Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
Mixed-Language Arabic- English Information Retrieval
Includes abstract.
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | English |
| Published: |
Department of Computer Science
2014
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| _version_ | 1867613546664165376 |
|---|---|
| access_status_str | Open Access |
| author | Mustafa, Ali Mohammed |
| author2 | Suleman, Hussein |
| author_browse | Mustafa, Ali Mohammed Suleman, Hussein |
| author_facet | Suleman, Hussein Mustafa, Ali Mohammed |
| author_sort | Mustafa, Ali Mohammed |
| collection | Thesis |
| description | Includes abstract. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/6421 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:37:52.426Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2014 |
| publishDateRange | 2014 |
| publishDateSort | 2014 |
| publisher | Department of Computer Science |
| publisherStr | Department of Computer Science |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/6421 Mixed-Language Arabic- English Information Retrieval Mustafa, Ali Mohammed Suleman, Hussein Computer Science Includes abstract. Includes bibliographical references. This thesis attempts to address the problem of mixed querying in CLIR. It proposes mixed-language (language-aware) approaches in which mixed queries are used to retrieve most relevant documents, regardless of their languages. To achieve this goal, however, it is essential firstly to suppress the impact of most problems that are caused by the mixed-language feature in both queries and documents and which result in biasing the final ranked list. Therefore, a cross-lingual re-weighting model was developed. In this cross-lingual model, term frequency, document frequency and document length components in mixed queries are estimated and adjusted, regardless of languages, while at the same time the model considers the unique mixed-language features in queries and documents, such as co-occurring terms in two different languages. Furthermore, in mixed queries, non-technical terms (mostly those in non-English language) would likely overweight and skew the impact of those technical terms (mostly those in English) due to high document frequencies (and thus low weights) of the latter terms in their corresponding collection (mostly the English collection). Such phenomenon is caused by the dominance of the English language in scientific domains. Accordingly, this thesis also proposes reasonable re-weighted Inverse Document Frequency (IDF) so as to moderate the effect of overweighted terms in mixed queries. 2014-08-13T19:31:35Z 2014-08-13T19:31:35Z 2013 Doctoral Thesis Doctoral PhD http://hdl.handle.net/11427/6421 eng application/pdf Department of Computer Science Faculty of Science University of Cape Town |
| spellingShingle | Computer Science Mustafa, Ali Mohammed Mixed-Language Arabic- English Information Retrieval |
| thesis_degree_str | Doctoral |
| title | Mixed-Language Arabic- English Information Retrieval |
| title_full | Mixed-Language Arabic- English Information Retrieval |
| title_fullStr | Mixed-Language Arabic- English Information Retrieval |
| title_full_unstemmed | Mixed-Language Arabic- English Information Retrieval |
| title_short | Mixed-Language Arabic- English Information Retrieval |
| title_sort | mixed language arabic english information retrieval |
| topic | Computer Science |
| url | http://hdl.handle.net/11427/6421 |
| work_keys_str_mv | AT mustafaalimohammed mixedlanguagearabicenglishinformationretrieval |