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Supervised and Unsupervised learning for Natural language processing

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  1. Supervised and Unsupervised learning for Natural language processing Manaal Faruqui Language Technologies Institute SCS, CMU

  2. Natural Language Processing + Linguistics Computer Science

  3. Natural Language Processing • But Why ? • Inability to handle large amount of data • Much much faster information access

  4. Natural Language Processing • How can this be done ? • Can you teach a computer ?

  5. Natural Language Processing = Mathematics Using Maths to learn language ??? Are you kidding me !

  6. Machine Learning Teaching computers make decisions like humans Computer vision Machine Translation Clustering

  7. Machine Learning Supervised Unsupervised Semi-supervised Learning by examples Learning by patterns Learning by patterns + examples

  8. Formal & Informal address • Most languages distinguish formal (V) and informal (T) address in direct speech (Brown and Gilman, 1960) • Formal address: Neutrality, distance • Informal address: Friends, subordinates • Variety of realization in different languages • French: Pronoun usage (Vous/Tu) • German: Pronoun usage (Sie/Du) • Hindi: Pronoun usage (Aap/Tum) • Japanese: Verbal inflections • English: ???

  9. Main goals of this work • Goal 1: Determine whether English distinguishes between V & T consistently • If yes, what are the indicators ? • Goal 2: Develop a computational model that labels English sentences as T or V • Ideally without spending effort on annotation

  10. Methodology • Use a parallel corpus to analyze aligned sentences with overt (De) T/V choice and covert (En) T/V choice • For Goal 1: Compare De & En sentences • For Goal 2 : Project De labels onto En sentences

  11. Digression: Creation of a parallel corpus • Current parallel corpora not suitable • Europarl: Overwhelmingly formal (99%) • Newswire: No dialogue • Creation of a new corpus: De-En literary texts • 106 19th century novels (Project Gutenberg) • Sentence-aligned: Gargantuan (Braune & Fraser 2010) • POS-tagged (Schmidt 1994) • German sentence can be labeled as T, V or None • Using orthographic rules • Corpus: http://cs.cmu.edu/~mfaruqui

  12. Goal 1: Compare De and En address • Give English monolingual text to human annotators • Ask for T/V judgment • Their annotation provides the following information • How well do annotators agree on English text? • Does English monolingual text provide enough information to identify T/V? (1a) • How well do annotators agree with copied labels? • Is there a direct correspondence ? (1b) • Only if this is the case is the copying of labels appropriate

  13. Experiment 1: Human Annotation • 200 randomly drawn English sentences • Two annotators (“A1”, “A2”) • Two conditions: • No context: just one sentence • In context: three sentences pre- and post-context each

  14. Results: Reliability • Context improves reliability • Many sentences can not be tagged with T/V in isolation “And she is a sort of relation of your lordship’s,” said Dawson. “And perhaps sometime you may see her.” • Reliability in context is reasonable: • English does provide strong clues on T/V Goal 1a ✓

  15. Results: Correspondence • Agreement with German projected labels again reasonable, but not perfect • Error analysis showed strong influence of social norms • Example: Lovers in 19th cent. novels use V (!) • [...] she covered her face with the other to conceal her tears. “Corinne!”, said Oswald, “Dear Corinne! My absence has then rendered you unhappy!” Goal 1b ✓

  16. Experiment 2: Prediction of T/V • Copy German T/V labels onto English: No annotation • Learn L2-regularized logit classifier on train set; optimize on dev set; evaluate on test set • Feature candidates : • Lexical features (bag-of-words, χ² feature selection) • Distributional semantic word classes • 200 word classes clustered with the algorithm by Clark (2003) • Politeness theory (Brown & Levinson 2003) • Polite speech has specific features, which are inherited by V

  17. Supervised Learning • Logistic regression classifier • Linear combination of features • Every feature assigned a weight acc. to its importance • higher weight = more importance • L2 regularization to avoid overfitting • Used “Weka” as the open-source toolkit

  18. Context • As shown by human annotation: Individual sentences often insufficient for classification • Simplest solution: Compute features over a window of context sentences • Problem: context typically includes non-speech sentences “I am going to see his ghost!” Lorry quietly chafed the hands that held his arm.

  19. Context • Our solution: A simple “direct speech” recognizer CRF-based sequence tagger (Mallet) trained on 1000 sentences • Ideal results for 8 sentences of direct speech context +5% accuracy over no context Speech context Sentence context • B-SP: “I am going to see his ghost!” • O: Lorry quietly chafed the hands that held his arm.

  20. Quantitative results • (Faruqui & Pado, 2011; 2012) • Onlylexical features yield significant improvement over frequency baseline Goal 2 ✓

  21. Qualitative analysis: Lexical features Top 10 lexical features

  22. Conclusions • Formal and informal language exists in English as well • Indicators more dispersed across context • Bootstrapping a T/V classifier for English possible • Results still fairly modest • Asymmetry: V more marked than T → better features • Difficult to operationalize features with high recall (sociolinguistic features, first names, …)

  23. References • M. Faruqui & S. Pado, “I thou thee, thou traitor”: Predicting formal vs. informal address in English literature. ACL 2011. • M. Faruqui & S. Pado, Towards a model of formal and informal address in English. EACL 2012. • Roger Brown and Albert Gilman. 1960. The pronouns of power and solidarity. In Thomas A. Sebeok, editor, Style in Language, pages 253–277. MIT Press, Cambridge, MA. • Penelope Brown and Stephen C. Levinson. 1987. Politeness: Some Universals in Language Usage. Number 4 in Studies in Interactional Sociolinguistics. Cambridge University Press. • Fabienne Braune &Alexander Fraser. Improved unsupervised sentence alignment for symmetrical and asymmetrical parallel corpora. COLING 2010 • Helmut Schmid. 1994. Probabilistic Part-of-Speech Tagging Using Decision Trees. In Proceedings of the International Conference on New Methods in Language Processing, pages 44–49, Manchester, UK. • Andrew Kachites McCallum. 2002. Mallet: A machine learning for language toolkit. http://mallet.cs.umass.edu.

  24. Unsupervised Learning Learning by finding patterns in data Clustering

  25. Word clustering • Why ? • Feature reduction • From words to word classes • Generalization of unseen words • Bangalore ~ Bengaluru • Identification of words with similar meaning • Word-sense disambiguation • Reduces the need for tagged data

  26. Word clustering How ? • Distributional similarity • How similar is the occurrence pattern of two words in a given corpus ? • “You shall know a word by the company it keeps” – J. R. Firth • Morphological similarity • How similar are two words orthographically ? • Madras ~ Chennai … NO • Bangalore ~ Bengaluru … YES

  27. Word clustering Language modeling approach 1. Ranjitha cooks Uttapam. 2. Ranjitha cooks Rava masala dosa. 3. Ranjitha cooks Facebook. How do you know which one is wrong ??

  28. Word clustering • Language modeling approach • Maximize the probability of occurrence of a sequence of words • S: Ranjitha cooks Facebook • P(S) = P(Ranjitha) * P(cooks|Ranjitha) * P(Facebook|cooks) • P(Facebook|cooks) will be very near zero OR zero !

  29. Word clustering S: w1 w2 w3 w4 C2 C1 C3 C4 W1 W2 W4 W3 P(S) = P(C1) * P(w1|C1) * P(C2|C1) * P(w2|C2) * … (Och, 1999) This is called a Hidden-Markov Model (HMM)

  30. Word clustering Adding morphology (Clark, 2003) C2 C1 C3 C4 W1 W2 W4 W3 P(S) = P(C1) * P(w1|C1) * Pm(w1|C1) * P(C2|C1) * P(w2|C2) * Pm(w2|C2) …

  31. Word clustering • Implementation • Initialization of clusters • Randomized • Heuristic-based • Optimization algorithm • Greedy as closed form solution not present • Transfer word to the cluster with highest improvement • Termination • Till no more words are exchanged • Till a specific no. of words are exchanged

  32. Word clustering • Application / Evaluation • Named Entity Recognition • Identification and labeling of names of people, places, organization etc. • Pre-processing task for many NLP applications • Tags from the CoNLL-03 shared-task on NER: • PERson, ORGanization, LOCation, MISCellaneous • (Sonia Gandhi)PERis an (Italian)MISC who lives in (India)LOC.

  33. Named Entity Recognition NER for German: Challenges Complex Morphology: Difficult lemmatization Sparse data: Only one NE-tagged dataset (CoNLL 2003) Common noun capitalization: no easy entity detection Poor performance, in particular poor Recall Complex morphology: Difficult lemmatization Sparse data: Only one NE-tagged dataset (CoNLL 2003), just 0.2M tokens Common noun capitalization: no easy entity detection Complex morphology: Difficult lemmatization Sparse data: Only one NE-tagged dataset (CoNLL 2003), just 0.2M tokens Common noun capitalization: no easy entity detection Complex morphology: Difficult lemmatization Sparse data: Only one NE-tagged dataset (CoNLL 2003), just 0.2M tokens Common noun capitalization: no easy entity detection Complex morphology: Difficult lemmatization Sparse data: Only one NE-tagged dataset (CoNLL 2003), just 0.2M tokens Common noun capitalization: no easy entity detection Complex morphology: Difficult lemmatization Sparse data: Only one NE-tagged dataset (CoNLL 2003), just 0.2M tokens Common noun capitalization: no easy entity detection Complex morphology: Difficult lemmatization Complex morphology: Difficult lemmatization Complex morphology: Difficult lemmatization Poor Performance, in particular poor Recall Poor Performance, in particular poor Recall Poor Performance, in particular poor Recall Poor Performance, in particular poor Recall Poor Performance, in particular poor Recall

  34. Named Entity Recognition NER for German: Challenges • Recall is a problem ! • More amount of training data can help, but expensive ! • Semantic generalization ?

  35. Named Entity Recognition • Word clustering • Provides a way to semantic generalization • But how can it help ? Deutschland (70) Ostdeutschland(0) Westdeutschland(5) LOC

  36. Named Entity Recognition • Experimental setup • Cluster German words with Clark’s clustering software on the basis of an untagged generalization corpus • HGC, deWac (Baroni et. al, 2009) • Stanford’s CRF-based NER system (Finkel and Manning 2009) • Training on an NER-tagged corpus (CoNLL 2003 German train set newswire) • Evaluate on CoNLL 2003 testb set (50M words, in-domain)

  37. Named Entity Recognition Results (Faruqui & Pado, 2010)

  38. Multilingual word clustering • Clustering words from two languages together • If parallel data in two languages available • Word alignments can give additional information • Additional constraints may give better clustering I You We They She • Ich • Sie • Uns • Er

  39. Multilingual word clustering Language 1 Language 2

  40. Multilingual word clustering Language 1 Language 2

  41. Multilingual word clustering • Minimize the randomness of the clustering • Minimize the entropy of the clustering • If clustering of L1 is represented by a random variable X • We want to minimize the entropy of one clustering given the other:

  42. Multilingual word clustering • We optimize both the monolingual and multilingual objective together: • Further edge filtering heuristics can be used • Words aligned with stop words generally noisy • Low frequency words are important • Finding out whether edge filtering is language dependent or not

  43. References • M. Faruqui & S. Pado, Training and Evaluating a German Named Entity Recognizer with Semantic Generalization, KONVENS 2010. • Marco Baroni, Silvia Bernardini, Adriano Ferraresi, and Eros Zanchetta. 2009. The wacky wide web: A collection of very large linguistically processed web-crawled corpora. JLRE, 43(3):209–226. • Alexander Clark. 2003. Combining distributional and morphological information for part of speech induction. Proc. EACL 59–66, Budapest, Hungary. • Jenny Rose Finkel and Christopher D. Manning. 2009. Nested named entity recognition. Proc. EMNLP, pages 141–150, Singapore. • RaduFlorian, Abe Ittycheriah, Hongyan Jing, and Tong Zhang. 2003. Named entity recognition through classifier combination. Proc. CoNLL, pages 168–171. Edmonton. • Erik F. Tjong Kim Sang and Fien De Meulder. 2003. Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition. Proc. CoNLL, pages 142–147, Edmonton, AL

  44. Thank you! Questions? Please write to: mfaruqui@cs.cmu.edu Or visit: http://cs.cmu.edu/~mfaruqui