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Semantic disambiguation using Distributional Semantics
Název práce v češtině: Semantic disambiguation using Distributional Semantics
Název v anglickém jazyce: Semantic disambiguation using Distributional Semantics
Klíčová slova: -
Akademický rok vypsání: 2010/2011
Typ práce: diplomová práce
Jazyk práce: angličtina
Ústav: Ústav formální a aplikované lingvistiky (32-UFAL)
Vedoucí / školitel: RNDr. Jiří Hana, Ph.D.
Řešitel: skrytý - zadáno a potvrzeno stud. odd.
Datum přihlášení: 10.12.2010
Datum zadání: 14.01.2011
Datum a čas obhajoby: 10.05.2012 00:00
Datum odevzdání elektronické podoby:13.04.2012
Datum odevzdání tištěné podoby:13.04.2012
Datum proběhlé obhajoby: 10.05.2012
Oponenti: Mgr. Barbora Vidová Hladká, Ph.D.
Zásady pro vypracování
The goal of this thesis is to employ the combination of Distributional Semantics as used in Natural Language Programming (e.g. Schütze 1998) and of the traditional propositional semantics, as suggested for example by E. Hovy (2010), in a task of automatic categorization (for example, lemma disambiguation on the Prague Dependency Treebank).
E. Hovy's semantics combines traditional propositional semantics based on symbolic logic and statistical word distribution information of Distributional Semantics as used in Natural Language Programming (e.g. Schütze 1998). The core resource is a single lexico-semantic lexicon where concepts are organized as tensors encoding strenght of relations
to other concepts. Using these strenghts of relations, appropriateness of terms given a particular context can be determined, and used for a variety of tasks, including term disambiguation. Distributional Semantics has a strong cognitive plausibility, as shown for example by its ability to predict human brain activity associated with the meanings of nouns (Mitchell et al 2008).
The result of this thesis should be a system performing automatic categorization using Hovy's semantics, for example, a system for lexical disambiguation tested on the Prague Dependency Treebank. Lexical disambiguation is a process of determining the correct meaning of a word based on its context (e.g. determining whether 'bank' refers to an institution or to a river bank).
Seznam odborné literatury
Hovy, Eduard (2010): Distributional Semantics and the Lexicon, Keynote speech at COLLING 2010.

Landauer, Thomas K. and Dumais, Susan T. (1997). A solution to Plato's problem: The latent semantic analysis theory of acquisition, induction and representation of knowledge. Psychological Review, 104(2), 211-240.

Mitchell, Tom M.; Shinkareva, Svetlana V.; Carlson, Andrew; Chang, Kai-Min; Malave, Vicente L.; Mason, Robert A.; Just, Marcel Adam (2008). Predicting human brain activity
associated with the meanings of nouns. Science, 320, 1191-1195.

Schütze, Hinrich (1998). Automatic word sense discrimination. Computational Linguistics, 24(1), 97-123.

Stefan Evert, Alessandro Lenci: Distributional Semantic Models - A course at ESSLLI 2009, Bordeaux, July 27-31 2009.

Lin, Dekang (1998). Automatic retrieval and clustering of similar words. In Proceedings of the 17th International Conference on Computational Linguistics (COLING-ACL 1998), pages 768-774, Montreal, Canada.
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