1 Combining Lexical and Syntactic Features for Supervised Word Sense Disambiguation Masters Thesis : Saif Mohammad Advisor : Dr. Ted Pedersen University of Minnesota, Duluth Date: August 1, 2003

2 Path Map Introduction Background Data Experiments Conclusions

3 Word Sense Disambiguation Harry cast a bewitching spell Humans immediately understand spell to mean a charm or incantation reading out letter by letter or a period of time ? Words with multiple senses – polysemy, ambiguity Utilize background knowledge and context Machines lack background knowledge A utomatically i dentifying the intended sense of a word in written text, based on its context, remain s a hard problem Features are identified from the context Best accuracies in latest international event, around 65%

4 Why do we need WSD ! Information Retrieval Query: cricket bat Documents pertaining to the insect and the mammal, irrelevant Machine Translation Consider English to Hindi translation head to sar (upper part of the body) or adhyaksh (leader) Machine Hu man interaction Instructions to machines Interactive home system: turn on the lights Domestic Android: get the door Applications are widespread and will affect our way of life

5 Terminology Harry cast a bewitching spell Target word – the word whose intended sense is to be identified spell Context – the sentence housing the target word and possibly, 1 or 2 sentences around it Harry cast a bewitching spell Instance – target word along with its context WSD is a classification problem wherein the occurrence of the target word is assigned to one of its many possible senses

6 Corpus-Based Supervised Machine Learning A computer program is said to learn from experience … if its performance at tasks … improves with experience - Mitchell Task : Word Sense Disambiguation of given test instances Performance : Ratio of instances correctly disambiguated to the total test instances - accuracy Experience : Manually created instances such that target words are marked with intended sense – training instances Harry cast a bewitching spell / incantation

7 Path Map Introduction Background Data Experiments Conclusions

8 Decision Trees A kind of classifier Assigns a class by asking a series of questions Questions correspond to features of the instance Question asked depends on answer to previous question Inverted tree structure Interconnected nodes Top most node is called the root Each node corresponds to a question / feature Each possible value of feature has corresponding branch Leaves terminate every path from root Each leaf is associated with a class

9 Automating Toy Selection for Max Moving Parts ? Color ? Size ? Car ? Size ? Car ? LOVE SO LOVEHATE SO HATE No Yes Blue Big Red Small Other SmallBig ROOT NODES LEAVES

10 WSD Tree Feature 4? Feature 2 ? Feature 3 ? Feature 2 ? SENSE 4 SENSE 3SENSE 2 SENSE 1 SENSE Feature 1 ? SENSE 1

11 Issues… Why use decision trees for WSD ? How are decision trees learnt ? ID3 and C4.5algorithms What is bagging and its advantages Drawbacks of decision trees bagging Pedersen[2002]: Choosing the right features is of greater significance than the learning algorithm itself

12 Lexical Features Surface form A word we observe in text Case(n) 1. Object of investigation 2. frame or covering 3. A weird person Surface forms : case, cases, casing An occurrence of casing suggests sense 2 Unigrams and Bigrams One word and two word sequences in text The interest rate is low Unigrams: the, interest, rate, is, low Bigrams: the interest, interest rate, rate is, is low

13 Part of Speech Tagging Pre-requisite for many Natural Language Tasks Parsing, WSD, Anaphora resolution Brill Tagger – most widely used tool Accuracy around 95% Source code available Easily understood rules Harry /NNP cast /VBD a /DT bewitching /JJ spell / NN NNP proper noun, VBD verb past, DT determiner, NN noun

14 Pre-Tagging Pre-tagging is the act of manually assigning tags to selected words in a text prior to tagging Mona will sit in the pretty chair //NN this time chair is the pre-tagged word, NN is its pre-tag Reliable anchors or seeds around which tagging is done Brill Tagger facilitates pre-tagging Pre-tag not always respected ! Mona /NNP will /MD sit /VB in /IN the /DT pretty /RB chair //VB this /DT time /NN

15 Contextual Rules Initial state tagger – assigns most frequent tag for a type based on entries in a Lexicon (pre-tag respected) Final state tagger – may modify tag of word based on context (pre-tag not given special treatment) Relevant Lexicon Entries Type Most frequent tagOther possible tags chairNN (noun) VB (verb) prettyRB ( adverb ) JJ (adjective ) Relevant Contextual Rules Current TagNew TagWhen NNVBNEXTTAG DT RBJJNEXTTAG NN

16 Guaranteed Pre-Tagging A patch to the tagger provided – BrillPatch Application of contextual rules to the pre-tagged words bypassed Application of contextual rules to non pre-tagged words unchanged. Mona /NNP will /MD sit /VB in /IN the /DT pretty /JJ chair //NN this /DT time /NN Tag of chair retained as NN Contextual rule to change tag of chair from NN to VB not applied Tag of pretty transformed Contextual rule to change tag of pretty from RB to JJ applied

17 Part of Speech Features A word in different parts of speech has different senses A word used in different senses is likely to have different sets of pos around it Why did jack turn /VB against /IN his /PRP$ team /NN Why did jack turn /VB left /VBN at /IN the /DT crossing Features used Individual word POS: P -2, P -1, P 0, P 1, P 2 * P 2 = JJ implies P 2 is an adjective Sequential POS: P -1 P 0, P -1 P 0 P 1, and so on P -1 P 0 = NN, VB implies P -1 is a noun and P 0 is a verb A combination of the above

18 Parse Features Collins Parser used to parse the data Source code available Uses part of speech tagged data as input Head word of a phrase the hard work, the hard surface Phrase itself : noun phrase, verb phrase and so on Parent : Head word of the parent phrase fasten the line, cross the line Parent Phrase

19 Sample Parse Tree VERB PHRASENOUN PHRASE Harry NOUN PHRASE SENTENCE spell cast a bewitching NNP VBD DT JJ NN

20 Path Map Introduction Background Data Experiments Conclusions

21 Sense-Tagged Data Senseval2 data 4328 instances of test data and 8611 instances of training data ranging over 73 different noun, verb and adjectives. Senseval1 data 8512 test instances and 13,276 training instances, ranging over 35 nouns, verbs and adjectives. Line, hard, interest, serve data 4,149, 4,337, 4378 and 2476 sense-tagged instances with line, hard, serve and interest as the head words. Around 50,000 sense-tagged instances in all !

22 Data Processing Packages to convert line hard, serve and interest data to Senseval-1 and Senseval-2 data formats refine preprocesses data in Senseval-2 data format to make it suitable for tagging Restore one sentence per line and one line per sentence, pre-tag the target words, split long sentences posSenseval part of speech tags any data in Senseval-2 data format Brill tagger along with Guaranteed Pre-tagging utilized parseSenseval parses data in a format as output by the Brill Tagger restores xml tags, creating a parsed file in Senseval-2 data format Uses the Collins Parser

23 Sample line data instance Original instance: art} aphb : " There's none there. " He hurried outside to see if there were any dry ones on the line. Senseval-2 data format: " There's none there. " He hurried outside to see if there were any dry ones on the line.

24 Sample Output from parseSenseval Harry cast a bewitching spell Harry cast a bewitching spell

25 Issues… How is the target word identified in line, hard and serve data How the data is tokenized for better quality pos tagging and parsing How is the data pre-tagged How is parse output of Collins Parser interpreted How is the parsed output XML’ized and brought back to Senseval-2 data format Idiosyncrasies of line, hard, serve, interest, Senseval- 1 and Senseval-2 data and how they are handled

26 Path Map Introduction Background Data Experiments Conclusions

27 Surface Forms Senseval-1 & Senseval-2 Senseval-2Senseval-1 Majority47.7%56.3% Surface Form 49.3%62.9% Unigrams55.3%66.9% Bigrams55.1%66.9%

28 Individual Word POS (Senseval-1) AllNounsVerbsAdj. Majority56.3%57.2%56.9%64.3% P %58.2%58.6%64.0 P %62.2%58.2%64.3% P0P0 60.3%62.5%58.2%64.3% P1P1 63.9%65.4%64.4%66.2% P %60.0%60.8%65.2%

29 Individual Word POS (Senseval-2) AllNounsVerbsAdj. Majority47.7%51.0%39.7%59.0% P %51.9%38.0%57.9% P %55.2%40.2%59.0% P0P0 49.9%55.7%40.6%58.2% P1P1 53.1%53.8%49.1%61.0% P %50.2%43.2%59.4%

30 Combining POS Features Senseval-2Senseval-1line Majority47.7%56.3%54.3% P 0, P %66.7%54.1% P -1, P 0, P %68.0%60.4% P -2, P -1, P 0, P 1, P %67.8%62.3%

31 Effect Guaranteed Pre-tagging on WSD Guar. P.Reg. P.Guar. P.Reg. P P -1, P %62.1%50.8%50.9% P 0, P % 54.3%53.8% P -1, P 0, P %67.6%54.6%54.7% P -1 P 0, P 0 P %66.3%54.0%53.7% P -2, P -1, P 0, P 1, P %66.1%54.6%54.1% Senseval-1 Senseval-2

32 Parse Features (Senseval-1) AllNounsVerbsAdj. Majority56.3%57.2%56.9%64.3% Head64.3%70.9%59.8%66.9% Parent60.6%62.6%60.3%65.8% Phrase58.5%57.5%57.2%66.2% Par. Phr.57.9%58.1%58.3%66.2%

33 Parse Features (Senseval-2) AllNounsVerbsAdj. Majority 47.7%51.0%39.7%59.0% Head51.7%58.5%39.8%64.0% Parent50.0%56.1%40.1%59.3% Phrase48.3%51.7%40.3%59.5% Par. Phr.48.5%53.0%39.1%60.3%

34 Thoughts… Both lexical and syntactic features perform comparably But do they get the same instances right ? How much are the individual feature sets redundant Are there instances correctly disambiguated by one feature set and not by the other ? How much are the individual feature sets complementary Is the effort to combine of lexical and syntactic features justified ?

35 Measures Baseline Ensemble: accuracy of a hypothetical ensemble which predicts the sense correctly only if both individual feature sets do so Quantifies redundancy amongst feature sets Optimal Ensemble : a ccuracy of a hypothetical ensemble which predicts the sense correctly if either of the individual feature sets do so Difference with individual accuracies quantifies complementarity We used a simple ensemble which sums up the probabilities for each sense by the individual feature sets to decide the intended sense

36 Best Combinations DataSet 1Set 2BaseMaj.Ens.Opt. Sval2 Unigrams 55.3% P -1,P 0, P % 43.6%47.7%57.0%67.9% Sval1 Unigrams 66.9% P -1,P 0, P % 57.6%56.3%71.1%78.0% line Unigrams 74.5% P -1,P 0, P % 55.1%54.3%74.2%82.0% hard Bigrams 89.5% Head, Par 87.7% 86.1%81.5%88.9%91.3% serve Unigrams 73.3% P -1,P 0, P % 58.4%42.2%81.6%89.9% Interest Bigrams 79.9% P -1,P 0, P % 67.6%54.9%83.2%90.1%

37 Path Map Introduction Background Data Experiments Conclusions

38 Conclusions Significant amount of complementarity across lexical and syntactic features Combination of the two justified Part of speech of word immediately to the right of target word found most useful Pos of words immediately to the right of target word best for verbs and adjectives Nouns helped by tags on either side Head word of phrase particularly useful for adjectives Nouns helped by both head and parent

39 Other Contributions Converted line, hard, serve and interest data into Senseval-2 data format Part of speech tagged and Parsed the Senseval2, Senseval-1, line, hard, serve and interest data Developed the Guaranteed Pre-tagging mechanism to improve quality of pos tagging Showed that guaranteed pre-tagging improves WSD

40 Code, Data, Resources and Publication posSenseval : part of speech tags any data in Senseval-2 data format parseSenseval : parses data in a format as output by the Brill Tagger. Output is in Senseval-2 data format with part of speech and parse information as xml tags. Packages to convert line hard, serve and interest data to Senseval-1 and Senseval-2 data formats BrillPatch : Patch to Brill Tagger to employ Guaranteed Pre-Tagging Brill Tagger: Collins Parser: “Guaranteed Pre-Tagging for the Brill Tagger”, Mohammad and Pedersen, Fourth International Conference of Intelligent Systems and Text Processing, February 2003, Mexico

41 Thank You