Michael Kaisser

Also published as: Michael Kaißer


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Answer Sentence Retrieval by Matching Dependency Paths acquired from Question/Answer Sentence Pairs
Michael Kaisser
Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics


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Creating a Research Collection of Question Answer Sentence Pairs with Amazon’s Mechanical Turk
Michael Kaisser | John Lowe
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

Each year NIST releases a set of question, document id, answer-triples for the factoid questions used in the TREC Question Answering track. While this resource is widely used and proved itself useful for many purposes, it also is too coarse a grain-size for a lot of other purposes. In this paper we describe how we have used Amazon’s Mechanical Turk to have multiple subjects read the documents and identify the sentences themselves which contain the answer. For most of the 1911 questions in the test sets from 2002 to 2006 and each of the documents said to contain an answer, the Question-Answer Sentence Pairs (QASP) corpus introduced in this paper contains the identified answer sentences. We believe that this corpus, which we will make available to the public, can further stimulate research in QA, especially linguistically motivated research, where matching the question to the answer sentence by either syntactic or semantic means is a central concern.

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Improving Search Results Quality by Customizing Summary Lengths
Michael Kaisser | Marti A. Hearst | John B. Lowe
Proceedings of ACL-08: HLT

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The QuALiM Question Answering Demo: Supplementing Answers with Paragraphs drawn from Wikipedia
Michael Kaisser
Proceedings of the ACL-08: HLT Demo Session


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Question Answering based on Semantic Roles
Michael Kaisser | Bonnie Webber
ACL 2007 Workshop on Deep Linguistic Processing


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The SAMMIE Corpus of Multimodal Dialogues with an MP3 Player
Ivana Kruijff-Korbayová | Tilman Becker | Nate Blaylock | Ciprian Gerstenberger | Michael Kaißer | Peter Poller | Verena Rieser | Jan Schehl
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

We describe a corpus of multimodal dialogues with an MP3player collected in Wizard-of-Oz experiments and annotated with a richfeature set at several layers. We are using the Nite XML Toolkit (NXT) to represent and further process the data. We designed an NXTdata model, converted experiment log file data and manualtranscriptions into NXT, and are building tools for additionalannotation using NXT libraries. The annotated corpus will be used to (i) investigate various aspects of multimodal presentation andinteraction strategies both within and across annotation layers; (ii) design an initial policy for reinforcement learning of multimodalclarification requests.


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An Experiment Setup for Collecting Data for Adaptive Output Planning in a Multimodal Dialogue System
Ivana Kruijff-Korbayová | Nate Blaylock | Ciprian Gerstenberger | Verena Rieser | Tilman Becker | Michael Kaisser | Peter Poller | Jan Schehl
Proceedings of the Tenth European Workshop on Natural Language Generation (ENLG-05)