Distributional Semantic Models in Lexical

Transcript:Distributional Semantic Models in Lexical:
Distributional Semantic Models in Lexical Typology: Constructing a typological questionnaire Daria Ryzhova School of Linguistics NRU HSE Outline Lexical Typology: Frame-based Approach Ideology Typological questionnaire Constructing a questionnaire with Distributional Semantic Models tasks DSModels Clustering Evaluation Conclusions Lexical Typology: Frame-based Approach MLexT E. Rakhilina T. Reznikova B. Orekhov A. Vyrenkova D. Ryzhova M. Kyuseva E. Kashkin L. Kholkina L. Khokhlova A. Panina E. Rudnickaja E. Parina V. Krugljakova E. Kozlova M. Tagabileva M. Shapiro I. Stenin А. Ladygina E. Luchina A. Kozlov M. Privizentseva E. Kuzmenko E. Mustakimova Maksimova E. Baskakova E. Pavlova … MLexT: it works! Majsak, Rakhilina (eds.) 2007 Britsyn, Rakhilina, Reznikova, Yavorska (eds.) 2010 Work in progress: Verbs of rotation Verbs of oscillation Quality concepts (mainly physical qualities: SOFT / HARD, HEAVY / LIGHT, FULL / EMPTY, SHARP / BLUNT, ROUGH, DIRECT, … Ideology Distributional hypothesis: Meaning via the prism of co-occurrence Moscow Semantic School tradition (Apresjan 1974) Meaning = co-occurrence: deep well deep river deep plate deep + ‘container’ => size deep sympathy deep impression deep grief deep + ‘emotion’ => intensifier deep blue deep red deep + ‘colour’ => ‘dark’ Typological extension: Russian glubokij ‘deep’ deep well deep river deep plate deep + ‘container’ => size deep sympathy deep impression deep grief deep + ‘emotion’ => intensifier deep blue deep red deep + ‘color’ => saturation Typological questionnaire Typological questionnaire: “shuttle” method Russian ostryj Serbian oštar: ‘sharp knife’, ‘sharp spear’, ‘sharp contrast’, ‘sharp picture’, ‘prickly blanket’ Russian ostryj, rezkij, kolyuchij Typological questionnaire Very labor-consuming! Typological questionnaire Very labor-consuming! Is it possible to construct a questionnaire automatically? Constructing a typological questionnaire with Distributional Semantic Models Tasks to collect a set of collocations (= examples) to divide it into frames Step 1: List of collocations The main subcorpus of Russian National Corpus (lemmatized) List of nouns co-occurring with the adjective in question in the corpus [window = +1] Collocations occurring more than 10 times only Step 2: Dividing into frames Vector representation for every collocation (Distributional Semantic Models) Clustering of the vector space DSModels: parameters Vectors of co-occurrences Dimensions: 10 000 most frequent content words Dimension values: number of co-occurrences Window: ±5 content words ±5 window Вместо этого он [раскрыл свой кожаный чемоданчик, достал из него несколько острых ножей и соединил их один с другим, так что получилась длинная] сабля. Instead, he [opened his leathern case, took from it several sharp knives, and joined them one after