30.4391, Diss: English; Hungarian; Spanish; Computational Linguistics; Semantics; Text/Corpus Linguistics: András Dobó: ''A comprehensive analysis of the parameters in the creation and comparison of feature vectors in distributional semantic models for multiple languages''

[The LINGUIST List]

LINGUIST List: Vol-30-4391. Tue Nov 19 2019. ISSN: 1069 - 4875.

Subject: 30.4391, Diss: English; Hungarian; Spanish; Computational Linguistics; Semantics; Text/Corpus Linguistics: András Dobó: ''A comprehensive analysis of the parameters in the creation and comparison of feature vectors in distributional semantic models for multiple languages''

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Date: Tue, 19 Nov 2019 19:29:50
From: András Dobó [dobo at inf.u-szeged,hu]
Subject: A comprehensive analysis of the parameters in the creation and comparison of feature vectors in distributional semantic models for multiple languages

 
Institution: University of Szeged 
Program: Doctoral School of Computer Science 
Dissertation Status: Completed 
Degree Date: 2019 

Author: András Dobó

Dissertation Title: A comprehensive analysis of the parameters in the creation
and comparison of feature vectors in distributional
semantic models for multiple languages 

Dissertation URL:  http://doktori.bibl.u-szeged.hu/10120/

Linguistic Field(s): Computational Linguistics
                     Semantics
                     Text/Corpus Linguistics

Subject Language(s): English (eng)
                     Hungarian (hun)
                     Spanish (spa)


Dissertation Director(s):
János Csirik

Dissertation Abstract:

Measuring the semantic similarity and relatedness of words is important for
many natural language processing tasks. Although distributional semantic
models designed for this task have many different parameters, such as vector
similarity measures, weighting schemes and dimensionality reduction
techniques, there is no truly comprehensive study simultaneously evaluating
these parameters while also analysing the differences in the findings for
multiple languages.

We would like to address this gap with our systematic study by searching for
the best configuration in the creation and comparison of feature vectors in
distributional semantic models for English, Spanish and Hungarian separately,
and then comparing our findings across these languages.

During our extensive analysis we test a large number of possible settings for
all parameters, with more than a thousand novel variants in case of some of
them. As a result of this we were able to find such configurations that
significantly outperform conventional configurations and achieve
state-of-the-art results.




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LINGUIST List: Vol-30-4391	
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