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Spoken Language Understanding (SLP-UNDE)

Semantic Mapping of Natural Language Input to Database Entries via Convolutional Neural Networks


Natural language processing research has made major advances with the concept of representing words, sentences, paragraphs, and even documents by embedded vector representations. We apply this idea to the problem of relating foods, as expressed in natural language meal descriptions, to corresponding database entries. We generate fixed-length embeddings for U.S.

icassp_17.pdf

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Authors:
Zachary Collins, Jim Glass
Submitted On:
3 March 2017 - 12:34pm
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icassp_17.pdf

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[1] Zachary Collins, Jim Glass, "Semantic Mapping of Natural Language Input to Database Entries via Convolutional Neural Networks", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1616. Accessed: Apr. 30, 2017.
@article{1616-17,
url = {http://sigport.org/1616},
author = {Zachary Collins; Jim Glass },
publisher = {IEEE SigPort},
title = {Semantic Mapping of Natural Language Input to Database Entries via Convolutional Neural Networks},
year = {2017} }
TY - EJOUR
T1 - Semantic Mapping of Natural Language Input to Database Entries via Convolutional Neural Networks
AU - Zachary Collins; Jim Glass
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1616
ER -
Zachary Collins, Jim Glass. (2017). Semantic Mapping of Natural Language Input to Database Entries via Convolutional Neural Networks. IEEE SigPort. http://sigport.org/1616
Zachary Collins, Jim Glass, 2017. Semantic Mapping of Natural Language Input to Database Entries via Convolutional Neural Networks. Available at: http://sigport.org/1616.
Zachary Collins, Jim Glass. (2017). "Semantic Mapping of Natural Language Input to Database Entries via Convolutional Neural Networks." Web.
1. Zachary Collins, Jim Glass. Semantic Mapping of Natural Language Input to Database Entries via Convolutional Neural Networks [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1616

THE SHEFFIELD SEARCH AND RESCUE CORPUS


As part of an ongoing research into extracting mission-critical information from Search and Rescue speech communications, a corpus of unscripted, goal-oriented, two-party spoken conversations has been designed and collected. The Sheffield Search and Rescue (SSAR) corpus comprises about 12 hours of data from 96 conversations by 24 native speakers of British English with a southern accent. Each conversation is about a collaborative task of exploring and estimating a simulated indoor environment.

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Authors:
Saeid Mokaram, Roger K. Moore
Submitted On:
28 February 2017 - 5:01am
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Poster: THE SHEFFIELD SEARCH AND RESCUE CORPUS

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[1] Saeid Mokaram, Roger K. Moore, "THE SHEFFIELD SEARCH AND RESCUE CORPUS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1494. Accessed: Apr. 30, 2017.
@article{1494-17,
url = {http://sigport.org/1494},
author = {Saeid Mokaram; Roger K. Moore },
publisher = {IEEE SigPort},
title = {THE SHEFFIELD SEARCH AND RESCUE CORPUS},
year = {2017} }
TY - EJOUR
T1 - THE SHEFFIELD SEARCH AND RESCUE CORPUS
AU - Saeid Mokaram; Roger K. Moore
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1494
ER -
Saeid Mokaram, Roger K. Moore. (2017). THE SHEFFIELD SEARCH AND RESCUE CORPUS. IEEE SigPort. http://sigport.org/1494
Saeid Mokaram, Roger K. Moore, 2017. THE SHEFFIELD SEARCH AND RESCUE CORPUS. Available at: http://sigport.org/1494.
Saeid Mokaram, Roger K. Moore. (2017). "THE SHEFFIELD SEARCH AND RESCUE CORPUS." Web.
1. Saeid Mokaram, Roger K. Moore. THE SHEFFIELD SEARCH AND RESCUE CORPUS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1494

EEG Evidence for a Three-Phase Recurrent Process during Spoken Word Processing

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Authors:
Bin Zhao, Jianwu Dang, Gaoyan Zhang
Submitted On:
15 October 2016 - 10:03am
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ISCSLP 2016_Bin Zhao.pdf

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[1] Bin Zhao, Jianwu Dang, Gaoyan Zhang, "EEG Evidence for a Three-Phase Recurrent Process during Spoken Word Processing", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1236. Accessed: Apr. 30, 2017.
@article{1236-16,
url = {http://sigport.org/1236},
author = {Bin Zhao; Jianwu Dang; Gaoyan Zhang },
publisher = {IEEE SigPort},
title = {EEG Evidence for a Three-Phase Recurrent Process during Spoken Word Processing},
year = {2016} }
TY - EJOUR
T1 - EEG Evidence for a Three-Phase Recurrent Process during Spoken Word Processing
AU - Bin Zhao; Jianwu Dang; Gaoyan Zhang
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1236
ER -
Bin Zhao, Jianwu Dang, Gaoyan Zhang. (2016). EEG Evidence for a Three-Phase Recurrent Process during Spoken Word Processing. IEEE SigPort. http://sigport.org/1236
Bin Zhao, Jianwu Dang, Gaoyan Zhang, 2016. EEG Evidence for a Three-Phase Recurrent Process during Spoken Word Processing. Available at: http://sigport.org/1236.
Bin Zhao, Jianwu Dang, Gaoyan Zhang. (2016). "EEG Evidence for a Three-Phase Recurrent Process during Spoken Word Processing." Web.
1. Bin Zhao, Jianwu Dang, Gaoyan Zhang. EEG Evidence for a Three-Phase Recurrent Process during Spoken Word Processing [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1236

Dialog State Tracking for Interview Coaching Using Two-Level LSTM


This study presents an approach to dialog state tracking (DST) in an interview conversation by using the long short-term memory (LSTM) and artificial neural network (ANN). First, the techniques of word embedding are employed for word representation by using the word2vec model. Then, each input sentence is represented by a sentence hidden vector using the LSTM-based sentence model. The sentence hidden vectors for each sentence are fed to the LSTM-based answer model to map the interviewee’s answer to an answer hidden vector.

Paper Details

Authors:
Ming-Hsiang Su, Kun-Yi Huang, Tsung-Hsien Yang, and Tsui-Ching Huang
Submitted On:
14 October 2016 - 11:52pm
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ISCSLP-2016-1012-MH.pdf

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[1] Ming-Hsiang Su, Kun-Yi Huang, Tsung-Hsien Yang, and Tsui-Ching Huang, "Dialog State Tracking for Interview Coaching Using Two-Level LSTM", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1215. Accessed: Apr. 30, 2017.
@article{1215-16,
url = {http://sigport.org/1215},
author = {Ming-Hsiang Su; Kun-Yi Huang; Tsung-Hsien Yang; and Tsui-Ching Huang },
publisher = {IEEE SigPort},
title = {Dialog State Tracking for Interview Coaching Using Two-Level LSTM},
year = {2016} }
TY - EJOUR
T1 - Dialog State Tracking for Interview Coaching Using Two-Level LSTM
AU - Ming-Hsiang Su; Kun-Yi Huang; Tsung-Hsien Yang; and Tsui-Ching Huang
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1215
ER -
Ming-Hsiang Su, Kun-Yi Huang, Tsung-Hsien Yang, and Tsui-Ching Huang. (2016). Dialog State Tracking for Interview Coaching Using Two-Level LSTM. IEEE SigPort. http://sigport.org/1215
Ming-Hsiang Su, Kun-Yi Huang, Tsung-Hsien Yang, and Tsui-Ching Huang, 2016. Dialog State Tracking for Interview Coaching Using Two-Level LSTM. Available at: http://sigport.org/1215.
Ming-Hsiang Su, Kun-Yi Huang, Tsung-Hsien Yang, and Tsui-Ching Huang. (2016). "Dialog State Tracking for Interview Coaching Using Two-Level LSTM." Web.
1. Ming-Hsiang Su, Kun-Yi Huang, Tsung-Hsien Yang, and Tsui-Ching Huang. Dialog State Tracking for Interview Coaching Using Two-Level LSTM [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1215

Dialog State Tracking for Interview Coaching Using Two-Level LSTM


This study presents an approach to dialog state tracking (DST) in an interview conversation by using the long short-term memory (LSTM) and artificial neural network (ANN). First, the techniques of word embedding are employed for word representation by using the word2vec model. Then, each input sentence is represented by a sentence hidden vector using the LSTM-based sentence model. The sentence hidden vectors for each sentence are fed to the LSTM-based answer model to map the interviewee’s answer to an answer hidden vector.

Paper Details

Authors:
Ming-Hsiang Su, Kun-Yi Huang, Tsung-Hsien Yang, and Tsui-Ching Huang
Submitted On:
14 October 2016 - 11:52pm
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ISCSLP-2016-1012-MH.pdf

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[1] Ming-Hsiang Su, Kun-Yi Huang, Tsung-Hsien Yang, and Tsui-Ching Huang, "Dialog State Tracking for Interview Coaching Using Two-Level LSTM", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1214. Accessed: Apr. 30, 2017.
@article{1214-16,
url = {http://sigport.org/1214},
author = {Ming-Hsiang Su; Kun-Yi Huang; Tsung-Hsien Yang; and Tsui-Ching Huang },
publisher = {IEEE SigPort},
title = {Dialog State Tracking for Interview Coaching Using Two-Level LSTM},
year = {2016} }
TY - EJOUR
T1 - Dialog State Tracking for Interview Coaching Using Two-Level LSTM
AU - Ming-Hsiang Su; Kun-Yi Huang; Tsung-Hsien Yang; and Tsui-Ching Huang
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1214
ER -
Ming-Hsiang Su, Kun-Yi Huang, Tsung-Hsien Yang, and Tsui-Ching Huang. (2016). Dialog State Tracking for Interview Coaching Using Two-Level LSTM. IEEE SigPort. http://sigport.org/1214
Ming-Hsiang Su, Kun-Yi Huang, Tsung-Hsien Yang, and Tsui-Ching Huang, 2016. Dialog State Tracking for Interview Coaching Using Two-Level LSTM. Available at: http://sigport.org/1214.
Ming-Hsiang Su, Kun-Yi Huang, Tsung-Hsien Yang, and Tsui-Ching Huang. (2016). "Dialog State Tracking for Interview Coaching Using Two-Level LSTM." Web.
1. Ming-Hsiang Su, Kun-Yi Huang, Tsung-Hsien Yang, and Tsui-Ching Huang. Dialog State Tracking for Interview Coaching Using Two-Level LSTM [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1214

Poster for Unsupervised User Intent Modeling by Feature-Enriched Matrix Factorization


Feature-Enrich Matrix Factorization for SLU at ICASSP16

Spoken language interfaces are being incorporated into various devices such as smart phones and TVs. However, dialogue systems may fail to respond correctly when users’ request functionality is not supported by currently installed apps. This paper proposes a feature-enriched matrix factorization (MF) approach to model open domain intents, which allows a system to dynamically add unexplored domains according to users’ requests.

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Authors:
Ming Sun, Alexander I. Rudnicky, Anatole Gershman
Submitted On:
31 March 2016 - 7:51pm
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FeatureMF_poster.pdf

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[1] Ming Sun, Alexander I. Rudnicky, Anatole Gershman, "Poster for Unsupervised User Intent Modeling by Feature-Enriched Matrix Factorization", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1079. Accessed: Apr. 30, 2017.
@article{1079-16,
url = {http://sigport.org/1079},
author = {Ming Sun; Alexander I. Rudnicky; Anatole Gershman },
publisher = {IEEE SigPort},
title = {Poster for Unsupervised User Intent Modeling by Feature-Enriched Matrix Factorization},
year = {2016} }
TY - EJOUR
T1 - Poster for Unsupervised User Intent Modeling by Feature-Enriched Matrix Factorization
AU - Ming Sun; Alexander I. Rudnicky; Anatole Gershman
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1079
ER -
Ming Sun, Alexander I. Rudnicky, Anatole Gershman. (2016). Poster for Unsupervised User Intent Modeling by Feature-Enriched Matrix Factorization. IEEE SigPort. http://sigport.org/1079
Ming Sun, Alexander I. Rudnicky, Anatole Gershman, 2016. Poster for Unsupervised User Intent Modeling by Feature-Enriched Matrix Factorization. Available at: http://sigport.org/1079.
Ming Sun, Alexander I. Rudnicky, Anatole Gershman. (2016). "Poster for Unsupervised User Intent Modeling by Feature-Enriched Matrix Factorization." Web.
1. Ming Sun, Alexander I. Rudnicky, Anatole Gershman. Poster for Unsupervised User Intent Modeling by Feature-Enriched Matrix Factorization [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1079

Poster for Zero-Shot Learning of Intent Embeddings for Expansion by Convolutional Deep Structured Semantic Models


CDSSM for Zero-Shot Intent Modeling at ICASSP16

The recent surge of intelligent personal assistants motivates spoken language understanding of dialogue systems. However, the domain constraint along with the inflexible intent schema remains a big issue. This paper focuses on the task of intent expansion, which helps remove the domain limit and make an intent schema flexible. A convolutional deep structured semantic model (CDSSM) is applied to jointly learn the representations for human intents and associated utterances.

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Authors:
Dilek Hakkani-Tur, Xiaodong He
Submitted On:
31 March 2016 - 7:30pm
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ZeroShot_poster.pdf

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[1] Dilek Hakkani-Tur, Xiaodong He, "Poster for Zero-Shot Learning of Intent Embeddings for Expansion by Convolutional Deep Structured Semantic Models", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1078. Accessed: Apr. 30, 2017.
@article{1078-16,
url = {http://sigport.org/1078},
author = {Dilek Hakkani-Tur; Xiaodong He },
publisher = {IEEE SigPort},
title = {Poster for Zero-Shot Learning of Intent Embeddings for Expansion by Convolutional Deep Structured Semantic Models},
year = {2016} }
TY - EJOUR
T1 - Poster for Zero-Shot Learning of Intent Embeddings for Expansion by Convolutional Deep Structured Semantic Models
AU - Dilek Hakkani-Tur; Xiaodong He
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1078
ER -
Dilek Hakkani-Tur, Xiaodong He. (2016). Poster for Zero-Shot Learning of Intent Embeddings for Expansion by Convolutional Deep Structured Semantic Models. IEEE SigPort. http://sigport.org/1078
Dilek Hakkani-Tur, Xiaodong He, 2016. Poster for Zero-Shot Learning of Intent Embeddings for Expansion by Convolutional Deep Structured Semantic Models. Available at: http://sigport.org/1078.
Dilek Hakkani-Tur, Xiaodong He. (2016). "Poster for Zero-Shot Learning of Intent Embeddings for Expansion by Convolutional Deep Structured Semantic Models." Web.
1. Dilek Hakkani-Tur, Xiaodong He. Poster for Zero-Shot Learning of Intent Embeddings for Expansion by Convolutional Deep Structured Semantic Models [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1078

DOCUMENT LEVEL SEMANTIC CONTEXT FOR RETRIEVING OOV PROPER NAMES

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Authors:
Imran Sheikh, Irina Illina, Dominique Fohr, Georges Linarès
Submitted On:
20 March 2016 - 9:06am
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draft2_15Mar16.pdf

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[1] Imran Sheikh, Irina Illina, Dominique Fohr, Georges Linarès, "DOCUMENT LEVEL SEMANTIC CONTEXT FOR RETRIEVING OOV PROPER NAMES", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/873. Accessed: Apr. 30, 2017.
@article{873-16,
url = {http://sigport.org/873},
author = {Imran Sheikh; Irina Illina; Dominique Fohr; Georges Linarès },
publisher = {IEEE SigPort},
title = {DOCUMENT LEVEL SEMANTIC CONTEXT FOR RETRIEVING OOV PROPER NAMES},
year = {2016} }
TY - EJOUR
T1 - DOCUMENT LEVEL SEMANTIC CONTEXT FOR RETRIEVING OOV PROPER NAMES
AU - Imran Sheikh; Irina Illina; Dominique Fohr; Georges Linarès
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/873
ER -
Imran Sheikh, Irina Illina, Dominique Fohr, Georges Linarès. (2016). DOCUMENT LEVEL SEMANTIC CONTEXT FOR RETRIEVING OOV PROPER NAMES. IEEE SigPort. http://sigport.org/873
Imran Sheikh, Irina Illina, Dominique Fohr, Georges Linarès, 2016. DOCUMENT LEVEL SEMANTIC CONTEXT FOR RETRIEVING OOV PROPER NAMES. Available at: http://sigport.org/873.
Imran Sheikh, Irina Illina, Dominique Fohr, Georges Linarès. (2016). "DOCUMENT LEVEL SEMANTIC CONTEXT FOR RETRIEVING OOV PROPER NAMES." Web.
1. Imran Sheikh, Irina Illina, Dominique Fohr, Georges Linarès. DOCUMENT LEVEL SEMANTIC CONTEXT FOR RETRIEVING OOV PROPER NAMES [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/873

ADVERSARIAL BANDIT FOR ONLINE INTERACTIVE ACTIVE LEARNING OF ZERO-SHOT SPOKEN LANGUAGE UNDERSTANDING - POSTER


Many state-of-the-art solutions for the understanding of speech data
have in common to be probabilistic and to rely on machine learning
algorithms to train their models from large amount of data. The
difficulty remains in the cost of collecting and annotating such data.
Another point is the time for updating an existing model to a new domain.
Recent works showed that a zero-shot learning method allows
to bootstrap a model with good initial performance. To do so, this
method relies on exploiting both a small-sized ontological description

main.pdf

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Authors:
Emmanuel Ferreira, Alexandre Reiffers Masson, Bassam Jabaian and Fabrice Lefèvre
Submitted On:
16 March 2016 - 3:42am
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main.pdf

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[1] Emmanuel Ferreira, Alexandre Reiffers Masson, Bassam Jabaian and Fabrice Lefèvre, "ADVERSARIAL BANDIT FOR ONLINE INTERACTIVE ACTIVE LEARNING OF ZERO-SHOT SPOKEN LANGUAGE UNDERSTANDING - POSTER", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/705. Accessed: Apr. 30, 2017.
@article{705-16,
url = {http://sigport.org/705},
author = {Emmanuel Ferreira; Alexandre Reiffers Masson; Bassam Jabaian and Fabrice Lefèvre },
publisher = {IEEE SigPort},
title = {ADVERSARIAL BANDIT FOR ONLINE INTERACTIVE ACTIVE LEARNING OF ZERO-SHOT SPOKEN LANGUAGE UNDERSTANDING - POSTER},
year = {2016} }
TY - EJOUR
T1 - ADVERSARIAL BANDIT FOR ONLINE INTERACTIVE ACTIVE LEARNING OF ZERO-SHOT SPOKEN LANGUAGE UNDERSTANDING - POSTER
AU - Emmanuel Ferreira; Alexandre Reiffers Masson; Bassam Jabaian and Fabrice Lefèvre
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/705
ER -
Emmanuel Ferreira, Alexandre Reiffers Masson, Bassam Jabaian and Fabrice Lefèvre. (2016). ADVERSARIAL BANDIT FOR ONLINE INTERACTIVE ACTIVE LEARNING OF ZERO-SHOT SPOKEN LANGUAGE UNDERSTANDING - POSTER. IEEE SigPort. http://sigport.org/705
Emmanuel Ferreira, Alexandre Reiffers Masson, Bassam Jabaian and Fabrice Lefèvre, 2016. ADVERSARIAL BANDIT FOR ONLINE INTERACTIVE ACTIVE LEARNING OF ZERO-SHOT SPOKEN LANGUAGE UNDERSTANDING - POSTER. Available at: http://sigport.org/705.
Emmanuel Ferreira, Alexandre Reiffers Masson, Bassam Jabaian and Fabrice Lefèvre. (2016). "ADVERSARIAL BANDIT FOR ONLINE INTERACTIVE ACTIVE LEARNING OF ZERO-SHOT SPOKEN LANGUAGE UNDERSTANDING - POSTER." Web.
1. Emmanuel Ferreira, Alexandre Reiffers Masson, Bassam Jabaian and Fabrice Lefèvre. ADVERSARIAL BANDIT FOR ONLINE INTERACTIVE ACTIVE LEARNING OF ZERO-SHOT SPOKEN LANGUAGE UNDERSTANDING - POSTER [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/705

Distributional Semantics for Understanding Spoken Meal Descriptions

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Authors:
Calvin Huang, Michael Price, James Glass
Submitted On:
14 March 2016 - 12:04pm
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icassp_2016.pdf

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[1] Calvin Huang, Michael Price, James Glass, "Distributional Semantics for Understanding Spoken Meal Descriptions", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/676. Accessed: Apr. 30, 2017.
@article{676-16,
url = {http://sigport.org/676},
author = {Calvin Huang; Michael Price; James Glass },
publisher = {IEEE SigPort},
title = {Distributional Semantics for Understanding Spoken Meal Descriptions},
year = {2016} }
TY - EJOUR
T1 - Distributional Semantics for Understanding Spoken Meal Descriptions
AU - Calvin Huang; Michael Price; James Glass
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/676
ER -
Calvin Huang, Michael Price, James Glass. (2016). Distributional Semantics for Understanding Spoken Meal Descriptions. IEEE SigPort. http://sigport.org/676
Calvin Huang, Michael Price, James Glass, 2016. Distributional Semantics for Understanding Spoken Meal Descriptions. Available at: http://sigport.org/676.
Calvin Huang, Michael Price, James Glass. (2016). "Distributional Semantics for Understanding Spoken Meal Descriptions." Web.
1. Calvin Huang, Michael Price, James Glass. Distributional Semantics for Understanding Spoken Meal Descriptions [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/676