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- aggregation classification "C1".
- aggregation creator person.
- aggregation creator person.
- aggregation creator person.
- aggregation creator person.
- aggregation date "2008".
- aggregation format "application/pdf".
- aggregation hasFormat 624022.bibtex.
- aggregation hasFormat 624022.csv.
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- aggregation isPartOf urn:isbn:9781605582511.
- aggregation language "eng".
- aggregation publisher "Association for Computing Machinery (ACM)".
- aggregation rights "I have transferred the copyright for this publication to the publisher".
- aggregation subject "Biology and Life Sciences".
- aggregation title "The role of syntactic features in protein interaction extraction".
- aggregation abstract "Most approaches for protein interaction mining from biomedical texts use both lexical and syntactic features. However, the individual impact of these two kinds of features on the effectiveness of the mining process has not yet been thoroughly studied. In this paper, we perform such a study on a recently published state of the art support vector machine approach that uses both lexical and syntactic features. To this end, we strip this approach down to an algorithm that uses only a subset of the initial syntactic features. Next, we compare the original and the stripped-down method by evaluating them on 5 benchmark datasets as well as by performing 5 additional cross-dataset experiments. Although the original method exploits a very rich feature set including words, parts-of-speech and grammatical relations, it is not significantly better than the stripped-down version; in fact, the former does not even consistently outperform the latter.".
- aggregation authorList BK175182.
- aggregation endPage "68".
- aggregation startPage "61".
- aggregation aggregates 627099.
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- aggregation similarTo 1458449.1458463.
- aggregation similarTo LU-624022.