Please use this identifier to cite or link to this item:
https://www.um.edu.mt/library/oar/handle/123456789/47775
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.date.accessioned | 2019-10-23T09:23:09Z | - |
dc.date.available | 2019-10-23T09:23:09Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Grech, B. (2019). Models for word prediction and information retrieval (Bachelor's dissertation). | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/47775 | - |
dc.description | B.SC.(HONS)STATS.&OP.RESEARCH | en_GB |
dc.description.abstract | In the past, many techniques have been explored to predict words in a sentence or extract information from textual documents based on a query. The purpose of this thesis is to compare and contrast different word-prediction models in the Natural Language Processing area of N-grams which can ultimately be used to facilitate the process of writing texts which follow a certain structure or pattern. Moreover, an Information Retrieval System will be created using the word2vec framework which can be used to obtain a pre-specified number of documents based on a given query. The application section involves applying these models on data acquired from the recruitment industry which can be split as job descriptions and CVs. | en_GB |
dc.language.iso | en | en_GB |
dc.rights | info:eu-repo/semantics/restrictedAccess | en_GB |
dc.subject | Information storage and retrieval systems | en_GB |
dc.subject | Natural language processing (Computer science) | en_GB |
dc.subject | Neural networks (Computer science) | en_GB |
dc.title | Models for word prediction and information retrieval | en_GB |
dc.type | bachelorThesis | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. | en_GB |
dc.publisher.institution | University of Malta | en_GB |
dc.publisher.department | Faculty of Science. Department of Statistics and Operations Research | en_GB |
dc.description.reviewed | N/A | en_GB |
dc.contributor.creator | Grech, Brandon | - |
Appears in Collections: | Dissertations - FacSci - 2019 Dissertations - FacSciSOR - 2019 |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
19BSCBFSOR003.pdf Restricted Access | 4.16 MB | Adobe PDF | View/Open Request a copy |
Items in OAR@UM are protected by copyright, with all rights reserved, unless otherwise indicated.