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  1. Natural Language Processing Berlin Chen Department of Computer Science & Information Engineering National Taiwan Normal University References: 1. Foundations of Statistical Natural Language Processing 2. Speech and Language Processing

  2. Motivation for NLP (1/2) • Academic: Explore the nature of linguistic communication • Obtain a better understanding of how languages work • Practical: Enable effective human-machine communication • Conversational agents are becoming an important form of human-computer communication • Revolutionize the way computers are used • More flexible and intelligent

  3. Motivation for NLP (2/2) • Different Academic Disciplines: Problems and Methods • Electrical Engineering, Statistics • Computer Science • Linguistics • Psychology • Many of the techniques presented were first develpoed for speech and then spread over into NLP • E.g. Language models in speech recognition Linguistics Psychology NLP Electrical Engineering, Statistics Computer Science

  4. Turing Test • Alan Turing,1950 • Alan predicted at the end of 20 century a machine with 10 gigabytes of memory would have 30% chance of fooling a human interrogator after 5 minutes of questions • Does it come true? interrogator

  5. Hollywood Cinema • Computers/robots can listen, speak, and answer our questions • E.g.: HAL 9000 computer in “2001: A Space Odyssey” (2001太空漫遊)

  6. State of the Art (1/3) • Canadian computer program accepted daily weather data and generated weather reports (1976) • Read student essays and grade them • Automated reading tutor • Spoken Dialogues • AT&T, How May I Help You?

  7. Major Topics for NLP (2/2) • Semantics/Meaning • Representation of Meaning • Semantic Analysis • Word Sense Disambiguation • Pragmatics • Natural Language Generation • Discourse, Dialogue and Conversational Agents • Machine Translation

  8. Dissidences • Rationalists (e.g. Chomsky) • Humans are innate language faculties • (Almost fully) encoded rules plus reasoning mechanisms • Dominating between 1960’s~mid 1980’s • Empiricists (e.g. Shannon) • The mind does not begin with detailed sets of principles and procedures for language components and cognitive domains • Rather, only general operations for association, pattern recognition, generalization etc., are endowed with • General language models plus machine learning approaches • Dominating between 1920’s~mid 1960’s and resurging 1990’s~

  9. Dissidences: Statistical and Non-Statistical NLP • The dividing line between the two has become much more fuzzy recently • An increasing number of non-statistical researches use corpus evidence and incorporate quantitative methods • Corpus: “a body of texts” (大量的文稿) • Statistical NLP needs to start with all the scientific knowledge available about a phenomenon when building a probabilistic model, rather than closing one’s eye and taking a clean-slate approach • Probabilistic and data-driven • Statistical NLP → “Language Technology” or “Language Engineering”

  10. (I) Part-of-Speech Tagging

  11. Review • Tagging (part-of-speech tagging) • The process of assigning (labeling) a part-of-speech or other lexical class marker to each word in a sentence (or a corpus) • Decide whether each word is a noun, verb, adjective, or whatever The/ATrepresentative/NNput/VBDchairs/NNSon/INthe/ATtable/NN Or The/ATrepresentative/JJput/NNchairs/VBZon/INthe/ATtable/NN • An intermediate layer of representation of syntactic structure • When compared with syntactic parsing • Above 96% accuracy for most successful approaches Tagging can be viewed as a kind of syntactic disambiguation

  12. Introduction • Parts-of-speech • Known as POS, word classes, lexical tags, morphology classes • Tag sets • Penn Treebank : 45 word classes used (Francis, 1979) • Penn Treebank is a parsed corpus • Brown corpus: 87 word classes used (Marcus et al., 1993) • …. The/DTgrand/JJjury/NNcommented/VBDon/INa/DTnumber/NNof/INother/JJtopics/NNS./.

  13. The Penn Treebank POS Tag Set

  14. Disambiguation • Resolve the ambiguities and choose the proper tag for the context • Most English words are unambiguous (have only one tag) but many of the most common words are ambiguous • E.g.: “can” can be a (an auxiliary) verb or a noun • E.g.: statistics of Brown corpus - 11.5% word types are ambiguous - But 40% tokens are ambiguous (However, the probabilities of tags associated a word are not equal → many ambiguous tokens are easy to disambiguate)

  15. Process of POS Tagging A String of Words A Specified Tagset Tagging Algorithm A Single Best Tag of Each Word VB DT NN . Book that flight . VBZ DT NN VB NN ? Does that flight serve dinner ? Two information sources used: - Syntagmatic information (looking at information about tag sequences) - Lexical information (predicting a tag based on the word concerned)

  16. POS Tagging Algorithms (1/2) Fall into One of Two Classes • Rule-based Tagger • Involve a large database of handcrafted disambiguation rules • E.g. a rule specifies that an ambiguous word is a noun rather than a verb if it follows a determiner • ENGTWOL: a simple rule-based tagger based on the constraint grammar architecture • Stochastic/Probabilistic Tagger • Also called model-based tagger • Use a training corpus to compute the probability of a given word having a given context • E.g.: the HMM tagger chooses the best tag for a given word (maximize the product of word likelihood and tag sequence probability) “a new play” P(NN|JJ) ≈ 0.45 P(VBP|JJ) ≈ 0.0005

  17. POS Tagging Algorithms (1/2) • Transformation-based/Brill Tagger • A hybrid approach • Like rule-based approach, determine the tag of an ambiguous word based on rules • Like stochastic approach, the rules are automatically induced from previous tagged training corpus with the machine learning technique • Supervised learning

  18. Pavlov had shown that salivation … Pavlov PAVLOV N NOM SG PROPER had HAVE V PAST VFIN SVO HAVE PCP2 SVO shown SHOW PCP2 SVOO SVO SV that ADV PRON DEM SG DET CENTRAL DEM SG CS salivation N NOM SG Rule-based POS Tagging (1/3) • Two-stage architecture • First stage: Use a dictionary to assign each word a list of potential parts-of-speech • Second stage: Use large lists of hand-written disambiguation rules to winnow down this list to a single part-of-speech for each word An example for The ENGTOWL tagger (preterit) (past participle) A set of 1,100 constraints can be applied to the input sentence (complementizer)

  19. Rule-based POS Tagging (2/3) • Simple lexical entries in the ENGTWOL lexicon past participle

  20. Rule-based POS Tagging (3/3) Example: It isn’t that odd! I consider that odd. ADV A NUM Compliment

  21. HMM-based Tagging (1/8) • Also called Maximum Likelihood Tagging • Pick the most-likely tag for a word • For a given sentence or words sequence , an HMM tagger chooses the tag sequence that maximizes the following probability tag sequence probability word/lexical likelihood N-gram HMM tagger

  22. HMM-based Tagging (2/8) • Assumptions made here • Words are independent of each other • A word’s identity only depends on its tag • “Limited Horizon” and “Time Invariant” (“Stationary”) • Limited Horizon: a word’s tag only depends on the previous few tags (limited horizon) and the dependency does not change over time (time invariance) • Time Invariant: the tag dependency won’t change as tag sequence appears different positions of a sentence Do not model long-distance relationships well ! - e.g., Wh-extraction,…

  23. HMM-based Tagging (3/8) • Apply a bigram-HMM tagger to choose the best tag for a given word • Choose the tag ti for word wi that is most probable given the previous tag ti-1 and current word wi • Through some simplifying Markov assumptions word/lexical likelihood tag sequence probability

  24. HMM-based Tagging (4/8) • Example: Choose the best tag for a given word Secretariat/NNP is /VBZ expected/VBN to/TO race/VB tomorrow/NN 0.34 0.00003 to/TO race/??? P(VB|TO) P(race|VB)=0.00001 P(NN|TO) P(race|NN)=0.000007 0.021 0.00041 Pretend that the previous word has already tagged

  25. HMM-based Tagging (5/8) • The Viterbi algorithm for the bigram-HMM tagger

  26. HMM-based Tagging (6/8) • Apply trigram-HMM tagger to choose the best sequence of tags for a given sentence • When trigram model is used • Maximum likelihood estimation based on the relative frequencies observed in the pre-tagged training corpus (labeled data) Smoothing or linear interpolation are needed !

  27. HMM-based Tagging (7/8) • Probability smoothing of and is necessary

  28. HMM-based Tagging (8/8) • Probability re-estimation based on unlabeled data • EM (Expectation-Maximization) algorithm is applied • Start with a dictionary that lists which tags can be assigned to which words • word likelihood function cab be estimated • tag transition probabilities set to be equal • EM algorithm learns (re-estimates) the word likelihood function for each tag and the tag transition probabilities • However, a tagger trained on hand-tagged data worked better than one trained via EM • Treat the model as a Markov Model in training but treat them as a Hidden Markov Model in tagging Secretariat/NNP is /VBZ expected/VBN to/TO race/VB tomorrow/NN

  29. Transformation-based Tagging (1/8) • Also called Brill tagging • An instance of Transformation-Based Learning (TBL) • Motive • Like the rule-based approach, TBL is based on rules that specify what tags should be assigned to what word • Like thestochastic approach, rules are automatically induced from the data by the machine learning technique • Note that TBL is a supervised learning technique • It assumes a pre-tagged training corpus

  30. Transformation-based Tagging (2/8) • How the TBL rules are learned • Three major stages 1. Label every word with its most-likely tag using a set of tagging rules (use the broadest rules at first) 2. Examine every possible transformation (rewrite rule), and select the one that results in the most improved tagging (supervised! should compare to the pre-tagged corpus ) 3. Re-tag the data according this rule • The above three stages are repeated until some stopping criterion is reached • Such as insufficient improvement over the previous pass • An ordered list of transformations (rules) can be finally obtained

  31. Transformation-based Tagging (3/8) • Example • P(NN|race)=0.98 • P(VB|race)=0.02 So, race will be initially coded as NN (label every word with its most-likely tag) 1 2 Refer to the correct tag Information of each word, and find the tag of race in (a) is wrong • (a). is/VBZ expected/VBN to/To race/NN tomorrow/NN • (b). the/DT race/NN for/IN outer/JJ space/NN 3 Learn/pick a most suitable transformation rule: (by examining every possible transformation) Change NN to VB while the previous tag is TO Rewrite rule: expected/VBN to/To race/NN → expected/VBN to/To race/VB

  32. Transformation-based Tagging (4/8) • Templates (abstracted transformations) • The set of possible transformations may be infinite • Should limit the set of transformations • The design of a small set of templates (abstracted transformations) is needed E.g., a strange rule like: transform NN to VB if the previous word was “IBM” and the word “the” occurs between 17 and 158 words before that

  33. Transformation-based Tagging (5/8) • Possible templates (abstracted transformations) Brill’s templates. Each begins with “Change tag a to tagb when….”

  34. Transformation-based Tagging (6/8) • Learned transformations Verb, 3sg, past tense Modal verbs (should, can,…) Rules learned by Brill’s original tagger Verb, 3sg, Present Verb, past participle Constraints for tags more valuable player Constraints for words

  35. Transformation-based Tagging (7/8) • Reference for tags used in the previous slide

  36. Transformation-based Tagging (8/8) • Algorithm for all combinations of tags Y traverse corpus X Z append to the rule list Check if it is better than the best instance achieved in previous iterations score Get best instance for each transformation The GET_BEST_INSTANCE procedure in the example algorithm is “Change tag from X to Y if the previous tag is Z”.

  37. (II) Extractive Spoken Document Summarization - Models and Features

  38. Introduction (1/3) • World Wide Web has led to a renaissance of the research of automatic document summarization, and has extended it to cover a wider range of new tasks • Speech is one of the most important sources of information about multimedia content • However, spoken documents associated with multimedia are unstructured without titles and paragraphs and thus are difficult to retrieve and browse • Spoken documents are merely audio/video signals or a very long sequence of transcribed words including errors • It is inconvenient and inefficient for users to browse through each of them from the beginning to the end

  39. Introduction (2/3) • Spoken document summarization, which aims to generate a summary automatically for the spoken documents, is the key for better speech understanding and organization • Extractive vs. Abstractive Summarization • Extractive summarization is to select a number of indicative sentences or paragraphs from original document and sequence them to form a summary • Abstractive summarization is to rewrite a concise abstract that can reflect the key concepts of the document • Extractive summarization has gained much more attention in the recent past

  40. Introduction (3/3)

  41. Spoken-document Summarization The emergence of new areas such as multi-document summarization (1997), multiligual summarization, and multimedia summarization (1997) Text-document Summarization More natural language generation work begins to focus on text summarization The first discourse-based approaches based on story grammars (1980) The first SVD-based approach (1995) The use of location features (1969) The first training approach (1995) The first entity-level approaches based on syntactic analysis (1961) A variety of different work (entity-level approaches based on AI、logic and production rules semantic networks、hybrid approaches) The emergence of more extensive entity-level approaches (1972) Recent work has almost exclusively focused on extract rather than abstracts. A renewed interest in earlier surface-level approaches. The extended surface-level approach to include the used of cue phrases Early system using a surface-level approach (1958) 1950 1960 1970 1980 1990 2000 History of Summarization Research

  42. Extraction Based on Sentence Locations/Structures • Sentence extraction using sentence location information • Lead (Hajime and Manabu 2000) • Focusing on the introductory and concluding segments (Hirohata et al. 2005) • Specific structure on some domain (Maskey et al. 2003) • E.g., broadcast news programs-sentence position, speaker type, previous-speaker type, next-speaker type, speaker change

  43. Statistical Summarization Approaches (1/7) • Spoken sentences are ranked and selected based on some similarity measures or significant scores (a) Similarity Measures • Vector Space Model (VSM) (Ho 2003) • The document and sentence of it are represented in vector forms • The sentences that have the highest relevance scores to the whole document are selected • To summarize more important and different concepts in a document • Relevance measure (Gong et al. 2001) • Maximum Marginal Relevance (MMR) (Murray et al. 2005)

  44. Statistical Summarization Approaches (2/7) (a) Similarity Measures • Relevance measure (Gong et al. 2001) • Maximum Marginal Relevance, MMR (Murray et al. 2005) D

  45. Statistical Summarization Approaches (3/7) (b) SVD-based Method • The sentence can also be represented as a semantic vector • While the sentence with more topic or semantic information are selected • LSA (Gong et al. 2001) • DIM (Hirohata et al. 2005)

  46. Statistical Summarization Approaches (4/7) (c) Sentence Significance Score (SIG) • Each sentence in the document is represented as a sequence of terms, which can be simply given by a significance score • Features such as the confidence score, linguistic score or prosodic information also can be further integrated • Sentence selection can be performed based on this score • E.g., Given a sentence • Linguistic score: • Significance score: • Or Sentence Significance Score (Hirohata et al. 2005)

  47. Statistical Summarization Approaches (5/7) (c) Sentence Significance Score • Sentence: • :statistical measure, such as TF/IDF • :linguistic measure, e.g., named entities and POSs • :confidence score • :N-gram score • is calculated from the grammatical structure of the sentence • Statistical measure also can be evaluated using PLSA (Probabilistic Latent Semantic Analysis) • Topic Significance • Term Entropy

  48. Statistical Summarization Approaches (6/7) (d) Classification-based Methods • These methods need a set of training documents (or labeled data) for training the classifiers • Naïve Bayes’ Classifier/Bayesian Network Classifier (Kupiec 1995, Koumpis et al. 2005, Maskey et al. 2005) • Support Vector Machine (SVM) (Zhu and Penn 2005) • Logistic Regression (Zhu and Penn 2005) • Gaussian Mixture Models (GMM) (Murray et al. 2005) Summary Non-summary

  49. Statistical Summarization Approaches (7/7) (e) Combined Methods (Hirohata et al. 2005) • Sentence Significance Score (SIG) combined with Location Information • Latent semantic analysis (LSA) combined with Location Information • DIM combined with Location Information

  50. Probabilistic Generative Approaches (1/7) • MAP criterion for sentence selection • Sentence prior • Sentence prior is simply set to uniform here • Or may have to do with • Sentence duration/position, correctness of sentence boundary, confidence score, prosodic information, etc. • Each sentence of the document can be ranked by this likelihood value Sentence prior Sentence model