Detecting and extracting named entities from spontaneous speech in a mixed-initiative spoken dialogue context: How May I Help You?sm,tm
| Autor: | Frédéric Béchet, Allen Louis Gorin, Dilek Hakkani Tür, Jeremy Huntley Wright |
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| Rok vydání: | 2004 |
| Předmět: |
Linguistics and Language
Computer science business.industry Process (engineering) Communication Speech recognition Feature extraction Context (language use) computer.software_genre Language and Linguistics Computer Science Applications Information extraction Rule-based machine translation Application domain Modeling and Simulation Subject (grammar) Computer Vision and Pattern Recognition Artificial intelligence business computer Software Word (computer architecture) Natural language processing |
| Zdroj: | Speech Communication. 42:207-225 |
| ISSN: | 0167-6393 |
| DOI: | 10.1016/j.specom.2003.07.003 |
| Popis: | The understanding module of a spoken dialogue system must extract, from the speech recognizer output, the kind of request expressed by the caller (the call type) and its parameters (numerical expressions, time expressions or propernames). Such expressions are called Named Entities and their definitions can be either generic or linked to the dialogue application domain. Detecting and extracting such Named Entities within a mixed-initiative dialogue context like How May I Help You? sm;tm (HMIHY) is the subject of this study. After reviewing standard methods based on hand-written grammars and statistical tagging, we propose a new approach, combining the advantages of both in a 2-step process. We also propose a novel architecture which exploits understanding to improve recognition accuracy: the output of the Automatic Speech Recognition module is now a word lattice and the understanding module is responsible for transcribing the word strings which are useful to the Dialogue Manager. All the methods proposed are trained and evaluated on a corpus comprising utterances from live customer traffic. � 2003 Elsevier B.V. All rights reserved. |
| Databáze: | OpenAIRE |
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