PPT-Detecting compositionality using semantic vector space models based on syntactic context

Guillermo Garrido and Anselmo Peñas NLP amp IR Group at UNED Madrid Spain ggarridoanselmo lsiunedes Shared Task System Description ACLHLT 2011 Workshop . Wilhelm von . Humbolt. famously described language as a system that “makes infinite use of finite means.”. Infinite Noun Phrases. There are infinitely many noun phrases: you can always make another one by adding another adjective:. Corpora and Statistical Methods. Lecture 6. Semantic similarity. Part 1. Synonymy. Different phonological. /orthographic. words. highly related meanings. :. sofa / couch. boy / lad. Traditional definition:. The corpus can be used to define a . probabilistic context-free grammar. , in which each expansion of a constituent is assigned a probability. The diagram represents only the 25 most frequently-occurring expansions; these account for about 65% of all constituent tokens in the corpus (see Table 1 at right).. patterning. Syntactic patterns. Antithesis. Listing. Parallelism. Syntactic parallelism. Repetition of similar phrasal . structures. ‘. Listing. ’*. A. ntithesis. Rhetorical contrast of ideas by means of parallel arrangements of words, clauses, or sentences. for concepts. Compute posterior probabilities . or . Semantic Multinomial . (SMN) under appearance models.. But, suffers from . contextual noise. Model the distribution of SMN for each concept. : assigns high probability to “. Lexical Semantics. 2. Information Retrieval System. IR. System. Query String. Document. corpus. Ranked. Documents. 1. Doc1. 2. Doc2. 3. Doc3. .. .. The Vector-Space Model. Graphic Representation. Movement led by W3C that promotes common formats for data on the web. Describes things in a way that computer applications can understand it. Describes the relationship between things and properties of things. algorithms in. Question Answering. Alexander . Solovyev. Bauman Moscow Sate Technical University. a-soloviev@mail.ru. 20.10.2011. 1. RCDL. Voronezh.. Agenda. Question Answering and Answer Validation task. computing the similarity between words. “. fast. ” is similar to “. rapid. ”. “. tall. ” is similar to “. height. ”. Question answering:. Q. : “. How . tall. . is Mt. Everest?”. Candidate A: “The . Roi Shillo, Nick Hoernle, Kobi Gal. Creativity is…. Ubiquitous. [Schank & Cleary 95]. Fundamental . [Boden, 98]. Machine recognisable . [Newell, Shaw & Simon 62]. Focus for EDM. Open Ended Environments. Dr. Alexander Panchenko. Language Technology Group -- TU Darmstadt. panchenko@lt.informatik.tu-darmstadt.de. Agenda. . Task. Method. Evaluation. Results. Based on participation in SemEval 2016. “Taxonomy Extraction Evaluation” Task. Ladislav Gallay. Supervisor. : Ing. Marián Šimko, PhD.. Slovak University of Technology. Faculty of Informatics and Information Technologies . Lemmatization. basic form of a word. : . houses . > . the Department set of this grammar the grammar in which very powerful of movinga new AcknowledgmentsI am very grateful to the many people who have influenced this research and madethis thesis possible Introduction. Semantic Role Labeling. Agent. Theme. Predicate. Location. Can we figure out that these have the same meaning?. XYZ . corporation . bought. the . stock.. They . sold. the stock to XYZ .