1. CARTIC RAMAKRISHNAN MEENAKSHI NAGARAJAN AMIT SHETH

2. How little you really know A Great way to find out….

3. We have used material from several popular books, papers, course notes and presentations made by experts in this area. We have provided all references to the best of our knowledge. This list however, serves only as a pointer to work in this area and is by no means a comprehensive resource.

4.  KNO.E.SIS  knoesis.org  Director: Amit Sheth  knoesis.wright.edu/amit/  Graduate Students:  Meena Nagarajan  knoesis.wright.edu/students/meena/  meena@knoesis.org meena@knoesis.org  Cartic Ramakrishnan  knoesis.wright.edu/students/cartic/  cartic@knoesis.org cartic@knoesis.org

5. Understanding Natural Language Word Sequence Syntactic Parser Parse Tree Semantic Analyzer Literal Meaning Discourse Analyzer Meaning An Overview of Empirical Natural Language Processing, Eric Brill, Raymond J. Mooney

6.  Traditional (Rationalist) Natural Language Processing  Main insight: Using rule-based representations of knowledge and grammar (hand-coded) for language study KB Text NLP System Analysis

7.  Empirical Natural Language Processing  Main insight: Using distributional environment of a word as a tool for language study KB Text NLP System Analysis Corpus Learning System

8.  Two approaches not incompatible. Several systems use both.  Many empirical systems make use of manually created domain knowledge.  Many empirical systems use representations of rationalist methods replacing hand-coded rules with rules acquired from data.

9. Goals of this Tutorial  Several algorithms, methods in each task, rationalist and empiricist approaches  What does a NL processing task typically entail?  How do systems, applications and tasks perform these tasks?  Syntax : POS Tagging, Parser  Semantics : Meaning of words, using context/domain knowledge to enhance tasks

10. What is Text Analysis for Semantic Computing?  Finding more about what we already know  Ex. patterns that characterize known information  The search/browse OR ‘finding a needle in a haystack’ paradigm  Discovering what we did not know  Deriving new information from data ▪Ex. Relationships between known entities previously unknown  The ‘extracting ore from rock’ paradigm

11. Levels of Text Analysis  Information Extraction - those that operate directly on the text input ▪this includes entity, relationship and event detection  Inferring new links and paths between key entities ▪sophisticated representations for information content, beyond the "bag-of-words" representations used by IR systems  Scenario detection techniques ▪discover patterns of relationships between entities that signify some larger event, e.g. money laundering activities.

12. What do they all have in common?  They all make use of knowledge of language (exploiting syntax and structure, different extents)  Named entities begin with capital letters  Morphology and meanings of words  They all use some fundamental text analysis operations  Pre-processing, Parsing, chunking, part-of-speech, lemmatization, tokenization  To some extent, they all deal with some language understanding challenges  Ambiguity, co-reference resolution, entity variations etc.  Use of a core subset of theoretical models and algorithms  State machines, rule systems, probabilistic models, vector-space models, classifiers, EM etc.

13. Some Fundamental Similarities  Analysis for both these goals have many similarities  Finding entities  What are we interested in knowing more about? (the known)  Is what we found something of interest? (the unknown)  Text is structured (to some extent)  Syntax, Structure  Semantics, Pragmatics, Discourse  Text is noisy  Pre-processing is not an option in many cases  Variations not uncommon

14.  Wikipedia like text (GOOD)  “Thomas Edison invented the light bulb.”  Scientific literature (BAD)  “This MEK dependency was observed in BRAF mutant cells regardless of tissue lineage, and correlated with both downregulation of cyclin D1 protein expression and the induction of G1 arrest.”  Text from Social Media (UGLY)  "heylooo..ano u must hear it loadsss bu your propa faabbb!!"

15. Goals of this Tutorial  Illustrate analysis of and challenges posed by these three text types throughout the tutorial

16. WHAT CAN TM DO FOR HARRY PORTER? A bag of words

17. Discovering connections hidden in text UNDISCOVERED PUBLIC KNOWLEDGE

18. Motivation for Text Mining  Undiscovered Public Knowledge [Swanson] – as mentioned in [Hearst99]  Search no longer enough ▪ Information overload – prohibitively large number of hits ▪ UPK increases with increasing corpus size  Manual analysis very tedious  Examples [Hearst99] ▪ Example 1 – Using Text to Form Hypotheses about Disease ▪ Example 2 – Using Text to Uncover Social Impact

19. Example 1  Swanson’s discoveries ▪ Associations between Migraine and Magnesium [Hearst99] ▪ stress is associated with migraines ▪ stress can lead to loss of magnesium ▪ calcium channel blockers prevent some migraines ▪ magnesium is a natural calcium channel blocker ▪ spreading cortical depression (SCD) is implicated in some migraines ▪ high levels of magnesium inhibit SCD ▪ migraine patients have high platelet aggregability ▪ magnesium can suppress platelet aggregability

20. Example 2  Mining popularity from Social Media  Goal: Top X artists from MySpace artist comment pages  Traditional Top X lists got from radio plays, cd sales. An attempt at creating a list closer to listeners preferences  Mining positive, negative affect / sentiment  Slang, casual text necessitates transliteration ▪ ‘you are so bad’ == ‘you are good’

21. Text Mining – Two objectives  Mining text to improve existing information access mechanisms  Search [Storylines]  IR [QA systems]  Browsing [Flamenco]  Mining text for  Discovery & insight [Relationship Extraction]  Creation of new knowledge ▪ Ontology instance-base population ▪ Ontology schema learning

22. Mining text to improve search  Web search – aims at optimizing for top k (~10) hits  Beyond top 10  Pages expressing related latent views on topic  Possible reliable sources of additional information  Storylines in search results [3]

23. Examples of storylines

24. Mining text to improve Fact Extraction  TextRunner[4]  A system that uses the result of dependency parses of sentences to train a Naïve Bayes classifier for Web-scale extraction of relationships  Does not require parsing for extraction – only required for training  Training on features – POS tag sequences, if object is proper noun, number of tokens to right or left etc.  This system is able to respond to queries like "What did Thomas Edison invent?" "What did Thomas Edison invent?"

25. Mining text to improve Browsing  Castanet [1] Castanet  Semi-automatically builds faceted hierarchical metadata structures from text  This is combined with Flamenco [2] to support faceted browsing of contentFlamenco

26. Castanet – building faceted hierarchies from text Documents Select terms WordNet Build core tree Augment core tree Remove top level categories Compress Tree Divide into facets

27. Details of Castanet’s method frozen dessert sundae entity substance,matter nutriment dessert ice cream sundae frozen dessert entity substance,matter nutriment dessert sherbet,sorbet sherbet sundae sherbet substance,matter nutriment dessert sherbet,sorbet frozen dessert entity ice cream sundae Domains used to prune applicable senses in Wordnet (e.g. “dip”)Wordnet

28. Ontology Population vs. ontology creation? Biologically active substance Lipid Disease or Syndrome affects causes affects causes complicates Fish Oils Raynaud’s Disease ??????? instance_of UMLS Semantic Network MeSH PubMed 9284 documents 4733 documents 5 documents

29. Text Mining for knowledge creation – Ontology population [Hearst92] Finding class instances [Nguyen07] Finding attribute “like” relation instances [Ramakrishnan et. al. 08]

30. Text Mining for Knowledge creation – Ontology Learning  Automatic acquisition of  Class Labels  Class hierarchies  Attributes  Relationships  Constraints  Rules

31. Mining text for Discovery

32. SYNTAX, SEMANTICS, STATISTICAL NLP, TOOLS, RESOURCES, GETTING STARTED

33. Language Understanding is hard  [hearst 97] Abstract concepts are difficult to represent  “Countless” combinations of subtle, abstract relationships among concepts  Many ways to represent similar concepts  E.g. space ship, flying saucer, UFO  Concepts are difficult to visualize  High dimensionality  Tens or hundreds of thousands of features

34. Why is NL hard – some challenges  Ambiguity (sense)  Keep that smile playin’ (Smile is a track)  Keep that smile on!  Variations (spellings, synonyms, complex forms)  Illeal Neoplasm vs. Adenomatous lesion of the Illeal wall  Coreference resolution  “John wanted a copy of Netscape to run on his PC on the desk in his den; fortunately, his ISP included it in their startup package,”

35. Text Mining is also easy  [hearst 97] Highly redundant data  …most of the methods count on this property  Just about any simple algorithm can get “good” results for simple tasks:  Pull out “important” phrases  Find “meaningfully” related words  Create some sort of summary from documents

36.  Concerned with processing documents in natural language  Computational Linguistics, Information Retrieval, Machine learning, Statistics, Information Theory, Data Mining etc.  TM generally concerned with practical applications  As opposed to lexical acquisition (for ex.)in CL

37. Helping us get there  Computing Resources  Faster disks, CPUs, Networked Information  Data Resources  Large corpora, tree banks, lexical data for training and testing systems  Tools for analysis  NL analysis: taggers, parsers, noun-chunkers, tokenizers; Statistical Text Analysis: classifiers, nl model generators  Emphasis on applications and evaluation  Practical systems experimentally evaluated on real data

38. TYPES, WHAT THEY DO AND DON’T

39. Text Mining Systems  Co-occurrence based  Rule / knowledge based  Statistical / machine-learning based  Systems typically use a combination of two or more

40. Co-occurrence based TM Systems  Look for terms and posit relationships based on co-occurrence  “A word is known by the company it keeps”  Non-trivial as they deal with problems of language – expression variability, ambiguity  Sometimes used as a simple baseline when evaluating more sophisticated systems

41. Rule/Knowledge based TM Systems  Exploit real-world knowledge  About language  About terms in the domain  About relationship between terms in the domain  About variations of terms we know of etc.  Spectrum of work from using hard-coded patterns (text or linguistic) for TM to using linguistic + semantic analysis for TM  Harder to develop, maintain rules; comprehensiveness also an issue

42. Statistical / ML based TM Systems  Using classifiers operating on text  At pos level, n-grams, parse trees  Classifying documents, words, attributes/properties  Typically requires hand-tagged/labeled data  Supervised, semi-supervised approaches http://compbio.uchsc.edu/Hunter_lab/Cohen/Hunter_Cohen_Molecular_Cell.pdf

43.  Computational Linguistics - Syntax  Parts of speech, morphology, phrase structure, parsing, chunking  Semantics  Lexical semantics, Syntax-driven semantic analysis, domain model-assisted semantic analysis (WordNet),  Getting your hands dirty  Text encoding, Tokenization, sentence splitting, morphology variants, lemmatization  Using parsers, understanding outputs  Tools, resources, frameworks

44.  Statistical NLP  Mathematical foundations, some information theory  Words, Statistical Inference using n-grams, language models  How are these used – some examples  Collocations, Lexical Acquisition, Word sense disambiguation

45.  Several algorithms, variations for each  We wont go into each algorithm  That’s a whole semester course  We’ll define the problem, point out general approach, show examples  More importantly, we’ll show how results of these components are used by applications we know of today

46. POS Tags, Taggers, Ambiguities, Examples Word Sequence Syntactic Parser Parse Tree Semantic Analyzer Literal Meaning Discourse Analyzer Meaning

47.  Word classes, syntactic/grammatical categories, parts-of-speech  Comprehensive lists used by taggers  87 in the Brown Corpus tagset  45 in the Penn Treebank tagset  146 for the C7 tagset POS Tag Description Example CCcoordinating conjunctionand CDcardinal number1, third DTdeterminerthe EXexistential therethere is FWforeign wordd'hoevre INpreposition/subordinating conjunctionin, of, like JJadjectivegreen JJRadjective, comparativegreener JJSadjective, superlativegreenest LSlist marker1) MDmodalcould, will NNnoun, singular or masstable NNSnoun pluraltables NNPproper noun, singularJohn NNPSproper noun, pluralVikings PDTpredeterminerboth the boys POSpossessive endingfriend's PRPpersonal pronounI, he, it PRP$possessive pronounmy, his

48.  Assigning a pos or syntactic class marker to a word in a sentence/corpus.  Word classes, syntactic/grammatical categories  Usually preceded by tokenization  delimit sentence boundaries, tag punctuations and words.  Publicly available tree banks, documents tagged for syntactic structure  Typical input and output of a tagger ▪ Cancel that ticket. Cancel/VB that/DT ticket/NN./.

49.  Lexical ambiguity  Words have multiple usages and parts-of-speech ▪ A duck in the pond ; Don’ t duck when I bowl ▪ Is duck a noun or a verb? ▪ Yes, we can ; Can of soup; I canned this idea ▪ Is can an auxiliary, a noun or a verb?  Problem in tagging is resolving such ambiguities

50.  Information about a word and its neighbors  has implications on language models ▪ Possessive pronouns (mine, her, its) usually followed a noun  Understand new words ▪ Toves did gyre and gimble.  On IE ▪ Nouns as cues for named entities ▪ Adjectives as cues for subjective expressions

51.  Useful in understanding words  We can guess what a new words means by looking at words around it.  Toves did gyre and gimble. Toves is something than can perform an action. (from hearst tagging)

52.  Rule-based  Database of hand-written/learned rules to resolve ambiguity -EngCG  Probability / Stochastic taggers  Use a training corpus to compute probability of a word taking a tag in a specific context - HMM Tagger  Hybrids – transformation-based  The Brill tagger  A comprehensive list of available taggers  http://www- nlp.stanford.edu/links/statnlp.html#Taggers

53.  Not a complete representation  EngCG based on the Constraint Grammar Approach  Two step architecture  Use a lexicon of words and likely pos tags to first tag words  Use a large list of hand-coded disambiguation rules that assign a single pos tag for each word

54.  Sample lexicon WordPOS AdditionalPOS features  SlowerADJCOMPARITIVE  ShowVPRESENT  ShowNNOMINATIVE  Sample rules

55.  What is the best possible tag given this sequence of words?  Takes context into account; global  Example: HMM (hidden Markov models)  A special case of Bayesian Inference  likely tag sequence is the one that maximizes the product of two terms: ▪ probability of sequence of tags and probability of each tag generating a word

56.  Peter/NNP is/VBZ expected/VBN to/TO race/VB tomorrow/NN  to/TOrace/??? t i = argmax j P(t j |t i-1 )P(w i |t j )  P(VB|TO) × P(race|VB)  Based on the Brown Corpus:  Probability that you will see this POS transition and that the word will take this POS  P(VB|TO) =.34 × P(race|VB) =.00003=.00001

57.  Be aware of possibility of ambiguities  Possible one has to normalize content before sending it to the tagger  Pre Post Transliteration ▪ “Rhi you were da coolest last eve” ▪ Rhi/VB you/PRP were/VBD da/VBG coolest/JJ last/JJ eve/NN ▪ “Rhi you were the coolest last eve” ▪ Rhi/VB you/PRP were/VBD the/DT coolest/JJ last/JJ eve/NN

58. Understanding Phrase Structures, Parsing, Chunking Word Sequence Syntactic Parser Parse Tree Semantic Analyzer Literal Meaning Discourse Analyzer Meaning

59.  Words don’t just occur in some order  Words are organized in phrases  groupings of words that clunk together  Major phrase types  Noun Phrases  Prepositional phrases  Verb phrases

60.  Deriving the syntactic structure of a sentence based on a language model (grammar)  Natural Language Syntax described by a context free grammar  the Start-Symbol S ≡ sentence  Non-Terminals NT ≡ syntactic constituents  Terminals T ≡ lexical entries/ words  Productions P  NT  (NT  T) + ≡ grammar rules http://www.cs.umanitoba.ca/~comp4190/2006/NLP-Parsing.ppt

61.  S  NT, Part-of-Speech  NT, Constituents  NT, Words  T, Rules:  S  NP VPstatement  S  Aux NP VPquestion  S  VPcommand  NP  Det Nominal  NP  Proper-Noun  Nominal  Noun | Noun Nominal | Nominal PP  VP  Verb | Verb NP | Verb PP | Verb NP PP  PP  Prep NP  Det  that | this | a  Noun  book | flight | meal | money

62. Bottom-up Parsing or data-driven Top-down Parsing or goal-driven S Aux NP VP Det Nominal Verb NP Noun Det Nominal doesthis flight include a meal

63. Natural Language Parsers, Peter Hellwig, Heidelberg Constituency Parse - Nested Phrasal Structures Dependency parse - Role Specific Structures

64.  Tagging  John/NNP bought/VBD a/DT book/NN./.  Constituency Parse  Nested phrasal structure ▪ (ROOT (S (NP (NNP John)) (VP (VBD bought) (NP (DT a) (NN book))) (..)))  Typed dependencies  Role specific structure ▪ nsubj(bought-2, John-1) ▪ det(book-4, a-3) ▪ dobj(bought-2, book-4)

65.  Grammar checking: sentences that cannot be parsed may have grammatical errors  Using results of Dependency parse  Word sense disambiguation (dependencies as features or co-occurrence vectors)

66.  MINIPAR  http://www.cs.ualberta.ca/~lindek/minipar.htm  Link Grammar parser: http://www.link.cs.cmu.edu/link/ http://www.link.cs.cmu.edu/link/  Standard “CFG” parsers like the Stanford parser  http://www-nlp.stanford.edu/software/lex- parser.shtml http://www-nlp.stanford.edu/software/lex- parser.shtml  ENJU’s probabilistic HPSG grammar  http://www-tsujii.is.s.u-tokyo.ac.jp/enju/

67.  Some applications don’t need the complex output of a full parse  Chunking / Shallow Parse / Partial Parse  Identifying and classifying flat, non-overlapping contiguous units in text ▪ Segmenting and tagging  Example of chunking a sentence ▪ [ NP The morning flight] from [ NP Denver] [ VP has arrived]  Chunking algos mention

68. From Hearst 97

69.  Entity recognition ▪ people, locations, organizations  Studying linguistic patterns (Hearst 92) ▪ gave NP ▪ gave up NP in NP ▪ gave NP NP ▪ gave NP to NP

70.  Stanford and Enju parser demos; analyzing results  http://www-tsujii.is.s.u- tokyo.ac.jp/enju/demo.html http://www-tsujii.is.s.u- tokyo.ac.jp/enju/demo.html  http://nlp.stanford.edu:8080/parser/ http://nlp.stanford.edu:8080/parser/  If you want to know how to run it stand alone  Talk to one of us or see their very helpful help pages

71. COLORLESS GREEN IDEAS SLEEP FURIOUSLY Word Sequence Syntactic Parser Parse Tree Semantic Analyzer Literal Meaning Discourse Analyzer Meaning

72.  When raw linguistic inputs nor any structures derived from them will facilitate required semantic processing  When we need to link linguistic information to the non-linguistic real-world knowledge  Typical sources of knowledge  Meaning of words, grammatical constructs, discourse, topic..

73.  Typical sources of knowledge  Meanings of words  Meanings of grammatical constructs  Knowledge about structure of discourse  Common sense knowledge about topic  Knowledge about state of affairs in which discourse is occurring

74.  Lexical Semantics  The meanings of individual words  Formal Semantics (Compositional Semantics or Sentential Semantics)  How those meanings combine to make meanings for individual sentences or utterances  Discourse or Pragmatics  How those meanings combine with each other and with other facts about various kinds of context to make meanings for a text or discourse http://www.stanford.edu/class/cs224u/224u.07.lec2.ppt

75.  Lexeme: set of forms taken by a single word  run, runs, ran and running forms of the same lexeme RUN  Lemma: a particular form of a lexeme that is chosen to represent a canonical form ▪ Carpet for carpets; Sing for sing, sang, sung  Lemmatization: Meaning of a word approximated by meaning of its lemma  Mapping a morphological variant to its root ▪ Derivational and Inflectional Morphology

76.  Word sense: Meaning of a word (lemma)  Varies with context  Significance  Lexical ambiguity ▪ consequences on tasks like parsing and tagging ▪ implications on results of Machine translation, Text classification etc.  Word Sense Disambiguation  Selecting the correct sense for a word

77.  The study of the way words are built up from smaller meaning units.  Derivational and Inflectional morphology  Forming new words from old words (derivational)  Suffixes (inflections)  Knowing root helps us understand new words

78.  Porter Stemming Algorithm  http://tartarus.org/~martin/PorterStemmer/ http://tartarus.org/~martin/PorterStemmer/  Catvar  http://clipdemos.umiacs.umd.edu/catvar/ http://clipdemos.umiacs.umd.edu/catvar/  Lingsoft  http://www2.lingsoft.fi/cgi- bin/engtwol?word=cmputers http://www2.lingsoft.fi/cgi- bin/engtwol?word=cmputers  Wordnet  http://www.shiffman.net/teaching/a2z/wordnet/

79.  Homonymy  Polysemy  Synonymy  Antonymy  Hypernomy  Hyponomy  Meronomy  Why do we care? http://www.stanford.edu/class/cs224u/224u.07.lec2.ppt

80.  Homonymy: share a form, relatively unrelated senses  Bank (financial institution, a sloping mound)  Polysemy: semantically related  Bank as a financial institution, as a blood bank  Verbs tend more to polysemy

81.  Different words/lemmas that have the same sense  Couch/chair  One sense more specific than the other (hyponymy)  Car is a hyponym of vehicle  One sense more general than the other (hypernymy)  Vehicle is a hypernym of car

82.  Meronymy  Engine part of car; engine meronym of car  Holonymy  Car is a holonym of engine

83.  Semantic fields  Cohesive chunks of knowledge  Air travel: ▪ Flight, travel, reservation, ticket, departure…

84.  Models these sense relations  A hierarchically organized lexical database  On-line thesaurus + aspects of a dictionary ▪ Versions for other languages are under development http://www.stanford.edu/class/cs224u/224u.07.lec2.ppt

86.  Verbs and Nouns in separate hierarchies http://www.stanford.edu/class/cs224u/224u.07.lec2.ppt

87.  The set of near-synonyms for a WordNet sense is called a synset (synonym set)  Their version of a sense or a concept  Duck as a verb to mean ▪ to move (the head or body) quickly downwards or away ▪ dip, douse, hedge, fudge, evade, put off, circumvent, parry, elude, skirt, dodge, duck, sidestep

88.  IR and QnA  Indexing using similar (synonymous) words/query or specific to general words (hyponymy / hypernymy) improves text retrieval  Machine translation, QnA  Need to know if two words are similar to know if we can substitute one for another

89.  Most well developed  Synonymy or similarity  Synonymy - a binary relationship between words, rather their senses  Approaches  Thesaurus based : measuring word/sense similarity in a thesaurus  Distributional methods: finding other words with similar distributions in a corpus

90.  Thesaurus based  Path based similarity – two words are similar if they are similar in the thesaurus hierarchy http://www.stanford.edu/class/cs224u/224u.07.lec2.ppt

91.  We don’t have a thesaurus for every language. Even if we do, many words are missing  Wordnet: Strong for nouns, but lacking for adjectives and even verbs  Expensive to build  They rely on hyponym info for similarity  car hyponym of vehicle  Alternative - Distributional methods for word similarity http://www.stanford.edu/class/cs224u/224u.07.lec2.ppt

92.  Firth (1957): “You shall know a word by the company it keeps!”  Similar words appear in similar contexts - Nida example noted by Lin: ▪ A bottle of tezgüino is on the table ▪ Everybody likes tezgüino ▪ Tezgüino makes you drunk ▪ We make tezgüino out of corn. Partial material from http://www.stanford.edu/class/cs224u/224u.07.lec2.ppt

93. http://www.stanford.edu/class/cs224u/224u.07.lec2.ppt

94. So you want to build your own text miner!

95.  Infrastructure intensive  Luckily, plenty of open source tools, frameworks, resources..  http://www-nlp.stanford.edu/links/statnlp.html http://www-nlp.stanford.edu/links/statnlp.html  http://www.cedar.buffalo.edu/~rohini/CSE718/Ref erences2.html http://www.cedar.buffalo.edu/~rohini/CSE718/Ref erences2.html

96.  Mining opinions from casual text  Data – user comments on artist pages from MySpace  “Your musics the shit,…lovve your video you are so bad”  “Your music is wicked!!!!”  Goal  Popularity lists generated from listener’s comments to complement radio plays/cd sales lists

97. “Your musics the shit,…lovve your video you are so bad”  Pre-processing ▪ strip html, normalizing text from different sources..  Tokenization ▪ Splitting text into tokens : word tokens, number tokens, domain specific requirements  Sentence splitting ▪ !. ? … ; harder in casual text  Normalizing words ▪ Stop word removal, lemmatization, stemming, transliterations (da == the)

98. ‘The smile is so wicked!!’  Syntax : Marking sentiment expression from syntax or a dictionary ▪ The/DT smile/NN is/VBZ so/RB wicked/JJ !/. !/.  Semantics : Surrounding context ▪ On Lily Allen’s MySpace page. Cues for Co-ref resolution ▪ Smile is a track by Lilly Allen. Ambiguity  Background knowledge / resources ▪ Using urbandictionary.com for semantic orientation of ‘wicked’

99.  GATE - General Architecture of Text Engineering, since 1995 at University of Sheffield, UK  UIMA - Unstructured Information Management Architecture, IBM  Document processing tools, Components syntactic tools, nlp tools, integrating framework

100.  The GATE (General Architecture for Text Engineering) System:  http://gate.ac.uk  http://sourceforge.net/projects/gate  User’s Guide: http://gate.ac.uk/sale/tao/  IBM’s UIMA (Unstructured Information Management  Architecture):  http://www.research.ibm.com/UIMA/  http://sourceforge.net/projects/uima-framework/  Other Resources  WordNet: http://wordnet.princeton.edu/  MuNPEx: http://www.ipd.uka.de/~durm/tm/munpex/

101. TO COME: USAGE EXAMPLES OF WHAT WE COVERED THUS FAR

102. SAMPLE APPLICATIONS, SURVEY OF EFFORTS IN TWO SAMPLE AREAS 102

103. This MEK dependency was observed in BRAF mutant cells regardless of tissue lineage, and correlated with both downregulation of cyclin D1 protein expression and the induction of G1 arrest. *MEK dependency ISA Dependency_on_an_Organic_chemical *BRAF mutant cells ISA Cell_type *downregulation of cyclin D1 protein expression ISA Biological_process *tissue lineage ISA Biological_concept *induction of G1 arrest ISA Biological_process Information Extraction = segmentation+classification+association+mining Text mining = entity identification+named relationship extraction+discovering association chains…. Segmentation Classification Named Relationship Extraction MEK dependency observed in BRAF mutant cells downregulation of cyclin D1 protein expression correlated with induction of G1 arrest correlated with

104. MEK dependency observed in BRAF mutant cells downregulation of cyclin D1 protein expression correlated with induction of G1 arrest correlated with

105.  The task of classifying token sequences in text into one or more predefined classes  Approaches  Look up a list ▪ Sliding window  Use rules  Machine learning  Compound entities  Applied to  Wikipedia like text  Biomedical text

106.  The simplest approach  Proper nouns make up majority of named entities  Look up a gazetteer ▪ CIA fact book for organizations, country names etc.  Poor recall ▪ coverage problems

107.  Rule based [Mikheev et. Al 1999] Frequency Based "China International Trust and Investment Corp” "Suspended Ceiling Contractors Ltd” "Hughes“ when "Hughes Communications Ltd.“ is already marked as an organization Scalability issues: Expensive to create manually Leverages domain specific information – domain specific Tend to be corpus-specific – due to manual process

108.  Machine learning approaches  Ability to generalize better than rules  Can capture complex patterns  Requires training data ▪ Often the bottleneck  Techniques [list taken from Agichtein2007]Agichtein2007  Naive Bayes  SRV [Freitag 1998], Inductive Logic Programming  Rapier [Califf and Mooney 1997]  Hidden Markov Models [Leek 1997]  Maximum Entropy Markov Models [McCallum et al. 2000]  Conditional Random Fields [Lafferty et al. 2001]

109.  Orthographical Features  CD28 a protein  Context Features  Window of words ▪ Fixed ▪ Variable  Part-of-speech features  Current word  Adjacent words – within fixed window  Word shape features  Kappa-B replaced with Aaaaa-A  Dictionary features  Inexact matches  Prefixes and Suffixes  “~ase” = protein

110.  HMMs ▪ a powerful tool for representing sequential data ▪ are probabilistic finite state models with parameters for state- transition probabilities and state-specific observation probabilities ▪ the observation probabilities are typically represented as a multinomial distribution over a discrete, finite vocabulary of words ▪ Training is used to learn parameters that maximize the probability of the observation sequences in the training data  Generative ▪ Find parameters to maximize P(X,Y) ▪ When labeling X i future observations are taken into account (forward-backward)  Problems ▪ Feature overlap in NER ▪ E.g. to extract previously unseen company names from a newswire article  the identity of a word alone is not very predictive  knowing that the word is capitalized, that is a noun, that it is used in an appositive, and that it appears near the top of the article would all be quite predictive ▪ Would like the observations to be parameterized with these overlapping features ▪ Feature independence assumption Several features about same word can affect parameters

111.  MEMMs [McCallum et. al, 2000]  Discriminative ▪ Find parameters to maximize P(Y|X)  No longer assume that features are independent ▪ f (“Apple”, Company) = 1.  Do not take future observations into account (no forward-backward)  Problems ▪ Label bias problem

112.  CRFs [Lafferty et. al, 2001]  Discriminative  Doesn’t assume that features are independent  When labeling Y i future observations are taken into account  Global optimization – label bias prevented  The best of both worlds!

113.  Example  [ORG U.S. ] general [PER David Petraeus ] heads for [LOC Baghdad ]. TokenPOSChunk Tag --------------------------------------------------------- U.S. NNP I-NP I-ORG generalNN I-NP O David NNP I-NP B-PER Petraeus NNP I-NP I-PER heads VBZ I-VP O for IN I-PP O Baghdad NNP I-NP I-LOC.. O O  CONLL format – MalletMallet  Major bottleneck is training data

114.  Context Induction approach [Talukdar2006] ▪ Starting with a few seed entities, it is possible to induce high-precision context patterns by exploiting entity context redundancy. ▪ New entity instances of the same category can be extracted from unlabeled data with the induced patterns to create high-precision extensions of the seed lists. ▪ Features derived from token membership in the extended lists improve the accuracy of learned named- entity taggers. Pruned Extraction patterns Feature generation For CRF

115.  Machine Learning  Best performance  Problem  Training data bottleneck  Pattern induction  Reduce training data creation time

116.  Knowledge Engineering approach  Manually crafted rules ▪ Over lexical items works for ▪ Over syntactic structures – parse trees  GATE GATE  Machine learning approaches  Supervised  Semi-supervised  Unsupervised

117.  Supervised ▪ BioText – extraction of relationships between diseases and their treatments [Rosario et. al 2004] BioText ▪ Rule-based supervised approach [Rinaldi et. al 2004] ▪ Semantics of specific relationship encoded as rules ▪ Identify a set of relations along with their morphological variants (bind, regulate, signal etc.)  subj(bind,X,_,_),pobj(bind,Y,to,_) prep(Y,to,_,_) => bind(X,Y). ▪ Axiom formulation was however a manual process involving a domain expert.

118.  Hand-coded domain specific rules that encode patterns used to extract ▪ Molecular pathways [Freidman et. al. 2001] ▪ Protein interaction [Saric et. al. 2006]  All of the above in the biomedical domain  Notice – specificity of relationship types  Amount of effort required  Also notice types of entities involved in the relationships

119. IMPLICIT EXPLICIT

120.  Semantic Role Labeling  Features  Detailed tutorial on SRL is available  By Yih & Toutanova herehere

121.  Other approaches  Discovering concept-specific relations ▪ Dmitry Davidov, et. al 2007,  preemptive IE approach ▪ Rosenfeld & Feldman 2007  Open Information Extraction ▪ Banko et. al 2007 ▪ Self supervised approach ▪ Uses dependency parses to train extractors  On-demand information extraction ▪ Sekine 2006 ▪ IR driven ▪ Patterns discovery ▪ Paraphrase

122.  Rule and Heuristic based method  YAGO Suchanek et. al, 2007 YAGO  Pattern-based approach  Uses WordNet  Subtree mining over dependency parse trees  Nguyen et. al, 2007

123. Compound Entities Entities (MeSH terms) in sentences occur in modified forms “adenomatous” modifies “hyperplasia” “An excessive endogenous or exogenous stimulation” modifies “estrogen” Entities can also occur as composites of 2 or more other entities “adenomatous hyperplasia” and “endometrium” occur as “adenomatous hyperplasia of the endometrium”

124. Relationship head Subject head Object head  Small set of rules over dependency types dealing with  modifiers ( amod, nn ) etc. subjects, objects ( nsubj, nsubjpass ) etc.  Since dependency types are arranged in a hierarchy  We use this hierarchy to generalize the more specific rules  There are only 4 rules in our current implementation Carroll, J., G. Minnen and E. Briscoe (1999) `Corpus annotation for parser evaluation'. In Proceedings of the EACL-99 Post-Conference Workshop on Linguistically Interpreted Corpora, Bergen, Norway. 35-41. Also in Proceedings of the ATALA Workshop on Corpus Annotés pour la Syntaxe - Treebanks, Paris, France. 13-20.

125. Modifiers Modified entities Composite Entities

126.  Manual Evaluation  Test if the RDF conveys same “meaning” as the sentence  Juxtapose the triple with the sentence  Allow user to assess correctness/incorrectness of the subject, object and triple

129. Discovering complex connection patterns  Discovering informative subgraphs (Harry Potter)  Given a pair of end-points (entities)  Produce a subgraph with relationships connecting them such that ▪ The subgraph is small enough to be visualized ▪ And contains relevant “interesting” connections  We defined an interestingness measure based on the ontology schema  In future biomedical domain the scientist will control this with the help of a browsable ontology  Our interestingness measure takes into account ▪ Specificity of the relationships and entity classes involved ▪ Rarity of relationships etc. Cartic RamakrishnanCartic Ramakrishnan, William H. Milnor, Matthew Perry, Amit P. Sheth: Discovering informative connection subgraphs in multi-relational graphs. SIGKDD Explorations 7(2): 56-63 (2005)SIGKDD Explorations 7

130. Heuristics  Two factor influencing interestingness

131. Discovery Algorithm Bidirectional lock-step growth from S and T Choice of next node based on interestingness measure Stop when there are enough connections between the frontiers This is treated as the candidate graph

132. Discovery Algorithm  Model the Candidate graph as an electrical circuit  S is the source and T the sink  Edge weight derived from the ontology schema are treated as conductance values  Using Ohm’s and Kirchoff’s laws we find maximum current flow paths through the candidate graph from S to T  At each step adding this path to the output graph to be displayed we repeat this process till a certain number of predefined nodes is reached  Results  Arnold schwarzenegger, Edward Kennedy  Other related work  Semantic Associations

135.  Text Mining, Analysis  understanding  utilization in decision making  knowledge discovery  Entity Identification  focus change from simple to compound  Relationship extraction  implicit vs. explicit  Need more unsupervised approaches  Need to think of incentives to evaluate

136.  Existing corpora  GENIA, BioInfer many others GENIABioInfer  Narrow focus  Precision and Recall  Utility  How useful is the extracted information? How do we measure utility? ▪ Swanson’s discovery, Enrichment of Browsing experience  Text types and mining  Systematically compensating for (in)formality

137. Useful links  http://www.cs.famaf.unc.edu.ar/~laura/text_ mining/ http://www.cs.famaf.unc.edu.ar/~laura/text_ mining/  http://www.stanford.edu/class/cs276/cs276- 2005-syllabus.html http://www.stanford.edu/class/cs276/cs276- 2005-syllabus.html  http://www- nlp.stanford.edu/links/statnlp.html http://www- nlp.stanford.edu/links/statnlp.html  http://www.cedar.buffalo.edu/~rohini/CSE718 /References2.html http://www.cedar.buffalo.edu/~rohini/CSE718 /References2.html

138. References 1. Automating Creation of Hierarchical Faceted Metadata Structures Emilia Stoica, Marti Hearst, and Megan Richardson, in the proceedings of NAACL-HLT, Rochester NY, April 2007NAACL-HLT 2. Finding the Flow in Web Site Search, Marti Hearst, Jennifer English, Rashmi Sinha, Kirsten Swearingen, and Ping Yee, Communications of the ACM, 45 (9), September 2002, pp.42-49. 3. R. Kumar, U. Mahadevan, and D. Sivakumar, "A graph-theoretic approach to extract storylines from search results", in Proc. KDD, 2004, pp.216-225. R. KumarU. MahadevanD. Sivakumar 4. Michele Banko, Michael J. Cafarella, Stephen Soderland, Matthew Broadhead, Oren Etzioni: Open Information Extraction from the Web. IJCAI 2007: 2670-2676 Michele BankoMichael J. CafarellaStephen SoderlandMatthew BroadheadIJCAI 2007 5. Hearst, M. A. 1992. Automatic acquisition of hyponyms from large text corpora. In Proceedings of the 14th Conference on Computational Linguistics - Volume 2 (Nantes, France, August 23 - 28, 1992). 6. Dat P. T. Nguyen, Yutaka Matsuo, Mitsuru Ishizuka: Relation Extraction from Wikipedia Using Subtree Mining. AAAI 2007: 1414-1420 Dat P. T. NguyenMitsuru IshizukaAAAI 2007 7. "Unsupervised Discovery of Compound Entities for Relationship Extraction" Cartic Ramakrishnan, Pablo N. Mendes, Shaojun Wang and Amit P. Sheth EKAW 2008 - 16th International Conference on Knowledge Engineering and Knowledge Management Knowledge PatternsCartic Ramakrishnan Pablo N. MendesAmit P. Sheth 8. Mikheev, A., Moens, M., and Grover, C. 1999. Named Entity recognition without gazetteers. In Proceedings of the Ninth Conference on European Chapter of the Association For Computational Linguistics (Bergen, Norway, June 08 - 12, 1999). 9. McCallum, A., Freitag, D., and Pereira, F. C. 2000. Maximum Entropy Markov Models for Information Extraction and Segmentation. In Proceedings of the Seventeenth international Conference on Machine Learning 10. Lafferty, J. D., McCallum, A., and Pereira, F. C. 2001. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In Proceedings of the Eighteenth international Conference on Machine Learning

139. 11. Barbara, R. and A.H. Marti, Classifying semantic relations in bioscience texts, in Proceedings of the 42 nd ACL. 2004, Association for Computational Linguistics: Barcelona, Spain. 12. M.A. Hearst. 1992. Automatic acquisition of hyponyms from large text corpora. In Proceedings of COLING‘ 92, pages 539–545 13. M. Hearst, "Untangling text data mining," 1999. [Online]. Available: http://citeseer.ist.psu.edu/563035.html 14. Friedman, C., et al., GENIES: a natural-language processing system for the extraction of molecular pathways from journal articles. Bioinformatics, 2001. 17 Suppl 1: p. 1367-4803. 15. Saric, J., et al., Extraction of regulatory gene/protein networks from Medline. Bioinformatics, 2005. 16. Ciaramita, M., et al., Unsupervised Learning of Semantic Relations between Concepts of a Molecular Biology Ontology, in 19th IJCAI. 2005. 17. Dmitry Davidov, Ari Rappoport, Moshe Koppel. Fully Unsupervised Discovery of Concept-Specific Relationships by Web Mining. Proceedings, ACL 2007, June 2007, Prague.Fully Unsupervised Discovery of Concept-Specific Relationships by Web MiningACL 2007 18. Rosenfeld, B. and Feldman, R. 2007. Clustering for unsupervised relation identification. In Proceedings of the Sixteenth ACM Conference on Conference on information and Knowledge Management (Lisbon, Portugal, November 06 - 10, 2007). 19. Michele Banko, Michael J. Cafarella, Stephen Soderland, Matthew Broadhead, Oren Etzioni: Open Information Extraction from the Web. IJCAI 2007: 2670-2676 Michele BankoMichael J. CafarellaMatthew BroadheadOren EtzioniIJCAI 2007 20. Sekine, S. 2006. On-demand information extraction. In Proceedings of the COLING/ACL on Main Conference Poster Sessions (Sydney, Australia, July 17 - 18, 2006). Annual Meeting of the ACL. Association for Computational Linguistics, Morristown, NJ, 731-738. 21. Suchanek, F. M., Kasneci, G., and Weikum, G. 2007. Yago: a core of semantic knowledge. In Proceedings of the 16th international Conference on World Wide Web (Banff, Alberta, Canada, May 08 - 12, 2007). WWW '07.