Combining Statistical and Syntactical Systems for Spoken Language Understanding with Graphical Models

被引：0

作者：

Schwaerzler, S. ^{[1
]}

Geiger, J. ^{[1
]}

Schenk, J. ^{[1
]}

Al-Hames, M. ^{[1
]}

Hoernler, B. ^{[1
]}

Ruske, G. ^{[1
]}

Rigoll, G. ^{[1
]}

机构：

[1] Tech Univ Munich, Inst Human Machine Commun, D-80290 Munich, Germany

来源：

INTERSPEECH 2008: 9TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2008, VOLS 1-5 | 2008年

关键词：

natural language understanding; machine learning; graphical models;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

There are two basic approaches for semantic processing in spoken language understanding: a rule based approach and a statistic approach. In this paper we combine both of them in a novel way by using statistical and syntactical dynamic bayesian networks (DBNs) together with Graphical Models (GMs) for spoken language understanding (SLU). GMs merge in a complex, mathematical way probability with graph theory. This results in four different setups which raise in their complexity. Comparing our results to a baseline system we achieve a F1-measure of 93.7% in word classes and 95.7% in concepts for our best setup in the ATIS-Task. This outperforms the baseline system relatively by 3.7% in word classes and by 8.2% in concepts. The expermiments were performend with the graphical model toolkit (GMTK).

引用

页码：1590 / 1593

页数：4

共 50 条

[21] Adaptive language models for spoken dialogue systems
Solsona, RA
Fosler-Lussier, E
Kuo, HKJ
Potamianos, A
Zitouni, I
2002 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS I-IV, PROCEEDINGS, 2002, : 37 - 40
[22] Combining active and semi-supervised learning for spoken language understanding
Tur, G
Hakkani-Tür, D
Schapire, RE
SPEECH COMMUNICATION, 2005, 45 (02) : 171 - 186
[23] Initial language models for spoken dialogue systems
Kellner, A
PROCEEDINGS OF THE 1998 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, VOLS 1-6, 1998, : 185 - 188
[24] Deep Contextual Language Understanding in Spoken Dialogue Systems
Liu, Chunxi
Xu, Puyang
Sarikaya, Ruhi
16TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2015), VOLS 1-5, 2015, : 120 - 124
[25] Extracting clauses for spoken language understanding in conversational systems
Gupta, NK
Bangalore, S
PROCEEDINGS OF THE 2002 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING, 2002, : 273 - 280
[26] FAST INTENT CLASSIFICATION FOR SPOKEN LANGUAGE UNDERSTANDING SYSTEMS
Tyagi, Akshit
Sharma, Varun
Gupta, Rahul
Samson, Lynn
Zhuang, Nan
Wang, Zihang
Campbell, Bill
2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, 2020, : 8119 - 8123
[27] Spoken language understanding
Wang, YY
Deng, L
Acero, A
IEEE SIGNAL PROCESSING MAGAZINE, 2005, 22 (05) : 16 - 31
[28] UNDERSTANDING SPOKEN LANGUAGE
BROWN, G
TESOL QUARTERLY, 1978, 12 (03) : 271 - 283
[29] Accurate statistical Spoken Language Understanding from limited development resources
Meza-Ruiz, Ivan V.
Riedel, Sebastian
Lemon, Oliver
2008 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, VOLS 1-12, 2008, : 5021 - 5024
[30] ON-LINE ADAPTATION OF SEMANTIC MODELS FOR SPOKEN LANGUAGE UNDERSTANDING
Bayer, Ali Orkan
Riccardi, Giuseppe
2013 IEEE WORKSHOP ON AUTOMATIC SPEECH RECOGNITION AND UNDERSTANDING (ASRU), 2013, : 90 - 95

← 1 2 3 4 5 →