An Application Of Word Embeddings In Recommending Alternative Query Terms In Domain-Speci c Search
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2019-06-01
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en
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Abstract
A Query is a statement of a requester specifying their information
need. A poorly formed query can be ambiguous, which can lead to poor
performance of an information retrieval machine. Aiding the user by sug-
gesting di erent query terms could be of use in avoiding this problem.
A common way of nding query recommendations is by using query logs
(Baeza-Yates et al.). However, smaller companies and institutions that
operate in a speci c domain rarely possess such query logs as they require
large user-bases. Instead of using logs, one could use a language model to
nd semantically similar terms to the input. A popular example of such
a model is a word embeddings (Word2Vec, Mikolov et al.) model. This
technique uses a neural network to encode word features to real vectors
based on neighboring words in texts of a corpus. These vectors can be
compared, so similar words to an input can be extracted. This research
proposes a system that can recommend words based on single query terms
provided by a user. This system functions as an add-on to an existing
domain-speci c search engine. A model was trained as part of this thesis
and its quality was evaluated. Furthermore, the model was used in a rec-
ommendation system and subsequently experimented with. No signi cant
evidence was found regarding a performance gain in this thesis. Improve-
ments are proposed that could potentially lead to a signi cant result in
the future.
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Faculteit der Sociale Wetenschappen