Real-valued syntactic word vectors

Department of Nordic Studies and Linguistics (NorS)

Real-valued syntactic word vectors

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Real-valued syntactic word vectors. / Basirat, A.; Nivre, J.

In: Journal of Experimental and Theoretical Artificial Intelligence, Vol. 32, No. 4, 03.07.2020, p. 557-579.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Basirat, A & Nivre, J 2020, 'Real-valued syntactic word vectors', Journal of Experimental and Theoretical Artificial Intelligence, vol. 32, no. 4, pp. 557-579. https://doi.org/10.1080/0952813X.2019.1653385

APA

Basirat, A., & Nivre, J. (2020). Real-valued syntactic word vectors. Journal of Experimental and Theoretical Artificial Intelligence, 32(4), 557-579. https://doi.org/10.1080/0952813X.2019.1653385

Vancouver

Basirat A, Nivre J. Real-valued syntactic word vectors. Journal of Experimental and Theoretical Artificial Intelligence. 2020 Jul 3;32(4):557-579. https://doi.org/10.1080/0952813X.2019.1653385

Author

Basirat, A. ; Nivre, J. / Real-valued syntactic word vectors. In: Journal of Experimental and Theoretical Artificial Intelligence. 2020 ; Vol. 32, No. 4. pp. 557-579.

Bibtex

@article{4e12171940ca4858bada8c7ab2d9666b,

title = "Real-valued syntactic word vectors",

abstract = "We introduce a word embedding method that generates a set of real-valued word vectors from a distributional semantic space. The semantic space is built with a set of context units (words) which are selected by an entropy-based feature selection approach with respect to the certainty involved in their contextual environments. We show that the most predictive context of a target word is its preceding word. An adaptive transformation function is also introduced that reshapes the data distribution to make it suitable for dimensionality reduction techniques. The final low-dimensional word vectors are formed by the singular vectors of a matrix of transformed data. We show that the resulting word vectors are as good as other sets of word vectors generated with popular word embedding methods.",

keywords = "context selection, dependency parsing, entropy, singular value decomposition, transformation, Word embeddings",

author = "A. Basirat and J. Nivre",

note = "Publisher Copyright: {\textcopyright} 2019, {\textcopyright} 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.",

year = "2020",

month = jul,

day = "3",

doi = "10.1080/0952813X.2019.1653385",

language = "English",

volume = "32",

pages = "557--579",

journal = "Journal of Experimental and Theoretical Artificial Intelligence",

issn = "0952-813X",

publisher = "Taylor & Francis",

number = "4",

}

RIS

TY - JOUR

T1 - Real-valued syntactic word vectors

AU - Basirat, A.

AU - Nivre, J.

PY - 2020/7/3

Y1 - 2020/7/3

N2 - We introduce a word embedding method that generates a set of real-valued word vectors from a distributional semantic space. The semantic space is built with a set of context units (words) which are selected by an entropy-based feature selection approach with respect to the certainty involved in their contextual environments. We show that the most predictive context of a target word is its preceding word. An adaptive transformation function is also introduced that reshapes the data distribution to make it suitable for dimensionality reduction techniques. The final low-dimensional word vectors are formed by the singular vectors of a matrix of transformed data. We show that the resulting word vectors are as good as other sets of word vectors generated with popular word embedding methods.

AB - We introduce a word embedding method that generates a set of real-valued word vectors from a distributional semantic space. The semantic space is built with a set of context units (words) which are selected by an entropy-based feature selection approach with respect to the certainty involved in their contextual environments. We show that the most predictive context of a target word is its preceding word. An adaptive transformation function is also introduced that reshapes the data distribution to make it suitable for dimensionality reduction techniques. The final low-dimensional word vectors are formed by the singular vectors of a matrix of transformed data. We show that the resulting word vectors are as good as other sets of word vectors generated with popular word embedding methods.

KW - context selection

KW - dependency parsing

KW - entropy

KW - singular value decomposition

KW - transformation

KW - Word embeddings

UR - http://www.scopus.com/inward/record.url?scp=85071012514&partnerID=8YFLogxK

U2 - 10.1080/0952813X.2019.1653385

DO - 10.1080/0952813X.2019.1653385

M3 - Journal article

AN - SCOPUS:85071012514

VL - 32

SP - 557

EP - 579

JO - Journal of Experimental and Theoretical Artificial Intelligence

JF - Journal of Experimental and Theoretical Artificial Intelligence

SN - 0952-813X

IS - 4

ER -

ID: 366046134