An Application Of Word Embeddings In Recommending Alternative Query Terms In Domain-Speci c Search

Simple item page
dc.contributor.advisorGrootjen, F.A.
dc.contributor.authorMoonemans, S.P.J.
dc.date.issued2019-06-01
dc.description.abstractA 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.en_US
dc.identifier.urihttps://theses.ubn.ru.nl/handle/123456789/12562
dc.language.isoenen_US
dc.thesis.facultyFaculteit der Sociale Wetenschappenen_US
dc.thesis.specialisationBachelor Artificial Intelligenceen_US
dc.thesis.studyprogrammeArtificial Intelligenceen_US
dc.thesis.typeBacheloren_US
dc.titleAn Application Of Word Embeddings In Recommending Alternative Query Terms In Domain-Speci c Searchen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Moonemans, S.-s4608364.pdf
Size:
244.28 KB
Format:
Adobe Portable Document Format
Download
Collections
Faculteit der Sociale Wetenschappen