Greedy Universal Dependency Parsing with Right Singular Word Vectors

• A set of continuous feature vectors formed by right singular vectors of a transformed co-occurrence matrix are used with the Stanford neural dependency parser to train parsing models for a limited number of languages in the corpus of universal dependencies. We show that the feature vector can help the parser to remain greedy and be as accurate as (or even more accurate than) some other greedy and non-greedy parsers.

Ämnesord

HUMANIORA  -- Språk och litteratur -- Jämförande språkvetenskap och allmän lingvistik (hsv//swe)

HUMANITIES  -- Languages and Literature -- General Language Studies and Linguistics (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)

NATURVETENSKAP  -- Data- och informationsvetenskap -- Språkteknologi (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Language Technology (hsv//eng)

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