Referential semantic language modeling for data-poor domains

Author

Wu, Stephen ; Schwartz, Lane ; Schuler, William

Author_Institution

Dept. of Comput. Sci. & Eng., Minnesota Univ., Minneapolis, MN

fYear

2008

fDate

March 31 2008-April 4 2008

Firstpage

5085

Lastpage

5088

Abstract

This paper describes a referential semantic language model that achieves accurate recognition in user-defined domains with no available domain-specific training corpora. This model is interesting in that, unlike similar recent systems, it exploits context dynamically, using incremental processing and limited stack memory of an HMM-like time series model to constrain search.

Keywords

natural language interfaces; programming language semantics; speech processing; speech recognition; time series; HMM-like time series model; data-poor domains; hidden Markov model; referential semantic language modeling; user-defined domains; Artificial intelligence; Computer science; Context modeling; Data engineering; Decoding; Hidden Markov models; Information resources; Natural languages; Speech recognition; Viterbi algorithm; Artificial intelligence; Natural language interfaces; Speech recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on

Conference_Location

Las Vegas, NV

ISSN

1520-6149

Print_ISBN

978-1-4244-1483-3

Electronic_ISBN

1520-6149

Type

conf

DOI

10.1109/ICASSP.2008.4518802

Filename

4518802

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3427486