| 1. |
- Alemu Argaw, Atelach
(författare)
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Amharic-English information retrieval with pseudo relevance feedback
- 2008
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Ingår i: Advances in multilingual and multimodal information retrieval. - Berlin ; New York : Springer. - 9783540857594 ; , s. 119-126
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- We describe cross language retrieval experiments using Amharic queries and English language document collection from our participation in the bilingual ad hoc track at the CLEF 2007. Two monolingual and eight bilingual runs were submitted. The bilingual experiments designed varied in terms of usage of long and short queries, presence of pseudo relevance feedback (PRF), and three approaches (maximal expansion, first-translation-given, manual) for word sense disambiguation. We used an Amharic-English machine readable dictionary (MRD) and an online Amharic-English dictionary in order to do the lookup translation of query terms. In utilizing both resources, matching query term bigrams were always given precedence over unigrams. Out of dictionary Amharic query terms were taken to be possible named entities in the language, and further filtering was attained through restricted fuzzy matching based on edit distance. The fuzzy matching was performed for each of these terms against automatically extracted English proper names. The Lemur toolkit for language modeling and information retrieval was used for indexing and retrieval. Although the experiments are too limited to draw conclusions from, the obtained results indicate that longer queries tend to perform similar to short ones, PRF improves performance considerably, and that queries tend to fare better when we use the first translation given in the MRD rather than using maximal expansion of terms by taking all the translations given in the MRD.
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| 2. |
- Alemu Argaw, Atelach, et al.
(författare)
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General-Purpose Text Categorization Applied to the Medical Domain.
- 2007
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- This paper presents work where a general-purpose text categorization method was applied to categorize medical free-texts. The purpose of the experiments was to examine how such a method performs without any domain-specific knowledge, hand-crafting or tuning. Additionally, we compare the results from the general-purpose method with results from runs in which a medical thesaurus as well as automatically extracted keywords were used when building the classifiers. We show that standard text categorization techniques using stemmed unigrams as the basis for learning can be applied directly to categorize medical reports, yielding an F-measure of 83.9, and outperforming the more sophisticated methods.
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| 3. |
- Alemu Argaw, Atelach
(författare)
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Ranked Terms List for Computer Assisted Translation
- 2008
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Ingår i: Lesser Used Languages and Computer Linguistics (LULCL) II Combining efforts to foster computational support of minority languages.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)
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| 4. |
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| 5. |
- Argaw, Atelach Alemu, et al.
(författare)
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Amharic-English information retrieval
- 2006
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Ingår i: CLEF2006 Working Notes. - : CEUR-WS.
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Konferensbidrag (refereegranskat)abstract
- We describe Amharic-English cross lingual information retrieval experiments in the adhoc bilingual tracs of the CLEF 2006. The query analysis is supported by morphological analysis and part of speech tagging while we used different machine readable dictionaries for term lookup in the translation process. Out of dictionary terms were handled using fuzzy matching and Lucene[4] was used for indexing and searching. Four experiments that differed in terms of utilized fields in the topic set, fuzzy matching, and term weighting, were conducted. The results obtained are reported and discussed.
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| 6. |
- Argaw, Atelach Alemu, et al.
(författare)
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Amharic-english information retrieval
- 2007
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Ingår i: Evaluation of Multilingual and Multi-modal Information Retrieval. - 9783540749981 ; , s. 43-50
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Konferensbidrag (refereegranskat)abstract
- We describe Amharic-English cross lingual information retrieval experiments in the ad hoc bilingual tracks of the CLEF 2006. The query analysis is supported by morphological analysis and part of speech tagging while we used two machine readable dictionaries supplemented by online dictionaries for term lookup in the translation process. Out of dictionary terms were handled using fuzzy matching and Lucene[4] was used for indexing and searching. Four experiments that differed in terms of utilized fields in the topic set, fuzzy matching, and term weighting, were conducted. The results obtained are reported and discussed.
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| 7. |
- Argaw, Atelach Alemu
(författare)
-
Amharic-English information retrieval with pseudo relevance feedback
- 2007
-
Ingår i: CLEF2007 Working Notes. - : CEUR-WS.
-
Konferensbidrag (refereegranskat)abstract
- We describe cross language retrieval experiments using Amharic queries and English language document collection from our participation in the bilingual ad hoc track at the CLEF 2007. Two monolingual and eight bilingual runs were submitted. The bilingual experiments designed varied in terms of usage of long and short queries, presence of pseudo relevance feedback (PRF), and three approaches (maximal expansion, first-translation-given, manual) for word sense disambiguation. We used an Amharic-English machine readable dictionary (MRD) and an online Amharic-English dictionary in order to do the lookup translation of query terms. In utilizing both resources, matching query term bigrams were always given precedence over unigrams. Out of dictionary Amharic query terms were taken to be possible named entities in the language, and further filtering was attained through restricted fuzzy matching based on edit distance. The fuzzy matching was performed for each of these terms against automatically extracted English proper names. The Lemur toolkit for language modeling and information retrieval was used for indexing and retrieval. Although the experiments are too limited to draw conclusions from, the obtained results indicate that longer queries tend to perform similar to short ones, PRF improves performance considerably, and that queries tend to fare better when we use the first translation given in the MRD rather than using maximal expansion of terms by taking all the translations given in the MRD.
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| 8. |
- Argaw, Atelach Alemu
(författare)
-
Amharic-English Information Retrieval with Pseudo Relevance Feedback
- 2008
-
Ingår i: Advances In Multilingual And Multimodal Information Retrieval. - 9783540857594 ; , s. 119-126
-
Konferensbidrag (refereegranskat)abstract
- We describe cross language retrieval experiments using Amharic queries and English language d ocument collection. Two monolingual and eight bilingual runs were submitted with variations in terms of 1 sage of long and short queries, presence of pseudo relevance feedback (PRF), and approaches for word sense disambiguation (WSD). We used an Amharic-English machine readable dictionary (MRD), and an online Amharic-English dictionary for lookup translation of query terms. Out of dictionary Amharic query terms were considered as possible named entities, and further filtering was attained through restricted fuzzy matching based on edit distance which is calculated against automatically extracted English proper names. The obtained results indicate that longer queries tend to perform similar to short ones, PRF improves performance considerably, and that queries tend to fare better with WSD rather than using maximal expansion of terms by taking all the translations given in the MRD.
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| 9. |
- Argaw, Atelach Alemu, et al.
(författare)
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Dictionary-based amharic - English information retrieval
- 2005
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Ingår i: MULTILINGUAL INFORMATION ACCESS FOR TEXT, SPEECH AND IMAGES. - BERLIN : SPRINGER-VERLAG BERLIN. - 3540274200 ; , s. 143-149
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Konferensbidrag (refereegranskat)abstract
- We present two approaches to the Amharic - English bilingual track in CLEF 2004. Both experiments use a dictionary based approach to translate the Amharic queries into English Bags-of-words, but while one approach removes non-content bearing words from the Amharic queries based on their IDF value, the other uses a list of English stop words to perform the same task. The resulting translated (English) terms are then submitted to a retrieval engine that supports the Boolean and vector-space models. In our experiments, the second approach (based on a list of English stop words) performs slightly better than the one based on IDF values for the Amharic terms.
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| 10. |
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