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Abduelbaset Goweder

dblp:37/5007 · DBLP profile ↗
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2ranked-venue papers
2as first author
0since 2021 · last 2004
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-authorDatabases, data management, data science and information retrieval · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Information retrieval · 100%
Artificial intelligence
1 paper
Information extraction and text analysis · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Information extraction and text analysis
arabic text processing
0.012004
Identifying Broken Plurals in Unvowelised Arabic Tex · EMNLP 2004
Natural language and speech › Information extraction and text analysis
morphological analysis
0.012004
Identifying Broken Plurals in Unvowelised Arabic Tex · EMNLP 2004
Information retrieval › cross-language information retrieval
arabic retrieval
0.012004
Broken plural detection for arabic information retrieval · SIGIR 2004
Information retrieval
cross-language information retrieval
0.012004
Broken plural detection for arabic information retrieval · SIGIR 2004
Information retrieval › indexing
stemming
0.012004
Broken plural detection for arabic information retrieval · SIGIR 2004
YearPublicationVenuePosition
2004 Identifying Broken Plurals in Unvowelised Arabic Tex
Abduelbaset Goweder, Massimo Poesio, Anne N. De Roeck, Jeff Reynolds
EMNLP1
2004 Broken plural detection for arabic information retrieval
abstract
Due to the high number of inflectional variations of Arabic words, empirical results suggest that stemming is essential for Arabic information retrieval. However, current light stemming algorithms do not extract the correct stem of irregular (so-called broken) plurals, which constitute ~10% of Arabic texts and ~41% of plurals. Although light stemming in particular has led to improvements in information retrieval [5, 6], the effects of broken plurals on the performance of information retrieval systems has not been examined.We propose a light stemmer that incorporates a broken plural recognition component, and evaluate it within the context of information retrieval. Our results show that identifying broken plurals and reducing them to their correct stems does result in a significant improvement in the performance of information retrieval systems.
Abduelbaset Goweder, Massimo Poesio, Anne N. De Roeck
SIGIR1