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Lecture 1: Semantic Analysis in Language Technology

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Lecture 1: Semantic Analysis in Language Technology

  1. 1. Semantic Analysis in Language Technology Lecture 1: Introduction Course Website: http://stp.lingfil.uu.se/~santinim/sais/sais_fall2013.htm MARINA SANTINI PROGRAM: COMPUTATIONAL LINGUISTICS AND LANGUAGE TECHNOLOGY DEPT OF LINGUISTICS AND PHILOLOGY UPPSALA UNIVERSITY, SWEDEN 12 NOV 2013
  2. 2. Acknowledgements 2  Thanks to Mats Dahllöf for the many slides I borrowed from his previous course and for structuring such an interesting and comprehensive content. Lecture 1: Introduction
  3. 3. Practical Information 3 INTENDED LEARNING OUTCOMES ASSIGNMENTS AND EXAMINATION READING LIST DEMOS Lecture 1: Introduction
  4. 4. Course Website & Contact Details 4  Course website:  http://stp.lingfil.uu.se/~santinim/sais/sais_fall2013.htm  Contact details:  santinim@stp.lingfil.uu.se  marinasantini.ms@gmail.com  marinaromestockholm@gmail.com Lecture 1: Introduction
  5. 5. Check the website regularly and make sure to refresh the page: we are building up this course together, so this page will be continously updated! 5 Lecture 1: Introduction
  6. 6. About the Course 6  Introduction to Semantics in Language Techology and NLP.  Focus on methods used in Language Technology and NLP for the perform the following tasks:     Sentiment Analysis (SA) Information Extraction (IE) Word Sense Disambiguation (WSD) Predicate-Argument Extraction (PAS) Lecture 1: Introduction
  7. 7. Intended Learning Outcomes 7  In order to pass the course, a student must be able to:  describe systems that perform the following tasks, apply them to authentic linguistic data, and evaluate the results: 1. detect and extract attitudes and opinions from text, i.e. Sentiment Analysis (SA); 2. use semantic analysis in the context of Information Extraction (IE) 3. disambiguate instances of polysemous lemmas, i.e. Word Sense Disambiguation (WSD); 4. use robust methods to extract the Predicate-Argument Structure (PAS). Lecture 1: Introduction
  8. 8. Compulsory Readings 8 1. Bing Liu (2012) Sentiment Analysis and Opinion Mining, Morgan & Claypool. 2. Richard Johansson and Pierre Nugues. 2008. Dependency-based Syntactic– Semantic Analysis with PropBank and NomBank, CoNLL 2008: Proceedings of the 12th Conference on Computational Natural Language Learning. 3. Daniel Jurafsky and James H. Martin (2009), Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Second Edition, Pearson Education. 4. Daniel Gildea and Daniel Jurafsky. 2002. Automatic Labeling of Semantic Roles, Computational Linguistics 28:3, 245-288. 5. M Palmer, D Gildea, P Kingsbury. 2005. The proposition bank: An annotated corpus of semantic roles, Computational Linguistics 31 (1), 71-106. 6. Additional suggested readings will be listed at the end of each lecture Lecture 1: Introduction
  9. 9. Demos & Tutorials 9  This list will be continuosly updated, also with your contribution… Lecture 1: Introduction
  10. 10. Assignments and Examination 10  Four Assignments: 1. Essay writing: independent study of a system, an approach, or a field within semantics-oriented language technology. The study will be presented both as a written essay and an oral presentation. The essay work will also include a feedback step where the work of another group is reviewed. 2. Assignment on Predicate-Argument Structure (PAS) 3. Assignment on Sentiment Analysis (SA) 4. Assignment on Word Sense Disambiguation (WSD)  General Info:  No lab sessions, supervision by email  Essay and assignments must be submitted to santinim@stp.lingfil.uu.se  Examination:  Written report submitted for each assignment  All four assignments necessary to pass the course  Grade G will be given to students who pass each assignment. Grade VG to those who pass the essay assignment and at least one of the other ones with distinction. Lecture 1: Introduction
  11. 11. IMPORTANT! 11  Start thinking about a topic you are interested in for your essay writing assignment! Lecture 1: Introduction
  12. 12. Practical Organization 12  45min + 15 min break  Lectures on Course webpage and SlideShare  Email all your questions to me: santinim@stp.lingfil.uu.se  IMPORTANT:  Send me an email to santinim@stp.lingfil.uu.se, so I make sure that I have all the correct email addresses. If you do not get an acknowledgement of receipt, please give me a shout! Lecture 1: Introduction
  13. 13. Interaction and Cooperation 13  Communicate with me and with your classmates to exchange ideas, if you have problems in understanding notions and concepts or practical implementations.  Recommemdation: share your knowledge with your peers and steam off stress.  Cheating is not permitted  Lecture 1: Introduction
  14. 14. Semantics in Language Technology Overview 14 SEMANTICS IN LANGUAGE TECHNOLOGY APPLICATIONS LEXICAL SEMANTICS REPRESENTATION OF MEANING SUMMARY Lecture 1: Introduction
  15. 15. 15 Semantics in Language Technology Lecture 1: Introduction
  16. 16. Logic and Semantics 16  Aristotelian logic – important ever since.  Syllogisms, e.g.:  Premise: No reptiles have fur.  Premise: All snakes are reptiles.  Conclusion: No snakes have fur.  Modern logic develops, late 19th Century – more general and systematic.  Formal semantics in linguistics and philosophy based on logic (20th Century). Lecture 1: Introduction
  17. 17. Formal and Computational Semantics 17  Computational semantics “is the study of how to automate the process of constructing and reasoning with meaning representations of natural language expressions.” (Wikipedia).  Early systems rule-based, most famous example: “Montague grammar” (1970). Sophisticated mechanisms for translation of English into a very rich logic.  Language technology: Recent interest in data-driven and machine learning-based methods. Lecture 1: Introduction
  18. 18. Semantics in NLP 18  NLP semantics is typically more limited in scope than NL semantics as analysed in linguistics and philosophy.  NLP applications often handle semantic aspects without having explicitly semantic components, e.g. in machine translation.  Other aspects of language – morphology, syntax, etc. – can be seen as support systems for semantics: The purpose of language lies in the use of expressions as carriers of semantic meaning. And that is what many NLP systems have to respect, e.g. MT, retrieval, classification, etc. Lecture 1: Introduction
  19. 19. Semantics and Truth (i) 19 Semantics, meanings and states of affairs:  What a sentence means: a structure involving (lexical) concepts and relations among them. Can be articulated as a semantic representation. E.g. I ate a turkey sandwich. in predicate logic:  A sentence and the semantic representation of a sentence is also the representation of a possible state of affairs. Lecture 1: Introduction
  20. 20. Semantics and Truth (ii) 20  Correspondence theory of truth: If the content of a sentence corresponds to an actual state of affairs if it is true; otherwise, it is false.  Ignoring philosophical complications, in many cases we can extract knowledge from texts. E.g. Warmer climate entails increased release of carbon dioxide by inland lakes. (From uu.se press release.)  Related issue: Which texts should we trust?  Many sentences are difficult to formalize in logic. (Modality, conditionality, vague quantification, tense, etc.) Lecture 1: Introduction
  21. 21. 21 Representation of Meaning Lecture 1: Introduction
  22. 22. Formalizing Meaning 22  Linguistic content has – at least to a certain degree – a logical structure that can be formalized by means of logical calculi – meaning representations.  The representation languages should be simple and unambiguous – in contrast to complex and ambiguous NL.  Logical calculi come with accounts of logical inference. They are useful for reasoning-based applications.  Meaning formalization faces far-reaching conceptual and  computational difficulties. Lecture 1: Introduction
  23. 23. Compositionality 23  Linguistic content is compositional: Simple expressions have a given (lexical) meaning; the meaning of complex expressions is determined by the meanings of their constituents. People produce and understand new phrases and sentences all the time. (NLP must also deal with these.)  Compositionality is studied in detail in compositional syntax-driven semantics. Work in this field is typically about hand-coded rule systems for small fragments of NL. Lecture 1: Introduction
  24. 24. Compositional Aspects 24 Lecture 1: Introduction
  25. 25. Compositional Aspects – Argument Structure 25 Lecture 1: Introduction
  26. 26. Discourse-Related Aspects 26 Lecture 1: Introduction
  27. 27. Compositional semantics in Language Technology 27 Lecture 1: Introduction
  28. 28. First-Order Predicate Logic (i) 28  “flexible, well-understood, and computationally     tractable approach to the representation of knowledge [and] meaning” (J&M. 2009: 589) expressive verifiability against a knowledge base (related to database languages) inference model-theoretic semantics Lecture 1: Introduction
  29. 29. First-Order Predicate Logic (ii) 29  Boolean operators: negation and connectives  Existential/universal quantification  Individual constants  Predicates (taking a number of arguments) Lecture 1: Introduction
  30. 30. When to assume compositionality? 30 Lecture 1: Introduction
  31. 31. Multi-Word Expressions 31 MWEs (a.k.a multiword units or MUs) are lexical units encompassing a wide range of linguistic phenomena, such as idioms (e.g. kick the bucket = to die), collocations (e.g. cream tea = a small meal eaten in Britain, with small cakes and tea), regular compounds (cosmetic surgery), graphically unstable compounds (e.g. selfcontained <> self contained <> selfcontained - all graphical variants have huge number of hits in Google), light verbs (e.g. do a revision vs. revise), lexical bundles (e.g. in my opinion), etc. While easily mastered by native speakers, MWEs' correct interpretation remains challenging both for non-native speakers and for language technology (LT), due to their complex and often unpredictable nature. Lecture 1: Introduction
  32. 32. Cross-linguality Use Case: Information Access 32 In multi-ethnic societies, like the Swedish society, it is common that many non-native speakers use public websites – e.g. Arbetesförmedlingen or Pensionsmyndigheten websites – to access information that are vital to their living and integration in the host country. National regulations are often accompanied by special terminology and new coinages. For instance, the Swedish expression /egenremiss/ (14,900 hits, Google.se April 2013) – or alternatively as an MWE – /egen remiss/ (8,210 hits, Google.se April 2013) denotes a referral to a specialist doctor written by patients themselves. This expression is made up from two common Swedish words /egen/ `own (adj)' and /remiss/ `referral'. It is a recent expression (probably coined around 20101) and not yet recorded in any official dictionary nor in Wiktionary or other multilingual online lexical resources. However, it is very frequent in query logs belonging to a Swedish public health service website. When trying to implement a cross-lingual search based on the automatic translation of query logs, it turned out that none of the existing multilingual lexical resources contained this expression. Lecture 1: Introduction
  33. 33. Use Case: Personal Use & Text Understanding 33  The use of expressions that are marked for style, genre, domain, or register (and/or other textual categories), or the use of expressions which are misspelled or idiomatic for some textual category are beyond the competence of a novice reader or a non-native speaker. Additionally, in a web search or in social networks, one cannot tell if the texts one reads are good or bad the way a firstlanguage readers can. When readers/users read a language they do not know at all, they can use automatic translation or online dictionaries or other lexical resources. However, what they cannot determine well is the *type* of text one is reading. They cannot tell if the text is verbose, terse, formal, informal, stupid, funny, bad, or good.  For instance, the phrase "es ist zum Kotzen" means this is vernacular and unrefined text as well as a controversial expression. The phrase "isch alle", instead, means that this line in the text is spoken by a Berliner. Lecture 1: Introduction
  34. 34. Semantics vs Pragmatics/Discourse (i) 34  What does a word, a phrase, a text segment mean as an NL expression? (“Linguistic meaning” – semantics.) Conventional, static, systemic aspect of meaning.  What does the author intend to convey by means of a word, a phrase, a text segment? (“Speaker meaning” – pragmatics/discourse.) Contextual, dynamic aspect of meaning.  The two aspects depend on each other, of course. Lecture 1: Introduction
  35. 35. Semantics vs Pragmatics/Discourse (ii) 35 Lecture 1: Introduction
  36. 36. Semantics vs Pragmatics/Discourse (iii) 36 Lecture 1: Introduction
  37. 37. 37 Applications Lecture 1: Introduction
  38. 38. Semantics-oriented NLP applications 38  Machine translation: The translation of a text segment should mean the same as the original (to emphasize linguistic meaning) or should convey the same content (to emphasize speaker meaning).  Information extraction is to extract components of the information conveyed by a text.  Question answering is extraction – combined with inference – of an answer to a given question.  Text classification, in typical cases, relates to the meanings of the texts being classified. Lecture 1: Introduction
  39. 39. Semantics and Generation 39  Generation: semantic representation  NL. Less challenging than analysis – the structure of the input is under control. Needed in e.g. dialogue systems.  Interlingua – semantic representation in machine  translation: Analysis: source language  interlingua. Generation: interlingua  target language. Would be economic if many languages are involved. The idea has not proved very successful so far. Lecture 1: Introduction
  40. 40. Reference 40  Reference is very important – what statements are about.  Referring expressions are very common.  Reference is a discourse phenomenon.  Resolving reference is a crucial step in e.g.   extraction, e.g.in sentiment analysis translation, e.g. to get agreement right  English it vs French il/elle vs Swedish den/det. Lecture 1: Introduction
  41. 41. Reference –An Example 41 Lecture 1: Introduction
  42. 42. Kinds of Referring Expressions 42  Indefinite noun phrases. E.g. a book. Introduce new     entities. Pronouns. E.g. he. Typically coreferent with a previous referring expression (antecedent). Names. E.g. Bill Gates. Demonstrative. E.g. this room. Other definite noun phrases. E.g. the first chapter. Reference to somehow known entity, often previously mentioned. Lecture 1: Introduction
  43. 43. Named Entity Recognition (NER) 43  To identify expressions being used as names. (What characterizes a “name”?)  Also to identify what kind of name it is: E.g. of a person, or a place, or a stretch of time, or a chemical compound, or a gene, etc.  “State-of-the-art NER systems for English produce near-human performance. For example, the best system entering MUC-7 scored 93.39% of F-measure while human annotators scored 97.60% and 96.95%” (Wikipedia). Lecture 1: Introduction
  44. 44. Anaphora and Deixis Resolution 44  Pronouns (they), pronominal adverbs (there, then), and definite NP’s refer to entities by means of contextually given information.  E.g. by referring to previously mentioned referents – anaphora.  E.g. by reference based on the participants, time, and place of the discourse – deixis (e.g. I, you, here, yesterday).  Anaphora and deixis resolution is much more challenging task than NER. The reference of name-like graph words is much more predictible. Compare Barack Obama and he. Lecture 1: Introduction
  45. 45. Sentiment Analysis – an extraction task 45  What views do people express in blogs and reviews? That’s interesting for politicans and marketing people.  Opinions are often expressed in a personal and informal way. E.g. Peter bought me a Baileys marzipan chocolate thing which I washed down with Gluehwein and that, in combination with the bright lights and cheery faces really made me feel warm inside! (From a blog post.)  Sentiment analysis: to extract the referent of a “sentiment” and the polarity positive–negative associated with it. E.g. Baileys marzipan chocolate – positive. Lecture 1: Introduction
  46. 46. 46 Lexical Semantics Lecture 1: Introduction
  47. 47. Lexical Concepts 47  Words are often grammatically simple, but carry a structured conceptual content. Definitions “unpack” the content of concepts:     friend – a person whom one knows well, is loyal to, etc. turkey – a kind of animal, a bird, etc. sandwich – a kind of food item, contains bread , etc. eat – a relation (holding in/of an event) between an organism and a food item, the food is chewed and ingested, etc. Lecture 1: Introduction
  48. 48. Lexical Concepts - Decomposition 48 Lecture 1: Introduction
  49. 49. Lexical Concepts – Relations (i) 49 Lecture 1: Introduction
  50. 50. Lexical Concepts – Relations (ii) 50 Lecture 1: Introduction
  51. 51. Synonimy 51 Synonymy holds between two words (word tokens) which express the same or similar concepts.  Unsupervised detection of synonymy can be based on “The Distributional Hypothesis: words with similar distributions have similar meanings.” = The Distributional Hypothesis in linguistics is the theory that words that occur in the same contexts tend to have similar meanings. The underlying idea that "a word is characterized by the company it keeps" was popularized by Firth. “Random Indexing” is a method here. (“a high-dimensional model can be projected into a space of lower dimensionality without compromising distance metrics if the resulting dimensions are chosen appropriately”)  Synonymy knowledge useful in e.g. translation, text classification, and information extraction. Also “query expansion” in retrieval. Lecture 1: Introduction
  52. 52. Lexical Ambiguity 52 Lecture 1: Introduction
  53. 53. Lexical Ambiguity - WSD 53 Lecture 1: Introduction
  54. 54. Word Ambiguity: Homography vs Polysemy (i) 54 Lecture 1: Introduction
  55. 55. Word Ambiguity: Homography vs Polysemy (ii) 55 Lecture 1: Introduction
  56. 56. Word Senses 56  Discerning word senses (for a lemma) – lexicographical task, matter of sophisticated linguistic judgements.  Theoretical principles. Practical purpose.  Different dictionaries make different analyses.  English: WordNet – a standard resource. Lecture 1: Introduction
  57. 57. Senses of day in WordNet, for instance (i) 57 Lecture 1: Introduction
  58. 58. Senses of day in WordNet, for instance (ii) 58 Lecture 1: Introduction
  59. 59. Word Sense Disambiguation (WSD) 59  A distributional hypothesis for WSD: words representing the same sense have more similar distributions than words representing different senses. I.e. distribution similarity implies sense similiarity.  We can use this for supervised learning of WSD.  This requires data in the form of a sense-tagged corpus (based on a given sense inventory, e.g. the one given by WordNet). Lecture 1: Introduction
  60. 60. Manual Sense-Tagging 60  More difficult than typical grammatical tagging.  As we saw in the day example, senses and their distinctions can be quite subtle. Definitions and examples are often far from obvious.  Expensive: requires competent people and standardised procedures.  Quality measure: inter-annotator agreement. ” Ex: Cohen's kappa coefficient is a statistical measure of inter-rater agreement or inter-annotator agreementfor qualitative (categorical) items. It is generally thought to be a more robust measure than simple percent agreement calculation since κ takes into account the agreement occurring by chance ” Lecture 1: Introduction
  61. 61. 61 Summary Lecture 1: Introduction
  62. 62. Conclusions (i) 62  Logic-based semantics is a theoretical foundation for NLP semantics, but implemented systems are typically more coarse-grained and of a more limited scope.  Meaning depends both on literal content and contextual information. This is a challenge for most NLP tasks.  Most NLP applications have to be highly sensitive to semantics. Lecture 1: Introduction
  63. 63. Conclusions (ii) 63  Finding and interpreting names and other referential expressions is a central issue for NLP semantics.  Disambiguation of polysemous lexical tokens is also a central issue for NLP semantics.  Accessing the content of lexical tokens is also useful.  Meaning representation involves predicateargument structure, which captures a basic aspect of NL compositionality. Lecture 1: Introduction
  64. 64. 64 Start thinking about a Topic of interest for your essay writing! Tell me your thoughts next time… Lecture 1: Introduction
  65. 65. Suggested Readings 65  Term Logic (Wikipedia)  Predicate Logic (Wikipedia)  Jurafsky and Martin (2009):  Ch. 17 ”Representation of Meaning”  Ch. 18 ”Computational Semantics”  Ch. 19 ”Lexical Semantics”  Ch. 20 ”Compuational Lexical Semantics”  Clark et al. (2010):  Ch 15 ”Computational Semantics”  Indurkhya and Damerau (2010):  Ch 5 ”Semantic Analysis” Lecture 1: Introduction
  66. 66. 66 This is the end… Thanks for your attention ! Lecture 1: Introduction

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