spaces? 犬に当る男の子は私の兄弟である。 inuo butta otokonokowa otooto da Periods? (U.K. Products) Hyphens? data-base = database = data base Apostrophes? won’t, couldn’t, O’Riley, car’s 2. should different word forms be counted as distinct? Lemma: a set of lexical forms having the same stem, the same pos, and the same word-sense. So, cat and cats are the same lemma. Sometimes, words are lemmatized by stemming, other times by morphological analysis, using a dictionary and/or morphological rules 3. fold case or not (usually folded)? The the THE Mark versus mark One may need, however, to regenerate the original case when presenting it to the user

Counting: Word Tokens vs Word Types
Word tokens in Tom Sawyer: 71,370 Word types: (i.e., how many different words) 8,018 In newswire text of that number of tokens, you would have 11,000 word types. Perhaps because Tom Sawyer is written in a simple style.

Inspecting word frequencies in a corpus
Usage demo:

Ngrams Sequences of linguistic items of length n See count.pl

A test for association strength: Mutual Information
Data from (Church et al. 1991) 1988 AP corpus; N=44.3M

Interpreting Mutual Information
High scores, e.g., strong supporter (8.85) indicates strongly associated in the corpus MI is a logarithmic score. To convert it, recall that X=2 log2X so,  So this is 461 X chance. Low scores – powerful support (1.74): this is 3X chance, since  3 I fxy fx fy x y ,428 powerful support I = log2 (2N/1984*13428) = 1.74 So, doesn’t necessarily mean weakly associated – could be due to data sparseness

Mutual Information over Grammatical Relations
Parse a corpus Determine subject-verb-object triples Identify head nouns of subject and object NPs Score subj-verb and verb-obj associations using MI

Demo of Verb-Subj, Verb-Obj Parses
Who devours or what gets devoured? Demo:

MI over verb-obj relations
Data from (Church et al. 1991)

A Subj-Verb MI Example: Who does what in news?
executive police politician reprimand shoot clamor 16.94 conceal raid jockey 17.53 bank arrest wrangle 17.59 foresee detain woo 18.92 conspire disperse exploit 19.57 convene interrogate brand 19.65 plead swoop behave 19.72 sue evict dare 19.73 answer bundle sway 19.77 commit manhandle criticize 19.78 worry search flank 19.87 accompany confiscate proclaim 19.91 own apprehend annul 19.91 witness round favor 19.92 Data from (Schiffman et al. 2001)

‘Famous’ Corpora Must see: http://www.ldc.upenn.edu/Catalog/
Brown Corpus British National Corpus International Corpus of English Penn Treebank Lancaster-Oslo-Bergen Corpus Canadian Hansard Corpus U.N. Parallel Corpus TREC Corpora MUC Corpora English, Arabic, Chinese Gigawords Chinese, ArabicTreebanks North American News Text Corpus Multext East Corpus – ‘1984’ in multiple Eastern/Central European langauges

Links to Corpora Corpora:
Linguistic Data Consortium (LDC) Oxford Text Archive Project Gutenberg CORPORA list Other: Chris Manning’s Corpora Page Michael Barlow’s Corpus Linguistics page Cathy Ball’s Corpora tutorial tml

Summary: Introduction
Concordances and corpora are widely used and available, to help one to develop empirically-based linguistic theories and computer implementations The linguistic items that can be counted are many, but “words” (defined appropriately) are basic items The frequency distribution of words in any natural language is Zipfian Data sparseness is a basic problem when using observations in a corpus sample of language Sequences of linguistic items (e.g., word sequences – n-grams) can also be counted, but the counts will be very rare for longer items Associations between items can be easily computed e.g., associations between verbs and parser-discovered subjs or objs

Using POS in Concordances
Words Freq Freq/ Fiction 2000 N \bdeal_NN 1.5M 115 7.66 VB 14 9.33 Gigaword 10.5M 2857 2.72 139 1.32 deal is more often a verb In Fiction 2000 deal is more often a noun in English Gigaword deal is more prevalent in Fiction 2000 than Gigaword

POS Tagging – What is it? Given a sentence and a tagset of lexical categories, find the most likely tag for each word in the sentence Tagset – e.g., Penn Treebank (45 tags, derived from the 87-tag Brown corpus tagset) Note that many of the words may have unambiguous tags Example Secretariat/NNP is/VBZ expected/VBN to/TO race/VB tomorrow/NN People/NNS continue/VBP to/TO inquire/VB the/DT reason/NN for/IN the/DT race/NN for/IN outer/JJ space/NN

More details of POS problem
How ambiguous? Most words in English have only one Brown Corpus tag Unambiguous (1 tag) 35,340 word types Ambiguous (2- 7 tags) 4,100 word types = 11.5% 7 tags: 1 word type “still” But many of the most common words are ambiguous Over 40% of Brown corpus tokens are ambiguous Obvious strategies may be suggested based on intuition to/TO race/VB the/DT race/NN will/MD race/NN Sentences can also contain unknown words for which tags have to be guessed: Secretariat/NNP is/VBZ

Different English Part-of-Speech Tagsets
Brown corpus - 87 tags Allows compound tags “I'm” tagged as PPSS+BEM PPSS for "non-3rd person nominative personal pronoun" and BEM for "am, 'm“ Others have derived their work from Brown Corpus LOB Corpus: 135 tags Lancaster UCREL Group: 165 tags London-Lund Corpus: 197 tags. BNC – 61 tags (C5) PTB – 45 tags To see comparisons ad mappings of tagsets, go to

PTB Tagset (36 main tags + 9 punctuation tags)

PTB Tagset Development
Several changes were made to Brown Corpus tagset: Recoverability Lexical: Same treatment of Be, do, have, whereas BC gave each its own symbol Do/VB does/VBZ did/VBD doing/VBG done/VBN Syntactic: Since parse trees were used as part of Treebank, conflated certain categories under the assumption that they would be recoverable from syntax subject vs. object pronouns (both PP) subordinating conjunctions vs. prepositions on being informed vs. on the table (both IN) Preposition “to” vs. infinitive marker (both TO) Syntactic Function BC: the/DT one/CD vs. PTB: the/DT one/NN BC: both/ABX vs. PTB: both/PDT the boys, the boys both/RB, both/NNS of the boys, both/CC boys and girls

PTB Tagging Process Tagset developed
Automatic tagging by rule-based and statistical pos taggers Human correction using an editor embedded in Gnu Emacs Takes under a month for humans to learn this (at 15 hours a week), and annotation speeds after a month exceed 3,000 words/hour Inter-annotator disagreement (4 annotators, eight 2000-word docs) was 7.2% for the tagging task and 4.1% for the correcting task Manual tagging took about 2X as long as correcting, with about 2X the inter-annotator disagreement rate and an error rate that was about 50% higher. So, for certain problems, having a linguist correct automatically tagged output is far more efficient and leads to better reliability among linguists compared to having them annotate the text from scratch!

Automatic POS tagging

A Baseline Strategy Choose the most likely tag for each ambiguous word, independent of previous words i.e., assign each token to the pos-category it occurred in most often in the training set E.g., race – which pos is more likely in a corpus? This strategy gives you 90% accuracy in controlled tests So, this “unigram baseline” must always be compared against

Beyond the Baseline Hand-coded rules Sub-symbolic machine learning

Machine Learning Machines can learn from examples
Learning can be supervised or unsupervised Given training data, machines analyze the data, and learn rules which generalize to new examples Can be sub-symbolic (rule may be a mathematical function) –e.g. neural nets Or it can be symbolic (rules are in a representation that is similar to representation used for hand-coded rules) In general, machine learning approaches allow for more tuning to the needs of a corpus, and can be reused across corpora

A Probabilistic Approach to POS tagging
What you want to do is find the “best sequence” of pos-tags C=C1..Cn for a sentence W=W1..Wn. (Here C1 is pos_tag(W1)). In other words, find a sequence of pos tags C that maximizes P(C| W) Using Bayes’ Rule, we can say P(C| W) = P(W | C) * P(C) / P(W ) Since we are interested in finding the value of C which maximizes the RHS, the denominator can be discarded, since it will be the same for every C So, the problem is: Find C which maximizes P(W | C) * P(C) Example: He will race Possible sequences: He/PP will/MD race/NN He/PP will/NN race/NN He/PP will/MD race/VB He/PP will/NN race/VB W = W1 W2 W3 = He will race C = C1 C2 C3 Choices: C= PP MD NN C= PP NN NN C = PP MD VB C = PP NN VB

Independence Assumptions
P(C1….Cn)  i=1, n P(Ci| Ci-1) assumes that the event of a pos-tag occurring is independent of the event of any other pos-tag occurring, except for the immediately previous pos tag From a linguistic standpoint, this seems an unreasonable assumption, due to long-distance dependencies P(W1….Wn | C1….Cn)  i=1, n P(Wi| Ci) assumes that the event of a word appearing in a category is independent of the event of any other word appearing in a category Ditto However, the proof of the pudding is in the eating! N-gram models work well for part-of-speech tagging

But Data Sparseness Bites Again!
Lexical generation probabilities will lack observations for low- frequency and unknown words Most systems do one of the following Smooth the counts E.g., add a small number to unseen data (to zero counts). For example, assume a bigram not seen in the data has a very small probability, e.g., Backoff bigrams with unigrams, etc. Use lots more data (you’ll still lose, thanks to Zipf!) Group items into classes, thus increasing class frequency e.g., group words into ambiguity classes, based on their set of tags. For counting, alll words in an ambiguity class are treated as variants of the same ‘word’

A Symbolic Learning Method
HMMs are subsymbolic – they don’t give you rules that you can inspect A method called Transformational Rule Sequence learning (Brill algorithm) can be used for symbolic learning (among other approaches) The rules (actually, a sequence of rules) are learnt from an annotated corpus Performs at least as accurately as other statistical approaches Has better treatment of context compared to HMMs rules which use the next (or previous) pos HMMs just use P(Ci| Ci-1) or P(Ci| Ci-2Ci-1) rules which use the previous (next) word HMMs just use P(Wi|Ci)

Brill Algorithm (Overview)
Assume you are given a training corpus G (for gold standard) First, create a tag-free version V of it Notes: As the algorithm proceeds, each successive rule becomes narrower (covering fewer examples, i.e., changing fewer tags), but also potentially more accurate Some later rules may change tags changed by earlier rules 1. First label every word token in V with most likely tag for that word type from G. If this ‘initial state annotator’ is perfect, you’re done! 2. Then consider every possible transformational rule, selecting the one that leads to the most improvement in V using G to measure the error 3. Retag V based on this rule 4. Go back to 2, until there is no significant improvement in accuracy over previous iteration

Brill Algorithm (Detailed)
1. Label every word token with its most likely tag (based on lexical generation probabilities). 2. List the positions of tagging errors and their counts, by comparing with ground-truth (GT) 3. For each error position, consider each instantiation I of X, Y, and Z in Rule template. If Y=GT, increment improvements[I], else increment errors[I]. 4. Pick the I which results in the greatest error reduction, and add to output e.g., VB NN PREV1OR2TAG DT improves 98 errors, but produces 18 new errors, so net decrease of 80 errors 5. Apply that I to corpus 6. Go to 2, unless stopping criterion is reached Most likely tag: P(NN|race) = .98 P(VB|race) = .02 Is/VBZ expected/VBN to/TO race/NN tomorrow/NN Rule template: Change a word from tag X to tag Y when previous tag is Z Rule Instantiation to above example: NN VB PREV1OR2TAG TO Applying this rule yields: Is/VBZ expected/VBN to/TO race/VB tomorrow/NN

Example of Error Reduction
From Eric Brill (1995): Computational Linguistics, 21, 4, p. 7

Example of Learnt Rule Sequence
1. NN VB PREVTAG TO to/TO race/NN->VB 2. VBP VB PREV1OR20R3TAG MD might/MD vanish/VBP-> VB 3. NN VB PREV1OR2TAG MD might/MD not/MD reply/NN -> VB 4. VB NN PREV1OR2TAG DT the/DT great/JJ feast/VB->NN 5. VBD VBN PREV1OR20R3TAG VBZ He/PP was/VBZ killed/VBD->VBN by/IN Chapman/NNP

Handling Unknown Words
Can also use the Brill method Guess NNP if capitalized, NN otherwise. Or use the tag most common for words ending in the last 3 letters. etc. Example Learnt Rule Sequence for Unknown Words

POS Tagging using Unsupervised Methods
Reason: Annotated data isn’t always available! Example: the can Let’s take unambiguous words from dictionary, and count their occurrences after the the .. elephant the .. guardian Conclusion: immediately after the, nouns are more common than verbs or modals Initial state annotator: for each word, list all tags in dictionary Transformation template: Change tag  of word to tag Y if the previous (next) tag (word) is Z, where  is a set of 2 or more tags Don’t change any other tags

Error Reduction in Unsupervised Method
Let a rule to change  to Y in context C be represented as Rule(, Y, C). Rule1: {VB, MD, NN} NN PREVWORD the Rule2: {VB, MD, NN} VB PREVWORD the Idea: since annotated data isn’t available, score rules so as to prefer those where Y appears much more frequently in the context C than all others in  frequency is measured by counting unambiguously tagged words so, prefer {VB, MD, NN} NN PREVWORD the to {VB, MD, NN} VB PREVWORD the since dict-unambiguous nouns are more common in a corpus after the than dict-unambiguous verbs

Summary: POS tagging A variety of POS tagging schemes exist, even for a single language Preparing a POS-tagged corpus requires, for efficiency, a combination of automatic tagging and human correction Automatic part-of-speech tagging can use Hand-crafted rules based on inspecting a corpus Machine Learning-based approaches based on corpus statistics e.g., HMM: lexical generation probability table, pos transition probability table Machine Learning-based approaches using rules derived automatically from a corpus Combinations of different methods often improve performance

Adjective Ordering *A political serious problem
*A social extravagant life *red lovely hair *old little lady *green little men Adjectives have been grouped into various classes to explain ordering phenomena

Collins COBUILD L2 Grammar
qualitative < color < classifying Qualitative – expresses a quality that someone or something has, e.g., sad, pretty, small, etc. Qualitative adjectives are gradable, i.e., the person or thing can have more or less of the quality Classifying – used to identify the class something belongs to, i.e.., distinguishing financial help, American citizens. Classifying adjectives aren’t gradable. So, the ordering reduces to Gradable < color < non-gradable A serious political problem Lovely red hair Big rectangular green Chinese carpet

Vendler 68 A9 < A8 < …A2 < A1x <A1m < …<A1a
A9: probably, likely, certain A8: useful, profitable, necessary A7: possible, impossible A6: clever, stupid, reasonable, nice, kind, thoughtful, considerate A5: ready, willing, anxious A4: easy A3: slow, fast, good, bad, weak, careful, beautiful A2: contrastive/polar adjectives: long-short, thick-thin, big-little, wide-narrow A1j: verb-derivatives: washed A1i: verb-derivatives: washing A1h: luminous A1g: rectangular A1f: color adjectives A1a: iron, steel, metal big rectangular green Chinese carpet

Other Adjective Ordering Theories
Goyvaerts 68 quality < size/length/shape < age < color < naturally < style < general < denominal Quirck & Greenbaum 73 Intensifying perfect < general-measurable careful wealthy < age young old < color < denominal material woollen scarf < denominal style Parisian dress Dixon 82 value < dimension < physical property < speed < human propensity < age < color Frawley 92 value < size < color (English, German, Hungarian, Polish, Turkish, Hindi, Persian, Indonesian, Basque) Collins COBUILD: gradable < color < non-gradable Goyvaerts, Q&G, Dixon: size < age < color Goyvaerts, Q&G: color < denominal Goyvaerts, Dixon: shape < color

Testing the Theories on Large Corpora
Selective coverage of a particular language or (small) set of languages Based on categories that aren’t defined precisely that are computable Based on small large numbers of examples Test gradable < color < non-gradable

Challenges: Data Sparseness
Only some pairs will be present in a given corpus few adjectives on the gradable list may be present Even fewer longer sequences will be present in a corpus Use transitivity? small < red, red < wooden --> small < red < wooden?

Challenges: Tool Incompleteness
Search pattern will return many non-examples Collocations common or marked ones American “green card” national Blue Cross Adjective Modification bright blue POS-tagging errors May also miss many examples

Results from Corpus Analysis
G < C < not G generally holds However, there are exceptions Classifying/Non-Gradable < Color After all, the maple leaf replaced the British red ensign as Canada's flag almost 30 years ago. where he stood on a stage festooned with balloons displaying the Palestinian green, white and red flag Color < Shape paintings in which pink, roundish shapes, enriched with flocking, gel, lentils and thread, suggest the insides of the female body.

Summary: Adjective Ordering
It is possible to test concrete predictions of a linguistic theory in a corpus-based setting The testing means that the machine searches for examples satisfying patterns that the human specifies The patterns can pre-suppose a certain/high degree of automatic tagging, with attendant loss of accuracy The patterns should be chosen so that they provide “handles” to identify the phenomena of interest The patterns should be restricted enough that the number of examples the human has to judge is not infeasible This is usually an iterative process

Mutual Information Part-of-speech tagging Annotation Issues Named Entity Tagging Inter-Annotator Reliability Relationship Tagging Case Studies metonymy adjective ordering Discourse markers: then TimeML

The Art of Annotation 101 Define Goal
Eyeball Data (with the help of Computers) Design Annotation Scheme Develop Example-based Guidelines Unless satisfied/exhausted, goto 1 WriteTraining Manuals Initiate HumanTraining Sessions Annotate Data / Train Computers Computers can also help with the annotation Evaluate Humans and Computers

Annottation Methodology Picture
Raw Corpus Initial Tagger Annotation Guidelines Annotation Editor Machine Learning Program Rule Apply Learned Rules Raw Corpus Annotated Corpus Annotated Corpus Knowledge Base?

Goals of an Annotation Scheme
Simplicity – simple enough for a human to carry out Precision – precise enough to be useful in CLI applications Text-based – annotation of an item should be based on information conveyed by the text, rather than information conveyed by background information Human-centered – should be based on what a human can infer from the text, rather than what a machine can currently do or not do Reproducible – your annotation should be reproducible by other humans (i.e., inter-annotator agreement should be high) obviously, these other humans may have to have particular expertise and training

What Should An Annotation Contain
Additional Information about the text being annotated – e.g., EAGLES external and internal criteria Information about the annotator – who, when, what version of tool, etc. (usually in meta-tags associated with the text) The tagged text itself Example:

External and Internal Criteria (EAGLES)
External: participants, occasion, social setting, communicative function origin: Aspects of the origin of the text that are thought to affect its structure or content. state: the appearance of the text, its layout and relation to non-textual matter, at the point when it is selected for the corpus. aims: the reason for making the text and the intended effect it is expected to have. Internal: patterns of language use Topic (economics, sports, etc.) Style (formal/informal, etc.)

External Criteria – state (EAGLES)
Mode spoken participant awareness: surreptitious/warned/aware venue: studio/on location/telephone written Relation to the medium written: how it is laid out, the paper, print, etc. spoken: the acoustic conditions, etc. Relation to non-linguistic communicative matter diagrams, illustrations, other media that are coupled with the language in a communicative event. Appearance e.g., advertising leaflets, aspects of presentation that are unique in design and are important enough to have an effect on the language.

Examples of annotation schemes (changing the way we do business!)
POS tagging annotation – Penn Treebank Scheme Named entity annotation – ACE Scheme Phrase Structure annotation – Penn Treebank scheme Time Expression annotation – TIMEX2 Scheme Protein Name Annotation – GU Scheme Event Annotation – TimeML Scheme Rhetorical Structure Annotation - RST Scheme Coreference Annotation, Subjectivity Annotation, Gesture Annotation, Intonation Annotation, Metonymy Annotation, etc., etc. Etc. Several hundred schemes exist, for different problems in different languages

POS Tag Formats: Non-SGML – to SGML
CLAWS tagger: non-SGML What_DTQ can_VM0 CLAWS_NN2 do_VDI to_PRP Inderjeet_NP0 's_POS noonsense_NN1 text_NN1 ?_? Brill tagger: non-SGML What/WP can/MD CLAWS/NNP do/VB to/TO Inderjeet/NNP 's/POS noonsense/NN text/NN ?/. Alembic POS tagger: <s><lex pos=WP>What</lex> <lex pos=MD>can</lex> <lex pos=NNP>CLAWS</lex> <lex pos=VB>do</lex> <lex pos=TO>to</lex> <lex pos=NNP>Inderjeet</lex> <lex pos=POS>'</lex><lex pos=PRP>s</lex> <lex pos=VBP>noonsense</lex> <lex pos=NN>text</lex> <lex pos=".">?</lex></s> Conversion to SGML is pretty trivial in such cases

SGML (Standard Generalized Markup Language)
A general markup language for text HTML is an instance of an SGML encoding Text Encoding Initiative (TEI): defines SGML schemes for marking up humanities text resources as well as dictionaries Examples: <p><s>I’m really hungry right now.</s><s>Oh, yeah?</s> <utt speak=“Fred” date=“10- Feb-1998”>That is an ugly couch.</utt> Note: some elements (e.g., <p>) can consist just of a single tag Character references: ways of referring to the non-ASCII characters using a numeric code å (this is in decimal) å (this is in hexadecimal) å Entity references: are used to encode a special character or sequence of characters via a symbolic name résum&eacute.; &docdate;

DTDs A document type definition, or DTD, is used to define a grammar of legal SGML structures for a document e.g., para should consist of one or more sentences and nothing else SGML parser verifies that document is compliant with DTD DTD’s can therefore be used for XML as well DTDs can specify what attributes are required, in what order, what their legit values are, etc. The DTDs are often ignored in practice! DTD: <!ENTITY writer SYSTEM " -entities.dtd"> <!ATTLIST payment type (check|cash) "cash"> XML: <author>&writer;</author> <payment type="check">

XML “Extensible Markup Language (XML) is a simple, very flexible text format derived from SGML. Originally designed to meet the challenges of large-scale electronic publishing, XML is also playing an increasingly important role in the exchange of a wide variety of data on the Web and elsewhere.” Defines a simplified subset of SGML, designed especially for Web applications Unlike HTML, separates out display (e.g., XSL) from content (XML) Example <p/><s><lex pos=“WP”>What</lex> <lex pos=“MD”>can</lex></s> Makes use of DTDs, but also RDF Schemas

RDF Schemas Example of Real RDF Schema:
TimeML.xsd (see EVENT tag and attributes)

Inline versus Standoff Annotation
Usually, when tags are added, an annotation tool is used, to avoid spurious insertions or deletions The annotation tool may use inline or standoff annotation Inline – tags are stored internally in (a copy of) the source text. Tagged text can be substantially larger than original text Web pages are a good example – i.e., HTML tags Standoff – tags are stored internally in separate files, with information as to what positions in the source text the tags occupy e.g., PERSON However, the annotation tool displays the text as if the tags were in-line

Summary: Annotation Issues
A ‘best-practices’ methodology is widely used for annotating corpora The annotation process involves computational tools at all stages Standard guidelines are available for use To share annotated corpora (and to ensure their survivability), it is crucial that the data be represented in a standard rather than ad hoc format XML provides a well-established, Web-compliant standard for markup languages DTDs and RDF provide mechanisms for checking well- formedness of annotation

Background Deborah Schiffrin. Anaphoric then: aspectual, textual, and epistemic meaning. Linguistics 30 (1992), Schiffrin xamines uses of then in data elicited via 20 sociolinguistic interviews, each an hour long Distinguishes two anaphoric temporal senses, showing that they are differentiated by clause position Shows that they have systematic effects on aspectual interpretation A parallel argument is made for two epistemic temporal senses

Schiffrin: Temporal and Non-Temporal Senses
Anaphoric Senses ‘Narrative’ temporal sense (shifts reference time) And then I uh lived there until I was sixteen Continuing Temporal sense (continues a previous reference time) I was only a little boy then. Epistemic senses Conditional ‘sentences’ (rare, but often have temporal antecedents in her data) But if I think about it for a few days -- well, then I seem to remember a great deal …if I’m still in the situation where I am now….I’m, not gonna have no more then Initiation-response-evaluation sequences (‘in that case’?) Freda: Do y’ still need the light? Debby: Um. Freda” W’ll have t’ go in then. Because the bugs are out.

Schiffrin’s Argument (Simplified) and Its Test
Shifting RT thens (call these Narrative) & then in if-then conditionals similar semantic function mainly clause-initial Continuing RT thens (call these Temporal) & IRE thens mainly clause final stative verb more likely (since RT overlaps, verbs conveying duration are expected) Call the rest Other isn’t differentiated into if-then versus IRE So, only part of her claims tested

So, What do we do Then? Define environments of interest, each one defined by a pattern For each environment Find examples matching the pattern If classifying the examples is manageable, carry it out and stop Otherwise restrict the environment by adding new elements to the pattern, and go back to 1 So, for each final environment, we claim that X% of the examples in that environment are of a particular class Initial ‘then’ Pattern: (^|_CC|_RB)\s*then\w+\s+\w Final ‘then’ Pattern: [^\,]\s+then[\.\?\'\;\!\:]

Exceptions Non-Narrative Initial ‘then’ then there [be] then come
then again then and now only then even then so then Non-Temporal Final ‘then’ What then? All right/OK [,] then And then?

Results Written Fiction 2000 Spoken Broadcast News Written Gigaword News T N O Clause Initial 1.73 (23/13 22) (1276/ 1322) (21/13 22) .73 (6/81 8) 93.88( 768/81 8) 5.3 (44/81 8) (27/740 ) (562/74 0) (151/74 0) Clause Final (79/11 0) (3/110) (28/11 0) (61/8 4) (5/84) (18/84) (179/19 2) (13/192 ) Other is a presence in final position in fiction and broadcast news, and in initial position in print news. Is this real or artifact of catch-all class? Conclusion: only part of her claims tested. But those claims are borne out across three different genres and much more data!

Considerations in Inter-Annotator Agreement
Size of tagset Structure of tagset Clarity of Guidelines Number of raters Experience of raters Training of raters Independent ratings (preferred) Consensus (not preferred) Exact, partial, and equivalent matches Metrics Lessons Learned: Disagreement patterns suggest guideline revisions

Protein Names function sequence features gene name cellular location
Considerable variability in the forms of the names Multiple naming conventions Researchers may name a newly discovered protein based on function sequence features gene name cellular location molecular weight discoverer or other properties Prolific use of abbreviations and acronyms fushi tarazu 1 factor homolog Fushi tarazu factor (Drosophila) homolog 1 FTZ-F1 homolog ELP steroid/thyroid/retinoic nuclear hormone receptor homolog nhr-35 V-INT 2 murine mammary tumor virus integration site oncogene homolog fibroblast growth factor 1 (acidic) isoform 1 precursor nuclear hormone receptor subfamily 5, Group A, member 1

Guidelines v1 TOC

2*Precision*Recall/(Precision+Recall)
Agreement Metrics Reference Candidate Yes No TP  FN FP TN  Measure Definition Percentage Agreement 100*(TP+TN)/ (TP+FP+TN+FN) Precision TP/(TP+FP) Recall TP/(TP+FN) (Balanced) F-Measure 2*Precision*Recall/(Precision+Recall)

Example for F-measure: Scorer Output (Protein Name Tagging)
REFERENCE CANDIDATE CORR FTZ-F1 homolog ELP | FTZ-F1 homolog ELP INCO M2-LHX | M2 SPUR | - SPUR | LHX3 Precision = ¼ = 0.25 Recall = ½ = 0.5 F-measure = 2 * ¼ * ½ / ( ¼ + ½ ) = 0.33

The importance of disagreement
Measuring inter-annotator agreement is very useful in “debugging” the annotation scheme Disagreement can lead to improvements in the annotation scheme Extreme disagreement can lead to abandonment of the scheme

V2 Assessment (ABS2) Old Guidelines New Guidelines protein 0.71 F
Coder s Corre ct Precisio n Recall F- me a- sure <protein> A1-A3 4497 0.874 0.852 0.863 A1-A4 4769 0.884 0.904 0.894 A3- A4 4476 0.830 0.870 0.849 Avera ge 0.862 0.875 0.868 <long-form> 172 0.720 0.599 0.654 241 0.837 0.840 0.838 175 0.608 0.732 0.664 0.721 0.723 0.718 Old Guidelines protein 0.71 F acronym 0.85 F array-protein 0.15 F New Guidelines protein 0.86 F long-form 0.71 F these are only ~4% of tags

TIMEX2 Annotation Scheme
Time Points <TIMEX2 VAL="2000-W42">the third week of October</TIMEX2> Durations <TIMEX2 VAL=“PT30M”>half an hour long</TIMEX2> Indexicality <TIMEX2 VAL=“ ”>tomorrow</TIMEX2> Sets <TIMEX2 VAL=”XXXX-WXX-2" SET="YES” PERIODICITY="F1W" GRANULARITY=“G1D”>every Tuesday</TIMEX2> Fuzziness <TIMEX2 VAL=“1990-SU”>Summer of 1990 </TIMEX2> <TIMEX2 VAL=“ TMO”>This morning</TIMEX2> Non-specificity <TIMEX2 VAL="XXXX-04" NON_SPECIFIC=”YES”>April</TIMEX2> is usually wet. For guidelines, tools, and corpora, please see timex2.mitre.org

TIMEX2 Inter-Annotator Agreement
193 NYT news docs 5 annotators 10 pairs of annotators Human annotation quality is ‘acceptable’ on EXTENT and VAL Poor performance on Granularity and Non-Specific But only a small number of instances of these (200 ~ 6000) Annotators deviate from guidelines, and produce systematic errors (fatigue?) several years ago: PXY instead of PAST_REF all day: P1D instead of YYYY-MM-DD

TempEx in Qanda

Summary: Inter-Annotator Reliability
There’s no point going on with an annotation scheme if it can’t be reproduced There are standard methods for measuring inter-annotator reliability An analysis of inter-annotator disagreements is critical for “debugging” an annotation scheme

Information Extraction
Types Flag names of people, organizations, places,… Flag and normalize time expressions, phrases such as time expressions, measure phrases, currency expressions, etc. Group coreferring expressions together Find relations between named entities (works for, located at, etc.) Find events mentioned in the text Find relations between events and entities A hot commercial technology! Example patterns: Mr. ---, , Ill.

Message Understanding Conferences (MUCs)
Idea: precise tasks to measure success, rather than test suite of input and logical forms. MUC and MUC messages about navy operations MUC and MIC news articles and transcripts of radio broadcasts about terrorist activity MUC news articles about joint ventures and microelectronics MUC news articles about management changes, + additional tasks of named entity recognition, coreference, and template element MUC – mostly multilingual information extraction Has also been applied to hundreds of other domains - scientific articles, etc., etc.

Historical Perspective
Until MUC-3 (1993), many IE systems used a Knowledge Engineering approach They did something like full chart parsing with a unification-based grammar with full logical forms, a rich lexicon and KB E.g., SRI’s Tacitus Then, they discovered that things could work much faster using finite-state methods and partial parsing And that using domain-specific rather than general purpose lexicons simplified parsing (less ambiguity due to fewer irrelevant senses) And that these methods worked even better for the IE tasks E.g., SRI’s Fastus, SRA’s Nametag Meanwhile, people also started using statistical learning methods from annotated corpora Including CFG parsing

An instantiated scenario template
Source Wall Street Journal, 06/15/88 MAXICARE HEALTH PLANS INC and UNIVERSAL HEALTH SERVICES INC have dissolved a joint venture which provided health services.

Templates Can get Complex! (MUC-5)

2002 Automatic Content Extraction (ACE) Program: Entity Types
Person Organization (Place) Location – e.g., geographical areas, landmasses, bodies of water, geological formations Geo-Political Entity – e.g., nations, states, cities Created due to metonymies involving this class of places The riots in Miami Miami imposed a curfew Miami railed against a curfew Facility – buildings, streets, airports, etc.

ACE Entity Attributes and Relations
Name: An entity mentioned by name Pronoun Nominal Relations AT: based-in, located, residence NEAR: relative-location PART: part-of, subsidiary, other ROLE: affiliate-partner, citizen-of, client, founder, general- staff, manager, member, owner, other SOCIAL: associate, grandparent, parent, sibling, spouse, other-relative, other-personal, other-professional

Designing an Information Extraction Task
Define the overall task Collect a corpus Design an Annotation Scheme linguistic theories help Use Annotation Tools - authoring tools -automatic extraction tools Apply to annotation to corpus, assessing reliability Use training portion of corpus to train information extraction (IE) systems Use test portion to test IE systems, using a scoring program

Annotation Tools Specialized authoring tools used for marking up text without damaging it Some tools are tied to particular annotation schemes

Annotation Tool Example: Alembic Workbench

Callisto (Java successor to Alembic Workbench)

Relationship Annotation: Callisto

Steps in Information Extraction
Tokenization Language Identification Document Zoning Sentence and Word Tokenization Morphological and Lexical Processing Tagging entities of interest Specific trigger lexicons Dealing with unknown words Part-of-Speech Tagging Word-Sense Tagging Morphological Analysis Parsing Finite-State Parsing (usually just chunking) Domain Semantics Coreference Merging Partial Results

Morphological Analysis
Inflectional morphology, mostly For simple languages (English, Japanese) – simple inflectional module suffices For more complex languages (Spanish) – a finite-state transducer is used For morphologically very complex languages (Arabic, Hebrew) – complex finite state transducer architectures For languages with productive noun compounding (German) – specialized module needed

Finite-State Parsing for IE
A.C. Nielesen CO. NG said VG George Garrick NG, 40 years old, president NG of information Resources Inc. NG's London- based European Information Services operationNG, will becomeVG presidentNG and chief operating officerNG of Nielsen Marketing Research USANG, a unit NG of Dun & Bradstree Corp. NG First find NG, VG, particles; ignore PP attachment; ignore clause boundaries; maybe ignore modifiers that aren’t domain-relevant Later transducers handle more complex phenomena: relative clauses (e.g., look for second verb for marking end of rc; subject relatives: associate subject with first and second verb; object relatives: associate object with head noun before rel mod) general clause segmentation coordination appositives PP argument attachment (only for verbs important in domain whose subcat info is provided – rest are adverbial adjuncts)

Example Text Processing
Bridgestone Sports Co. said Friday it has set up a joint venture in Taiwan with a local concern and a Japanese trading house to produce golf clubs to be shipped to Japan. CompanyNG Set-UPVG Joint- VentureNG with CompanyNG ProduceVG ProductNG The joint venture, Bridgestone Sports Taiwan Cp., capitalized at 20 million new Taiwan dollars, will start production in January 1990 with production of 20,000 iron and “metal wood” clubs a month. KEY: Trigger word tagging Named Entity tagging Chunk parsing: NGs, VGs, preps, conjunctions

Merging Structures Bridgestone Sports Co. said Friday it has set up a joint venture in Taiwan with a local concern and a Japanese trading house to produce golf clubs to be shipped to Japan. Activity: Type: PRODUCTION Company: Product: golf clubs Start-date: The joint venture, Bridgestone Sports Taiwan Cp., capitalized at 20 million new Taiwan dollars, will start production in January 1990 with production of 20,000 iron and “metal wood” clubs a month. Activity: Type: PRODUCTION Company: Bridgestone Sports Taiwan Co Product: iron and “metal wood” clubs Start-date: DURING 1990

Coreference Coreference means establishing referential relations between expressions. Pronouns Mr. Gates …he, the testimony….it Definite NPs Microsoft….the company Indefinite NPs the building…an apartment Proper Names Bill Gates…William Gates…. Mr. Gates Temporal Expressions today, three weeks from Monday Headless Determiners all, the one, five Prenominals aluminum siding …the price of aluminum Events they attacked at dawn…the attack Types of relationships: Identity, Part-whole Set-subset the jurors…five …. Set-member the jurors…on

Statistical Named Entity Tagging
Typically, treat it as a word-level tagging problem To get phrase-level tags, one could greedily concatenate adjacent tags this will fail to separate ‘like’ tags Approaches can separately model words at start, end, or middle of name BBN Identifinder does that P(C|W) = P(W, C)/P(W)  argmaxCP(W, C) P(Ci|Ci-1, wi-1) first word in a name * P(<w, f>i=first|Ci, Ci-1) first word in a name * P(<w,f>i|<w,f>i-1, Ci) all but the first word in a name Word features f includes information about capitalization, initials, etc.

Information Extraction Metrics
Precision: Correct Answers/Answers Produced Recall: Correct Answers /Total Possible Correct F-measure - uses a parameter  to weight precision versus recall (=1 for balance) F = (B2+1) PR / B2(P+R) F =.6 for the relationship/event extraction task (ceiling) in MUC F = .95+ for named entity task in MUC = .8 or so for coreference task

IE and QA Evaluations Names in English Names from audio @ 0% 15% word error Names in Japanese Names in Chinese Relations Question Answering Event extraction Current status for various information extraction and question-answering components

Summary: Information Extraction
A variety of IE tasks and methods are available Named entities, relations, and event templates can be filled, as well as coreference relations Linguistic information used can be hand-crafted or corpus-based Domain knowledge, where needed, is hand-crafted Performance on names is better than on relations, while “deep” templates have shown a 60% ceiling effect

Motivation for Temporal Information Extraction
Story Understanding Question-answering Summarization Focus on temporal aspects of narrative

Chronology of ‘The Marathon’ (mini-story)
Yesterday Holly was running a marathon when she twisted her ankle. David had pushed her. run twist ankle during finishes or before push 1. When did the running occur? Yesterday. 2. When did the twisting occur? Yesterday, during the running. 3. Did the pushing occur before the twisting? Yes. 4. Did Holly keep running after twisting her ankle? 5. Maybe not????

Factors influencing Event Ordering
(1) Max entered the room. He had drunk a lot of wine. TENSE: Past perfect indicates drinking precedes entering. (2) Max entered the room. Mary was seated behind the desk. ASPECT: State of ‘being seated’ overlaps with ‘entering’. (3) He had borrowed some shirts from local villagers after his backpack went down. TEMPORAL MODIFIER: Going down precedes borrowing, based on temporal adverbial after (4) Iraq was defeated during the Gulf War. In ancient times it was the cradle of civilization. TIMEX: Being the cradle precedes being defeated, based on explicit time expression. (5) Max stood up. John greeted him. NARR_CONVENTION: Narrative convention applies, with ‘standing up’ preceding ‘greeting’ (6) Max fell. John pushed him. DISCOURSE_REL: Narrative convention overridden, based on Explanation relation (7) A drunken man died in the central Philippines when he put a firecracker under his armpit. DISCOURSE_REL: dying after putting, with temporal modifier used to instantiate Explanation relation (8) U.N. Secretary- General Boutros Boutros-Ghali Sunday opened a meeting of Boutros-Ghali arrived in Nairobi from South Africa, accompanied by Michel... WORLD KNOWLEDGE: arrival at the place of a meeting precedes opening a meeting

What’s Needed for Computing Chronologies?
Chronology Time Event -participants Representation of tense and aspect Representation of events and time Linking of events and time Result: a temporal constraint network Here, both events and times are represented as pairs of points (nodes) Ordering relations (edges) are <, = Yesterday, Holly was running …. y1 < y2 during < > < x1 x2 run run [Verhagen 2004]

TimeML Annotation TimeML is a proposed metadata standard for markup of events and their temporal anchoring and ordering Consists of EVENT tags, TIMEX3 tags, and LINK tags EVENTS are grouped into classes and have tense and aspect features LINKS include overt and covert links Can be within or across sentences

How TimeML Differs from Previous Markups
Extends TIMEX2 annotation to TIMEX3 Temporal Functions: three years ago Anchors to events and other temporal expressions: three years after the Gulf War Addresses problem with Granularity/Periodicity: three days every month Inserts start/end points for Durations: two weeks from June 7 Identifies signals determining interpretation of temporal expressions; Temporal Prepositions: for, during, on, at; Temporal Connectives: before, after, while. Identifies event expressions; tensed verbs; has left, was captured, will resign; stative adjectives; sunken, stalled, on board; event nominals; merger, Military Operation, Gulf War; Creates dependencies between events and times: Anchoring; John left on Monday. Orderings; The party happened after graduation. Embedding; John said Mary left.

TLINK TLINK or Temporal Link represents the temporal relationship holding between events or between an event and a time, and establishes a link between the involved entities, making explicit if they are: Simultaneous (happening at the same time) Identical: (referring to the same event) John drove to Boston. During his drive he ate a donut. One before the other: The police looked into the slayings of 14 women.In six of the cases suspects have already been arrested. One immediately before the other: All passengers died when the plane crashed into the mountain. One including the other: John arrived in Boston last Thursday. One holding during the duration of the other: One being the beginning of the other: John was in the gym between 6:00 p.m. and 7:00 p.m. One being the ending of the other:

SLINK SLINK or Subordination Link is used for contexts introducing relations between two events, or an event and a signal, of the following sort: Modal: Relation introduced mostly by modal verbs (should, could, would, etc.) and events that introduce a reference to a possible world --mainly I_STATEs: John should have bought some wine. Mary wanted John to buy some wine. Factive: Certain verbs introduce an entailment (or presupposition) of the argument's veracity. They include forget in the tensed complement, regret, manage: John forgot that he was in Boston last year. Mary regrets that she didn't marry John. Counterfactive: The event introduces a presupposition about the non-veracity of its argument: forget (to), unable to (in past tense), prevent, cancel, avoid, decline, etc. John forgot to buy some wine. John prevented the divorce. Evidential: Evidential relations are introduced by REPORTING or PERCEPTION: John said he bought some wine. Mary saw John carrying only beer. Negative evidential: Introduced by REPORTING (and PERCEPTION?) events conveying negative polarity: John denied he bought only beer. Negative: Introduced only by negative particles (not, nor, neither, etc.), which will be marked as SIGNALs, with respect to the events they are modifying: John didn't forget to buy some wine. John did not want to marry Mary.

Role of the machine in human annotation
In cases of dense annotation (events, pos tags, word-sense tags, etc.), it can be too tedious for a human to annotate everything In such cases, it’s helpful to have a computer program pre- annotate the data that the human then corrects The machine can also interact to flag invalid entries The machine can also provide visualization The machine can also augment the annotation with information that can be inferred

Annotating Chronology in The Marathon

Pre-Closure

Post-Closure

Automatic TIMEX2 tagging

TimeML Annotation Issues
Problems Weaknesses in guidelines Links between subordinate clause and main clause of same/diff sentence Difficulties in annotating states Granularity of temporal relations (72% agreement on temporal relations on common links) Density of links. Number of links is quadratic in the number of events, but less than half the eventualities are linked. So, inter-annotator agreement on links likely to be low. Solutions Adding more annotation conventions Lightening the annotation. Expanding annotation using temporal reasoning. Using heavily mixed- initiative approach Providing user with visualization tools during annotation. Note: such problems are characteristic of semantic and discourse-level annotations!

TimeBank Browser and TimeML tools

Strategy for Automatically Inferring Linguistic Information
Develop a corpus of TimeML annotated documents TimeML represents temporal adverbials, tense, grammatical aspect, temporal relations Takes into account subordination and (to an extent) vagueness Work on metric constraints for durations of states is ongoing (Hobbs) Develop initial computer taggers to tag Events, Times, and Links in the corpus Correct the corpus using a human Ensure that the annotations can be reproduced accurately Inter-annotator reliability Use the corpus to train improved computer taggers

At the Florist’s (mini-story)
a. John went into the florist shop. b. He had promised Mary some flowers. c. She said she wouldn’t forgive him if he forgot. d. So he picked out three red roses. From (Webber 1988)

Chronology of At the Florist’s

At the Florist’s: A Rhetorical Structure Theory account
Assumes abstract nodes which are Rhetorical Relations Rhetorical relation annotations are not easily reproduced question of inter-annotator reliability Narration Explanation Ed Ea Elaboration Eb Ec

Temporal Relations as Surrogates for Rhetorical Relations
When E1 is left-sibling of E2 and E1 < E2, then typically, Narration(E1, E2) When E1 is right-sibling of E2 and E1 < E2, then typically Explanation(E2, E1) When E2 is a child node of E1, then typically Elaboration(E1, E2) a. John went into the florist shop. b. He had promised Mary some flowers. c. She said she wouldn’t forgive him if he forgot. d. So he picked out three red roses. Expl Elab Narr constraints: {Eb < Ec, Ec < Ea, Ea < Ed}

Temporal Discourse Model Annotation Conventions
Each tree is rooted in an abstract node. In the absence of any temporal adverbials or discourse markers, a tense shift will license the creation of an abstract node, with the tense shifted event being the leftmost daughter of the abstract node. The abstract node will then be inserted as the child of the immediately preceding text node. In the absence of temporal adverbials and discourse markers, a stative event will always be placed as a child of the immediately preceding text event when the latter is non-stative, and as a sibling of the previous event when the latter is stative (as in a scene-setting fragment of discourse).

Representing States Approach: Minimality Problem: Incompleteness
A tensed stative predicate is represented as a node in the tree (progressives are treated as stative). John walked home. He was feeling great. We represent the state of feeling great as being minimally a part of the event of walking, without committing to whether it extends before or after the event A constraint is added to C indicating that this inclusion is minimal. Problem: Incompleteness Max entered the room. He was wearing a black shirt The system will not know whether the shirt was worn after he entered the room.

TDMs and DRT EaEbEcxyzt1t2t3 [
enter(Ea, x, theWhiteHart) & man (x) & PROG(wear(Eb, x, y) & black-jacket(y) &serve(Ec, Bill, x, z) & beer(z) & t1 < n & Ea  t1 & t2 < n & Eb o t2 & Eb  Ea & t3 < n & Ec  t3 & Ea < Ec]

What’s Needed for Computing TDMs?
A Corpus of TDMs, annotated with high inter-annotator reliability ‘Syntactic’ parsers for TDMs, trained on the corpus

Conclusion There are lots of computational tools for manual and automatic annotation of linguistic data and exploration of linguistic hypotheses The automatic tools aren’t perfect, but neither are humans! An annotation scheme must be tested using guidelines and inter- annotator reliability Annotations must be prepared and used within standard XML- based frameworks There are many costs and tradeoffs in corpus preparation The yields can considerably speed up the pace of linguistic research

Desiderata for Indian Language Work
The data needs to be encoded using standard character encoding schemes – UNICODE, or else ISCII Annotation needs to follow the best-practices methodology, including proof of replicability, and XML representation Experience has shown that linguists and computer scientists can work in synergy on this Once corpora are prepared according to these guidelines, automatic tools can be developed in India and abroad and used to improve linguistic processing of Indian languages Morphological analyzers, stemmers, etc. Part-of-speech taggers Syntactic Parsers Word-Sense Disambiguators Temporal Taggers Information Extraction Systems Text Summarizers Statistical MT Systems etc.

Free Resources (contact me)
TIMEX2 corpora and tools: timex2.mitre.org (English, Korean, Spanish) TimeML and annotation tools: AQUAINT corpus, and TimeML software: watch this space PRONTO and iprolink corpora, guidelines, tagsets (see my web site) Thank You!

The Changing Environment
If statistical rules induced from examples perform just as well as rules derived from intuition, then this suggests that probabilistic statistical linguistic rules might help explain or model human linguistic behavior. It also suggests that humans might learn from experience by means of induction using statistical regularities. For many years, corpus linguistic research rarely examined statistics above the level of words, due to the lack of availability of broad-coverage parsers and statistical models that could handle syntax and other levels of ‘hidden structure’ (Manning 2003). The present climate with plenty of tools and statistical models, should allow corpus linguistics to extend its descriptive and explanatory scope dramatically.

Ngrams Details Consider a sequence of words W1…Wn, “I saw a rabbit”.
What’s P(W1…Wn)? Note that we can’t find sequences of length n, and count them - there won’t be enough data. Chain Rule of probability: P(W1, .. ,Wn ) = P(W1)P(W2|W1) P(W3|W1,W2)..P(Wn|W1,W2, ..,Wn-1 ) But you still have the problem of lacking enough data Bigram model Approximates P(Wn|W1…Wn-1) by P(Wn|Wn-1) Assumes the probability of a word depends just on the previous word. This means, that you don’t have to look back more than one word. P(I saw a rabbit) = P(I|<s>)*P(saw|I)*P(a|saw)*P(rabbit|a) More generally: P(W1….Wn)  i=1, n P(Wi| Wi-1) A trigram model, would look 2 words back into the past P(I saw a rabbit) =P(saw|<s> I)*P(a| I saw)*P(rabbit|saw a)

POS Tagging Based on N-grams
Problem: Find C which maximizes P(W | C) * P(C) Here W=W1..Wn and C=C1..Cn (these were sequences, remember?) P(W1, .. ,Wn ) = P(W1)P(W2|W1) P(W3|W1,W2)..P(Wn|W1,W2, ..,Wn-1 ) Using the bigram model, we get: P(W1….Wn | C1….Cn)  i=1, n P(Wi| Ci) P(C1….Cn)  i=1, n P(Ci| Ci-1) So, we want to find the value of C1..Cn which maximizes: i=1, n P(Wi| Ci) * P(Ci| Ci-1) pos bigram probs, estimated from training data lexical generation probabilities, estimated from training data

Problems in Event Anchoring
States John walked home. He was feeling great. How long does “feeling great” last? => We need a “minimal” duration for states a. Mary entered the President’s Office.b. A copy of the budget was on the president’s desk. c. The president’s financial advisor stood beside it. d. The president sat regarding both admiringly. e. The advisor spoke. (Dowty 1986) Was the budget on the desk before she entered the office? => “perceived scene” presents an imperfective view of states, not indicating their true onsets Vagueness The attack lasted 2-3 weeks. Recently, Holly turned 16. Next summer, Holly may run Three days later, David pushed her => temporal reasoning has to deal with vagueness

Problems in Event Anchoring (contd)
Vagueness (contd) John hurried to Mary’s house after work. But Mary had already left for dinner. => we need to track ‘reference time’ and decide when reference times coincide Modality John should have brought some wine. Did he bring wine? No. John prevented the divorce. Did the divorce happen? No. => we need to know about subordination Implicit Information Yesterday, Holly fell. (implicit “on”) Holly fell. David pushed her. (implicit “because”) => we need discourse modeling

Computational Tools for Linguists

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