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(Schlagwörter: "Abstract")
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DKPro keyphrases. Flexible and reusable keyphrase extraction experiments
Erbs, Nicolai; Bispo Santos, Pedro; Gurevych, Iryna; Zesch, Torsten
Book Chapter
| Aus: Bontcheva, Kalina; Jingbo, Zhu (Hrsg.): Proceedings of COLING 2014: System demonstrations | Stroudsburg; PA: Association for Computational Linguistics | 2014
34722 Endnote
Author(s):
Erbs, Nicolai; Bispo Santos, Pedro; Gurevych, Iryna; Zesch, Torsten
Title:
DKPro keyphrases. Flexible and reusable keyphrase extraction experiments
In:
Bontcheva, Kalina; Jingbo, Zhu (Hrsg.): Proceedings of COLING 2014: System demonstrations, Stroudsburg; PA: Association for Computational Linguistics, 2014 , S. 31-36
URL:
http://www.aclweb.org/anthology/P/P14/P14-5006.pdf
Publication Type:
4. Beiträge in Sammelbänden; Tagungsband/Konferenzbeitrag/Proceedings
Language:
Englisch
Keywords:
Abstract; Automatisierung; Computerlinguistik; Inhaltserschließung; Text; Textanalyse; Tool
Abstract:
DKPro Keyphrases is a keyphrase extraction framework based on UIMA. It offers a wide range of state-of-the-art keyphrase experiments approaches. At the same time, it is a workbench for developing new extraction approaches and evaluating their impact. DKPro Keyphrases is publicly available under an open-source license. (DIPF/Orig.)
DIPF-Departments:
Informationszentrum Bildung
Learning to summarise related sentences
Tzouridis, Emmanouil; Nasir, Jamal A.; Brefeld, Ulf
Book Chapter
| Aus: Association for Computational Linguistics (Hrsg.): Proceedings of COLING 2014: Technical papers | Stroudsburg; PA: Association for Computational Linguistics | 2014
35008 Endnote
Author(s):
Tzouridis, Emmanouil; Nasir, Jamal A.; Brefeld, Ulf
Title:
Learning to summarise related sentences
In:
Association for Computational Linguistics (Hrsg.): Proceedings of COLING 2014: Technical papers, Stroudsburg; PA: Association for Computational Linguistics, 2014 , S. 1636-1647
URL:
http://www.aclweb.org/anthology/C14-1155
Publication Type:
4. Beiträge in Sammelbänden; Tagungsband/Konferenzbeitrag/Proceedings
Language:
Englisch
Keywords:
Abstract; Algorithmus; Automatisierung; Computerlinguistik; Datenverarbeitung; Semantik; Text
Abstract:
We cast multi-sentence compression as a structured prediction problem. Related sentences are represented by a word graph so that summaries constitute paths in the graph (Filippova, 2010). We devise a parameterised shortest path algorithm that can be written as a generalised linear model in a joint space of word graphs and compressions. We use a large-margin approach to adapt parameterised edge weights to the data such that the shortest path is identical to the desired summary. Decoding during training is performed in polynomial time using loss augmented inference. Empirically, we compare our approach to the state-of-the-art in graph-based multi-sentence compression and observe significant improvements of about 7% in ROUGE F-measure and 8% in BLEU score, respectively. (DIPF/Orig.)
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