Yuliya Morozova Institute for Informatics Problems of the Russian Academy of Sciences, Moscow

Distributional semantics new area of linguistic research inferring semantic properties of linguistic units from corpora Theoretical foundations: distributional methodology by Z. Harris, F. de Saussure, L. Wittgenstein. Distributional hypothesis: semantically similar words occur in similar contexts. J. R. Firth “You shall know a word by the company it keeps”.

Vector space drink coffee – occurred 1 time drink tea – occurred 2 times

Cosine measure of vector similarity

Main application areas lexical ambiguity resolution information retrieval dictionaries of semantic relations multilingual dictionaries semantic maps of different domains modelling of synonymy document topic detection sentiment analysis

The present research Goal: to apply distributional semantics models to extraction of translation correspondences from a parallel corpus. Vector space model + test corpus

Test corpus Patent texts in French translated into Russian Texts splitted into sentences Alignment at the sentence level – manually verified (in the visual editor MakeBilingua) Uploaded to the Sketch Engine corpus manager

Preprocessing Lemmatization Frequent words removed (prepositions, conjunctions etc.) Punctuation marks removed

Vector space model type of linguistic units: single words; type of context: aligned regions; frequency measure: Boolean frequency (equal either to 1 or 0); method used to compute the distance between vectors: cosine measure.

Example (aligned region as a context) Aligned region #1 présent invention concerner liant minéral notamment hydraulique настоящий изобретение касаться неорганический связующий частность гидравлический связующий

Example (vector space) Aligned region#1#2#3 présent1…… invention1…… concerner1…… настоящий1…… изобретение1…… касаться1……

Results A list of translation correspondences. Linguistic filter: the same part of speech. Precision: 78%.

Correspondences with different POS Syntactic transformations verbal infinitive (French) → noun (Russian) traiter (“to process”) → обработка (“processing”) noun (French) → adjective (Russian) crochet (“hook”) → крюкообразный (“hook-shaped”) verbal infinitive (French) → adjective (Russian) connaître (“to know”) → известный (“well-known”)

Correspondences with different POS Parts of multi-word expressions au moins (“at least”) → по меньшей мере (“at least”) The output of the program: moins → мера

Evaluation Eduardo Cendejas, Grettel Barceló, Alexander Gelbukh, Grigori Sidorov. Incorporating Linguistic Information to Statistical Word-Level Alignment // Proceedings of the 14th Iberoamerican Conference on Pattern Recognition, CIARP 2009, Guadalajara, Jalisco, Mexico, November 15-18, Vector space model + similarity measures PMI, T- score, Log-likelihood ratio and Dice coefficient. Precision – 53%.

Conclusion Distributional semantics methodology can be used to extract translation correspondences from a parallel corpus with a high level of precision. It can be used to study productive syntactic transformations occurring in translation. The present vector space model needs to be enhanced to take into account multi-word expressions.

Thank you!