@inproceedings{vulic-etal-2017-word,
title = "Word Vector Space Specialisation",
author = "Vuli{\'c}, Ivan and
Mrk{\v{s}}i{\'c}, Nikola and
Pilehvar, Mohammad Taher",
editor = "Klementiev, Alexandre and
Specia, Lucia",
booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Tutorial Abstracts",
month = apr,
year = "2017",
address = "Valencia, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/E17-5004",
abstract = "Specialising vector spaces to maximise their content with respect to one key property of vector space models (e.g. semantic similarity vs. relatedness or lexical entailment) while mitigating others has become an active and attractive research topic in representation learning. Such specialised vector spaces support different classes of NLP problems. Proposed approaches fall into two broad categories: a) Unsupervised methods which learn from raw textual corpora in more sophisticated ways (e.g. using context selection, extracting co-occurrence information from word patterns, attending over contexts); and b) Knowledge-base driven approaches which exploit available resources to encode external information into distributional vector spaces, injecting knowledge from semantic lexicons (e.g., WordNet, FrameNet, PPDB). In this tutorial, we will introduce researchers to state-of-the-art methods for constructing vector spaces specialised for a broad range of downstream NLP applications. We will deliver a detailed survey of the proposed methods and discuss best practices for intrinsic and application-oriented evaluation of such vector spaces.Throughout the tutorial, we will provide running examples reaching beyond English as the only (and probably the easiest) use-case language, in order to demonstrate the applicability and modelling challenges of current representation learning architectures in other languages.",
}
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%0 Conference Proceedings
%T Word Vector Space Specialisation
%A Vulić, Ivan
%A Mrkšić, Nikola
%A Pilehvar, Mohammad Taher
%Y Klementiev, Alexandre
%Y Specia, Lucia
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F vulic-etal-2017-word
%X Specialising vector spaces to maximise their content with respect to one key property of vector space models (e.g. semantic similarity vs. relatedness or lexical entailment) while mitigating others has become an active and attractive research topic in representation learning. Such specialised vector spaces support different classes of NLP problems. Proposed approaches fall into two broad categories: a) Unsupervised methods which learn from raw textual corpora in more sophisticated ways (e.g. using context selection, extracting co-occurrence information from word patterns, attending over contexts); and b) Knowledge-base driven approaches which exploit available resources to encode external information into distributional vector spaces, injecting knowledge from semantic lexicons (e.g., WordNet, FrameNet, PPDB). In this tutorial, we will introduce researchers to state-of-the-art methods for constructing vector spaces specialised for a broad range of downstream NLP applications. We will deliver a detailed survey of the proposed methods and discuss best practices for intrinsic and application-oriented evaluation of such vector spaces.Throughout the tutorial, we will provide running examples reaching beyond English as the only (and probably the easiest) use-case language, in order to demonstrate the applicability and modelling challenges of current representation learning architectures in other languages.
%U https://aclanthology.org/E17-5004
Markdown (Informal)
[Word Vector Space Specialisation](https://aclanthology.org/E17-5004) (Vulić et al., EACL 2017)
ACL
- Ivan Vulić, Nikola Mrkšić, and Mohammad Taher Pilehvar. 2017. Word Vector Space Specialisation. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts, Valencia, Spain. Association for Computational Linguistics.