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- 613 creator guenther-palm.
- 613 creator harald-traue.
- 613 creator heiko-neumann.
- 613 creator holger-hoffmann.
- 613 creator petra-maria-strauss.
- 613 creator stefan-scherer.
- 613 creator ulrich-weidenbacher.
- 613 creator wolfgang-minker.
- 613 type InProceedings.
- 613 label "The PIT Corpus of German Multi-Party Dialogues".
- 613 sameAs 613.
- 613 abstract "The PIT corpus is a German multi-media corpus of multi-party dialogues recorded in a Wizard-of-Oz environment at the University of Ulm. The scenario involves two human dialogue partners interacting with a multi-modal dialogue system in the domain of restaurant selection. In this paper we present the characteristics of the data which was recorded in three sessions resulting in a total of 75 dialogues and about 14 hours of audio and video data. The corpus is available at http://www.uni-ulm.de/in/pit.".
- 613 hasAuthorList authorList.
- 613 hasTopic Linguistics.
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- 613 keyword "Corpus (creation, annotation, etc.)".
- 613 keyword "Dialogue & Natural Interactivity".
- 613 keyword "Multimedia annotation and processing".
- 613 title "The PIT Corpus of German Multi-Party Dialogues".
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- 614 creator feiyu-xu.
- 614 creator hans-uszkoreit.
- 614 creator hong-li.
- 614 creator niko-felger.
- 614 type InProceedings.
- 614 label "Adaptation of Relation Extraction Rules to New Domains".
- 614 sameAs 614.
- 614 abstract "This paper presents various strategies for improving the extraction performance of less prominent relations with the help of the rules learned for similar relations, for which large volumes of data are available that exhibit suitable data properties. The rules are learned via a minimally supervised machine learning system for relation extraction called DARE. Starting from semantic seeds, DARE extracts linguistic grammar rules associated with semantic roles from parsed news texts. The performance analysis with respect to different experiment domains shows that the data property plays an important role for DARE. Especially the redundancy of the data and the connectivity of instances and pattern rules have a strong influence on recall. However, most real-world data sets do not possess the desirable small-world property. Therefore, we propose three scenarios to overcome the data property problem of some domains by exploiting a similar domain with better data properties. The first two strategies stay with the same corpus but try to extract new similar relations with learned rules. The third strategy adapts the learned rules to a new corpus. All three strategies show that frequently mentioned relations can help in the detection of less frequent relations.".
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- 614 title "Adaptation of Relation Extraction Rules to New Domains".
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