NDLI: Different word representations and their combination for proper name retrieval from diachronic documents

Content Provider	IEEE Xplore Digital Library
Author	Illina, Irina Fohr, Dominique
Copyright Year	2015
Description	Author affiliation: MultiSpeech team, Université de Lorraine, LORIA, UMR 7503, Vandoeuvre-lès-Nancy, F-54506, France, Inria, Villers-lès-Nancy, F-54600, France CNRS, LORIA, UMR 7503, Vandoeuvre-lès-Nancy, F-54506, France (Illina, Irina; Fohr, Dominique)
Abstract	This paper deals with the problem of high-quality transcription systems for very large vocabulary automatic speech recognition (ASR). We investigate the problem of automatic retrieval of out-of-vocabulary (OOV) proper names (PNs). We want to take into account the temporal, syntactic and semantic context of words. Nowadays, Artificial Neural Networks (NN) are widely used in natural language processing: continuous space representations of words is learned automatically from unstructured text data. To model the latent topics at document level, Latent Dirichlet Allocation (LDA) has been successful. In this paper, we propose OOV PN retrieval using (1) temporal versus topic context modeling; (2) different word representation spaces for word-level and document-level context modeling; (3) combinations of retrieval results. Experimental evaluation on broadcast news data shows that the proposed method combinations lead to better results. This confirms the complementarity of methods.
Starting Page	1
Ending Page	7
File Size	127951
Page Count	7
File Format	PDF
e-ISBN	9781479972913
DOI	10.1109/ASRU.2015.7404766
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-12-13
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vocabulary Semantics Context Context modeling Artificial neural networks Measurement proper names speech recognition neural networks LDA vocabulary extension out-of-vocabulary words
Content Type	Text
Resource Type	Article

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