Wrapping up. Computational Lexical Semantics. Gemma Boleda 1 Stefan Evert 2. ESSLLI. Bordeaux, France, July 2009.

Transcription

1 Computational Lexical Semantics Gemma Boleda 1 Stefan Evert 2 1 Universitat Politècnica de Catalunya 2 University of Osnabrück ESSLLI. Bordeaux, France, July / 18

2 Outline / 18

3 Computational Lexical Semantics inter-annotator studies and Machine Learning approaches to semantic tasks 1 (eventually) improve applications 2 tools in developing semantic theories and getting the basic facts right empirical data tests and measures for hypotheses 3 / 18

4 The role of competitions in Computational Lexical Semantics SemEval, SensEval; shared tasks at CoNLL, EMNLP, ESSLLI,... relevant role: common efforts in the community same problems same datasets (usually made publicly available) gradual increase in difficulty comparability, measure of progress some cons often artificial setup and tasks (necessary step?) focus on particular tasks (WSD, SRL) and not others watch out for the next SemEval in / 18

5 Outline / 18

6 Questionnaire Concepts: hyponymy? thematic roles? selectional restrictions? Resources: ever heard about WordNet? ever browsed through WordNet? FrameNet? PropBank? Tasks: Word Sense Disambiguation? Semantic Role Labeling? Methods and tools: ever done any annotation? Machine Learning experiment? Methodology? ever heard about Weka? 6 / 18

7 Messages The most fundamental problems that empirical computational lexical semantics faces nowadays are due to a lack of theoretical understanding. Lexical semantics not as mature as other fields in linguistics (phonetics, syntax) Conceptual challenges what information to encode how to represent meaning how to represent relations between senses... 7 / 18

8 Messages The most fundamental problems that empirical computational lexical semantics faces nowadays are due to a lack of theoretical understanding. Lexical semantics not as mature as other fields in linguistics (phonetics, syntax) Conceptual challenges what information to encode how to represent meaning how to represent relations between senses... 7 / 18

9 Messages Linguists have a lot to say in that respect but we have to learn how to play the game by the rules Statistical analysis Machine Learning methodology Programming tools and resources: R, Weka, Python,... This course: basics to start playing the game Methodology, resources, tools State of the art and main problems 8 / 18

10 Good news it can be done especially with the help of tools R, weka,... you can use them at several levels as black boxes, to help you gain insight into the problem knowing a bit more knowing a lot innovating (implementing your own (open-source) method?) 9 / 18

11 Good news it can be done especially with the help of tools R, weka,... you can use them at several levels as black boxes, to help you gain insight into the problem knowing a bit more knowing a lot innovating (implementing your own (open-source) method?) 9 / 18

12 Outline / 18

13 Not covered in the course Word Similarity / Word Relatedness WordNet-based measures (intuition: distance in graph) distributional approaches (see course by A. Lenci and S. Evert!) Lexical Acquisition e.g. automatic classification of verbs into semantic classes (induction of frames, Levin classes,... ) syntax-semantics interface Word Relations Acquisition: e.g. automatic detection of hyponyms (Hearst 1992) Labeling: our case study 11 / 18

14 Not covered in the course Tools: formalization! how to represent lexical meaning so it can be used computationally? WordNet: simply through labeled links (hyponymy... ) (formal semantics has not focused on lexical semantics) frameworks: Generative Lexicon, Minimal Recursion Semantics, Ontological Semantics... but no common ground that is common to most linguists (as in phonetics, morphology, syntax) 12 / 18

15 Outline / 18

16 Tools and resources WordNet, FrameNet, VerbNet, OntoNotes, etc. Weka (which you already know now) and Weka book very good introduction to Machine Learning (Part I) very good tutorial of Weka (Part II) 14 / 18

17 If you want to learn how to program we recommend that you use Python it has some drawbacks (regular expressions,... ) but it is high-level, object-oriented, and has several facilities for text processing in conjunction with NLTK Natural Language Toolkit facilities to access corpora and resources some drawbacks: heavily oriented towards symbolic approaches (vs. statistical methods) but useful nevertheless NLTK book just came out (O Reilly) 15 / 18

18 If you want to learn how to program we recommend that you use Python it has some drawbacks (regular expressions,... ) but it is high-level, object-oriented, and has several facilities for text processing in conjunction with NLTK Natural Language Toolkit facilities to access corpora and resources some drawbacks: heavily oriented towards symbolic approaches (vs. statistical methods) but useful nevertheless NLTK book just came out (O Reilly) 15 / 18

19 Accessing WordNet in NLTK 16 / 18

20 And if you want to explore your data corpus processing and querying: Corpus WorkBench! graphics, statistical tests, and more: R! (Links to all these resources and tools soon on the course web page.) 17 / 18

21 Computational Lexical Semantics Gemma Boleda 1 Stefan Evert 2 1 Universitat Politècnica de Catalunya 2 University of Osnabrück ESSLLI. Bordeaux, France, July / 18