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\n  \n 2018\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n \n Opinion Dynamics Modeling for Movie Review Transcripts Classification with Hidden Conditional Random Fields.\n \n \n \n \n\n\n \n Barriere, V.; Clavel, C.; and Essid, S.\n\n\n \n\n\n\n Technical Report 2018.\n \n\n\n\n
\n\n\n\n \n \n Paper\n  \n \n \n Website\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@techreport{\n title = {Opinion Dynamics Modeling for Movie Review Transcripts Classification with Hidden Conditional Random Fields},\n type = {techreport},\n year = {2018},\n keywords = {Index Terms: Hidden Conditional Random Field,Linguistic Patterns,Opinion Mining,Word Embedding},\n websites = {https://github.com/phatpiglet/autocorrect},\n id = {88df06ad-0ee9-3349-9d56-126337c251cf},\n created = {2019-05-16T08:21:03.382Z},\n accessed = {2019-05-16},\n file_attached = {true},\n profile_id = {1bff199d-3fb6-39f8-9a95-8103f3a5d433},\n group_id = {af6234f1-5a5f-3882-bb01-83ebf2615cfd},\n last_modified = {2019-05-16T08:25:35.750Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {In this paper, the main goal is to detect a movie reviewer's opinion using hidden conditional random fields. This model allows us to capture the dynamics of the reviewer's opinion in the transcripts of long unsegmented audio reviews that are analyzed by our system. High level linguistic features are computed at the level of inter-pausal segments. The features include syntactic features, a statistical word embedding model and subjectivity lexicons. The proposed system is evaluated on the ICT-MMMO corpus. We obtain a F1-score of 82%, which is better than logistic regression and recurrent neural network approaches. We also offer a discussion that sheds some light on the capacity of our system to adapt the word embedding model learned from general written texts data to spoken movie reviews and thus model the dynamics of the opinion.},\n bibtype = {techreport},\n author = {Barriere, Valentin and Clavel, Chloé and Essid, Slim}\n}
\n
\n\n\n
\n In this paper, the main goal is to detect a movie reviewer's opinion using hidden conditional random fields. This model allows us to capture the dynamics of the reviewer's opinion in the transcripts of long unsegmented audio reviews that are analyzed by our system. High level linguistic features are computed at the level of inter-pausal segments. The features include syntactic features, a statistical word embedding model and subjectivity lexicons. The proposed system is evaluated on the ICT-MMMO corpus. We obtain a F1-score of 82%, which is better than logistic regression and recurrent neural network approaches. We also offer a discussion that sheds some light on the capacity of our system to adapt the word embedding model learned from general written texts data to spoken movie reviews and thus model the dynamics of the opinion.\n
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\n \n\n \n \n \n \n \n \n Structured Output Learning with Abstention: Application to Accurate Opinion Prediction.\n \n \n \n \n\n\n \n Garcia, A.; Essid, S.; Clavel, C.; and D'alché-Buc, F.\n\n\n \n\n\n\n Technical Report 2018.\n \n\n\n\n
\n\n\n\n \n \n \"StructuredPaper\n  \n \n \n \"StructuredWebsite\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@techreport{\n title = {Structured Output Learning with Abstention: Application to Accurate Opinion Prediction},\n type = {techreport},\n year = {2018},\n websites = {https://arxiv.org/pdf/1803.08355.pdf},\n id = {06d4f48a-da82-344d-8311-f4649853d1b9},\n created = {2019-06-17T10:36:18.271Z},\n accessed = {2019-06-17},\n file_attached = {true},\n profile_id = {1bff199d-3fb6-39f8-9a95-8103f3a5d433},\n group_id = {af6234f1-5a5f-3882-bb01-83ebf2615cfd},\n last_modified = {2019-06-17T10:36:18.373Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {Motivated by Supervised Opinion Analysis, we propose a novel framework devoted to Structured Output Learning with Abstention (SOLA). The structure prediction model is able to abstain from predicting some labels in the structured output at a cost chosen by the user in a flexible way. For that purpose, we decompose the problem into the learning of a pair of predictors, one devoted to structured abstention and the other, to struc-tured output prediction. To compare fully labeled training data with predictions potentially containing abstentions, we define a wide class of asymmetric abstention-aware losses. Learning is achieved by surrogate regression in an appropriate feature space while prediction with abstention is performed by solving a new pre-image problem. Thus, SOLA extends recent ideas about Struc-tured Output Prediction via surrogate problems and calibration theory and enjoys statistical guarantees on the resulting excess risk. Instantiated on a hierarchical abstention-aware loss, SOLA is shown to be relevant for fine-grained opinion mining and gives state-of-the-art results on this task. Moreover, the abstention-aware representations can be used to competitively predict user-review ratings based on a sentence-level opinion predic-tor.},\n bibtype = {techreport},\n author = {Garcia, Alexandre and Essid, Slim and Clavel, Chloé and D'alché-Buc, Florence}\n}
\n
\n\n\n
\n Motivated by Supervised Opinion Analysis, we propose a novel framework devoted to Structured Output Learning with Abstention (SOLA). The structure prediction model is able to abstain from predicting some labels in the structured output at a cost chosen by the user in a flexible way. For that purpose, we decompose the problem into the learning of a pair of predictors, one devoted to structured abstention and the other, to struc-tured output prediction. To compare fully labeled training data with predictions potentially containing abstentions, we define a wide class of asymmetric abstention-aware losses. Learning is achieved by surrogate regression in an appropriate feature space while prediction with abstention is performed by solving a new pre-image problem. Thus, SOLA extends recent ideas about Struc-tured Output Prediction via surrogate problems and calibration theory and enjoys statistical guarantees on the resulting excess risk. Instantiated on a hierarchical abstention-aware loss, SOLA is shown to be relevant for fine-grained opinion mining and gives state-of-the-art results on this task. Moreover, the abstention-aware representations can be used to competitively predict user-review ratings based on a sentence-level opinion predic-tor.\n
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\n  \n 2016\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n \n Grounding the detection of the user’s likes and dislikes on the topic structure of human-agent interactions.\n \n \n \n \n\n\n \n Langlet, C.; and Clavel, C.\n\n\n \n\n\n\n Knowledge-Based Systems, 106: 116-124. 8 2016.\n \n\n\n\n
\n\n\n\n \n \n \"GroundingWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Grounding the detection of the user’s likes and dislikes on the topic structure of human-agent interactions},\n type = {article},\n year = {2016},\n pages = {116-124},\n volume = {106},\n websites = {https://www.sciencedirect.com/science/article/pii/S0950705116301356},\n month = {8},\n publisher = {Elsevier},\n day = {15},\n id = {6ede2851-a8de-32e3-a1d7-257d8dc11ca3},\n created = {2019-05-16T08:30:28.636Z},\n accessed = {2019-05-16},\n file_attached = {false},\n profile_id = {1bff199d-3fb6-39f8-9a95-8103f3a5d433},\n group_id = {af6234f1-5a5f-3882-bb01-83ebf2615cfd},\n last_modified = {2019-05-16T08:30:28.636Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {This paper introduces a knowledge-based system which grounds the detection of the user’s likes and dislikes on the topic structure of the conversation. The targeted study is set in a human-agent interaction with the aim to help the creation of dialogue strategies of an agent based on the user’s interests. In this paper, we first describe the system based on linguistic resources such as lexicons, dependency grammars and dialogue information provided by the dialogue system. Second, we explain how the system merges its outputs at the end of each topic sequence. Finally, we present an evaluation of both the linguistic rules and the merging process. The system enables a better identification of the target of the user’s likes and dislikes and provides a synthetic representation of the user’s interests.},\n bibtype = {article},\n author = {Langlet, Caroline and Clavel, Chloé},\n doi = {10.1016/J.KNOSYS.2016.05.038},\n journal = {Knowledge-Based Systems}\n}
\n
\n\n\n
\n This paper introduces a knowledge-based system which grounds the detection of the user’s likes and dislikes on the topic structure of the conversation. The targeted study is set in a human-agent interaction with the aim to help the creation of dialogue strategies of an agent based on the user’s interests. In this paper, we first describe the system based on linguistic resources such as lexicons, dependency grammars and dialogue information provided by the dialogue system. Second, we explain how the system merges its outputs at the end of each topic sequence. Finally, we present an evaluation of both the linguistic rules and the merging process. The system enables a better identification of the target of the user’s likes and dislikes and provides a synthetic representation of the user’s interests.\n
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\n \n\n \n \n \n \n \n \n Sentiment Analysis: From Opinion Mining to Human-Agent Interaction.\n \n \n \n \n\n\n \n Clavel, C.; and Callejas, Z.\n\n\n \n\n\n\n IEEE Transactions on Affective Computing, 7(1): 74-93. 1 2016.\n \n\n\n\n
\n\n\n\n \n \n \"SentimentWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Sentiment Analysis: From Opinion Mining to Human-Agent Interaction},\n type = {article},\n year = {2016},\n pages = {74-93},\n volume = {7},\n websites = {http://ieeexplore.ieee.org/document/7122903/},\n month = {1},\n day = {1},\n id = {bb23b1ee-7710-3279-848f-161078d593e9},\n created = {2019-05-16T13:49:46.365Z},\n accessed = {2019-05-16},\n file_attached = {false},\n profile_id = {1bff199d-3fb6-39f8-9a95-8103f3a5d433},\n group_id = {af6234f1-5a5f-3882-bb01-83ebf2615cfd},\n last_modified = {2019-05-16T13:49:46.365Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Clavel, Chloe and Callejas, Zoraida},\n doi = {10.1109/TAFFC.2015.2444846},\n journal = {IEEE Transactions on Affective Computing},\n number = {1}\n}
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\n  \n 2015\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n \n Improving social relationships in face-to-face human-agent interactions: when the agent wants to know user's likes and dislikes.\n \n \n \n \n\n\n \n Langlet, C.; and Clavel, C.\n\n\n \n\n\n\n Technical Report 2015.\n \n\n\n\n
\n\n\n\n \n \n \"ImprovingWebsite\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@techreport{\n title = {Improving social relationships in face-to-face human-agent interactions: when the agent wants to know user's likes and dislikes},\n type = {techreport},\n year = {2015},\n pages = {1064-1073},\n websites = {https://www.aclweb.org/anthology/P15-1103},\n publisher = {Association for Computational Linguistics},\n id = {863c74f5-4c02-312f-92c7-79b255773e18},\n created = {2019-05-16T08:28:31.969Z},\n accessed = {2019-05-16},\n file_attached = {false},\n profile_id = {1bff199d-3fb6-39f8-9a95-8103f3a5d433},\n group_id = {af6234f1-5a5f-3882-bb01-83ebf2615cfd},\n last_modified = {2019-05-16T08:32:31.519Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {This paper tackles the issue of the detection of user's verbal expressions of likes and dislikes in a human-agent interaction. We present a system grounded on the theoretical framework provided by (Martin and White, 2005) that integrates the interaction context by jointly processing agent's and user's utterances. It is designed as a rule-based and bottom-up process based on a symbolic representation of the structure of the sentence. This article also describes the annotation campaign-carried out through Amazon Mechanical Turk-for the creation of the evaluation data-set. Finally, we present all measures for rating agreement between our system and the human reference and obtain agreement scores that are equal or higher than substantial agreements.},\n bibtype = {techreport},\n author = {Langlet, Caroline and Clavel, Chloé}\n}
\n
\n\n\n
\n This paper tackles the issue of the detection of user's verbal expressions of likes and dislikes in a human-agent interaction. We present a system grounded on the theoretical framework provided by (Martin and White, 2005) that integrates the interaction context by jointly processing agent's and user's utterances. It is designed as a rule-based and bottom-up process based on a symbolic representation of the structure of the sentence. This article also describes the annotation campaign-carried out through Amazon Mechanical Turk-for the creation of the evaluation data-set. Finally, we present all measures for rating agreement between our system and the human reference and obtain agreement scores that are equal or higher than substantial agreements.\n
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\n \n\n \n \n \n \n \n Adapting sentiment analysis to face-to-face human-agent interactions: From the detection to the evaluation issues.\n \n \n \n\n\n \n Langlet, C.; and Clavel, C.\n\n\n \n\n\n\n 2015 International Conference on Affective Computing and Intelligent Interaction, ACII 2015,14-20. 2015.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Adapting sentiment analysis to face-to-face human-agent interactions: From the detection to the evaluation issues},\n type = {article},\n year = {2015},\n keywords = {alignment,embodied conversational agent,emotional stance,engagement,other-repetition},\n pages = {14-20},\n publisher = {IEEE},\n id = {c1d5d5c5-353b-3650-b853-37d3372ebca4},\n created = {2019-06-17T10:48:49.388Z},\n file_attached = {false},\n profile_id = {1bff199d-3fb6-39f8-9a95-8103f3a5d433},\n group_id = {af6234f1-5a5f-3882-bb01-83ebf2615cfd},\n last_modified = {2019-06-17T10:48:49.388Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {© 2015 IEEE. This paper introduces a sentiment analysis method suitable to the human-agent and face-to-face interactions. We present the positioning of our system and its evaluation protocol according to the existing sentiment analysis literature and detail how the proposed system integrates the human-agent interaction issues. Finally, we provide an in-depth analysis of the results obtained by the evaluation, opening the discussion on the different difficulties and the remaining challenges of sentiment analysis in human-agent interactions.},\n bibtype = {article},\n author = {Langlet, Caroline and Clavel, Chloe},\n doi = {10.1109/ACII.2015.7344545},\n journal = {2015 International Conference on Affective Computing and Intelligent Interaction, ACII 2015}\n}
\n
\n\n\n
\n © 2015 IEEE. This paper introduces a sentiment analysis method suitable to the human-agent and face-to-face interactions. We present the positioning of our system and its evaluation protocol according to the existing sentiment analysis literature and detail how the proposed system integrates the human-agent interaction issues. Finally, we provide an in-depth analysis of the results obtained by the evaluation, opening the discussion on the different difficulties and the remaining challenges of sentiment analysis in human-agent interactions.\n
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\n  \n 2014\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n \n A CRF-based Approach to Automatic Disfluency Detection in a French Call-Centre Corpus.\n \n \n \n \n\n\n \n Dutrey, C.; Clavel, C.; Rosset, S.; Vasilescu, I.; and Adda-Decker, M.\n\n\n \n\n\n\n Technical Report 2014.\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n \n \"AWebsite\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@techreport{\n title = {A CRF-based Approach to Automatic Disfluency Detection in a French Call-Centre Corpus},\n type = {techreport},\n year = {2014},\n keywords = {Index Terms : disfluencies,conditional random fields,conver-sational speech,spontaneous speech},\n websites = {https://gforge.inria.fr/projects/discretize4crf/.},\n id = {496020df-f2c6-3537-b35e-f521e49636dc},\n created = {2019-05-16T08:21:33.452Z},\n accessed = {2019-05-16},\n file_attached = {true},\n profile_id = {1bff199d-3fb6-39f8-9a95-8103f3a5d433},\n group_id = {af6234f1-5a5f-3882-bb01-83ebf2615cfd},\n last_modified = {2019-05-16T08:21:35.038Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {In this paper, we present a Conditional Random Field based approach for automatic detection of edit disfluencies in a conversational telephone corpus in French. We define dis-fluency patterns using both linguistic and acoustic features to perform disfluency detection. Two related tasks are considered : the first task aims at detecting the disfluent speech portion proper or reparandum, i.e. the portion to be removed if we want to improve the readability of transcribed data ; in the second task, we aim at identifying also the corrected portion or repair which can be useful in follow-up discourse and dialogue analyses or in opinion mining. For these two tasks, we present comparative results as a function of the involved type of features (acoustic and/or linguistic). Generally speaking, best results are obtained by CRF models combining both acoustic and linguistic features.},\n bibtype = {techreport},\n author = {Dutrey, Camille and Clavel, Chloé and Rosset, Sophie and Vasilescu, Ioana and Adda-Decker, Martine}\n}
\n
\n\n\n
\n In this paper, we present a Conditional Random Field based approach for automatic detection of edit disfluencies in a conversational telephone corpus in French. We define dis-fluency patterns using both linguistic and acoustic features to perform disfluency detection. Two related tasks are considered : the first task aims at detecting the disfluent speech portion proper or reparandum, i.e. the portion to be removed if we want to improve the readability of transcribed data ; in the second task, we aim at identifying also the corrected portion or repair which can be useful in follow-up discourse and dialogue analyses or in opinion mining. For these two tasks, we present comparative results as a function of the involved type of features (acoustic and/or linguistic). Generally speaking, best results are obtained by CRF models combining both acoustic and linguistic features.\n
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\n \n\n \n \n \n \n \n \n Alignement par Production d'Hétéro-Répétitions chez un ACA.\n \n \n \n \n\n\n \n Glas, N.; Langlet, C.; and Pelachaud, C.\n\n\n \n\n\n\n Technical Report 2014.\n \n\n\n\n
\n\n\n\n \n \n \"AlignementWebsite\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@techreport{\n title = {Alignement par Production d'Hétéro-Répétitions chez un ACA},\n type = {techreport},\n year = {2014},\n websites = {https://www.researchgate.net/publication/282246832},\n id = {47ee85f8-cb7b-3878-a9b4-f6c2c69ddcc4},\n created = {2019-06-17T10:51:12.125Z},\n accessed = {2019-06-17},\n file_attached = {false},\n profile_id = {1bff199d-3fb6-39f8-9a95-8103f3a5d433},\n group_id = {af6234f1-5a5f-3882-bb01-83ebf2615cfd},\n last_modified = {2019-06-17T10:51:12.125Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n bibtype = {techreport},\n author = {Glas, Nadine and Langlet, Caroline and Pelachaud, Catherine}\n}
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\n  \n 2003\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Intelligent Virtual Agents.\n \n \n \n \n\n\n \n Langlet, C.; and Duplessis, G., D.\n\n\n \n\n\n\n , 2792: 239-240. 2003.\n \n\n\n\n
\n\n\n\n \n \n \"IntelligentWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{\n title = {Intelligent Virtual Agents},\n type = {article},\n year = {2003},\n pages = {239-240},\n volume = {2792},\n websites = {http://link.springer.com/10.1007/b12026},\n id = {82dd4731-49ba-357b-8f0f-b6e6235369a9},\n created = {2019-06-17T10:48:49.388Z},\n file_attached = {false},\n profile_id = {1bff199d-3fb6-39f8-9a95-8103f3a5d433},\n group_id = {af6234f1-5a5f-3882-bb01-83ebf2615cfd},\n last_modified = {2019-06-17T10:48:49.388Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {The importance of sound and music in movies (Film Scoring) is undeniable. Film Scoring is used as a complement to the visual\\n content helping to better understand it. The use of these artistic elements can be extended to interactive storytelling (IS)\\n environments where stories are also told in a dynamic and interactive way. Because of IS particularities, such as the unpredictability\\n of user actions and story development, this extension is not straightforward, and presents a problem that needs to be addressed.\\n Further, the presence of autonomous virtual agents makes the creation of these stories more emergent, making difficult the\\n process of creating sounds and music that is adequate. This paper presents a framework that proposes a possible solution to\\n score a story created in these environments by intelligent virtual agents. Additionally, we studied the influence of some\\n particular elements of sound and music (such as tempo, use of different instruments, etc) on the viewers and their perception\\n of the actions of the characters and thus, on the consequent understanding of the story.},\n bibtype = {article},\n author = {Langlet, Caroline and Duplessis, Guillaume Dubuisson},\n doi = {10.1007/b12026}\n}
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\n The importance of sound and music in movies (Film Scoring) is undeniable. Film Scoring is used as a complement to the visual\\n content helping to better understand it. The use of these artistic elements can be extended to interactive storytelling (IS)\\n environments where stories are also told in a dynamic and interactive way. Because of IS particularities, such as the unpredictability\\n of user actions and story development, this extension is not straightforward, and presents a problem that needs to be addressed.\\n Further, the presence of autonomous virtual agents makes the creation of these stories more emergent, making difficult the\\n process of creating sounds and music that is adequate. This paper presents a framework that proposes a possible solution to\\n score a story created in these environments by intelligent virtual agents. Additionally, we studied the influence of some\\n particular elements of sound and music (such as tempo, use of different instruments, etc) on the viewers and their perception\\n of the actions of the characters and thus, on the consequent understanding of the story.\n
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