Fixed versus dynamic co-occurrence windows in TextRank term weights for information retrieval
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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Fixed versus dynamic co-occurrence windows in TextRank term weights for information retrieval. / Lu, Wei ; Cheng, Qikai; Lioma, Christina.
Proceedings of the 35th international ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, 2012. s. 1079-1080.Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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TY - GEN
T1 - Fixed versus dynamic co-occurrence windows in TextRank term weights for information retrieval
AU - Lu, Wei
AU - Cheng, Qikai
AU - Lioma, Christina
N1 - Conference code: 35
PY - 2012
Y1 - 2012
N2 - TextRank is a variant of PageRank typically used in graphs that represent documents, and where vertices denote terms and edges denote relations between terms. Quite often the relation between terms is simple term co-occurrence within a fixed window of k terms. The output of TextRank when applied iteratively is a score for each vertex, i.e. a term weight, that can be used for information retrieval (IR) just like conventional term frequency based term weights. So far, when computing TextRank term weights over co-occurrence graphs, the window of term co-occurrence is always fixed. This work departs from this, and considers dynamically adjusted windows of term co-occurrence that follow the document structure on a sentence- and paragraph-level. The resulting TextRank term weights are used in a ranking function that re-ranks 1000 initially returned search results in order to improve the precision of the ranking. Experiments with two IR collections show that adjusting the vicinity of term co-occurrence when computing TextRank term weights can lead to gains in early precision.
AB - TextRank is a variant of PageRank typically used in graphs that represent documents, and where vertices denote terms and edges denote relations between terms. Quite often the relation between terms is simple term co-occurrence within a fixed window of k terms. The output of TextRank when applied iteratively is a score for each vertex, i.e. a term weight, that can be used for information retrieval (IR) just like conventional term frequency based term weights. So far, when computing TextRank term weights over co-occurrence graphs, the window of term co-occurrence is always fixed. This work departs from this, and considers dynamically adjusted windows of term co-occurrence that follow the document structure on a sentence- and paragraph-level. The resulting TextRank term weights are used in a ranking function that re-ranks 1000 initially returned search results in order to improve the precision of the ranking. Experiments with two IR collections show that adjusting the vicinity of term co-occurrence when computing TextRank term weights can lead to gains in early precision.
U2 - 10.1145/2348283.2348478
DO - 10.1145/2348283.2348478
M3 - Article in proceedings
SP - 1079
EP - 1080
BT - Proceedings of the 35th international ACM SIGIR Conference on Research and Development in Information Retrieval
PB - Association for Computing Machinery
T2 - 35th International ACM SIGIR Conference on Research and Development in Information Retrieval
Y2 - 12 August 2012 through 16 August 2012
ER -
ID: 38239990