Roccabruna G., Cervone A. and Riccardi G. Multifunctional ISO standard Dialogue Act tagging in Italian (Article) 2020. (Links | BibTeX | Tags: Discourse, Natural Language Processing) Cervone A. and Riccardi G. Is This Dialogue Coherent ? Learning From Dialogue Acts and Entities (Article) 2020. (Links | BibTeX | Tags: Conversational and Interactive Systems , Discourse, Natural Language Processing) Chowdhury S. A., Stepanov E. A., Danieli M. and Riccardi G. Automatic Classification of Speech Overlaps: Feature Representation and Algorithms (Article) Computer Speech and Language, 55 pp. 145-167, 2019. (Links | BibTeX | Tags: Discourse, Speech Analytics) Tortoreto G., Stepanov E. A., Cervone A., Dubiel M., Riccardi G. Affective Behaviour Analysis of On-line User Interactions: Are On-line Support Groups more Therapeutic than Twitter? (Conference) 2019. (Links | BibTeX | Tags: Discourse, Health, Language Analytics, Machine Learning) Alam F., Danieli M. and Riccardi G. Annotating and Modeling Empathy in Spoken Conversations (Article) Computer Speech and Language, 50 pp. 40-61, 2018. (Links | BibTeX | Tags: Affective Computing, Discourse, Signal Annotation and Interpretation) Cervone A., Stepanov E. A. and Riccardi G. Coherence Models for Dialogue (Conference) 2018. (Links | BibTeX | Tags: Conversational and Interactive Systems , Discourse) Mezza S., Cervone A., Stepanov E. A., Tortoreto G. and Riccardi G. ISO-Standard Domain-Independent Dialogue Act Tagging for Conversational Agents (Conference) 2018. (Links | BibTeX | Tags: Conversational and Interactive Systems , Discourse) Celli F., Stepanov A. E. , Riccardi G, Tell me who you are, I’ll tell whether you agree or disagree: Prediction of agreement/disagreement in news blogs (Proceeding) IJCAI, Proc. Workshop on Natural Language Processing Meets Journalism, New York, 2016, 2016. (Abstract | Links | BibTeX | Tags: Discourse) Schenk N., Chiarcos C., Donandt K., Rönnqvist S., Stepanov A. E. and Riccardi G. Do We Really Need All Those Rich Linguistic Features? A Neural Network-Based Approach to Implicit Sense Labeling (Proceeding) Proc. CoNLL, Berlin, 2016, 2016. (Abstract | Links | BibTeX | Tags: Discourse, Natural Language Processing) Riccardi G., Stepanov A. E. and Chowdhury S. Discourse Connective Detection in Spoken Conversations (Proceeding) Proc. ICASSP, Shanghai, 2016, 2016. (Abstract | Links | BibTeX | Tags: Discourse, Natural Language Processing, Speech Processing) Stepanov A. E. and Riccardi G. The UniTN End-To-End Discourse Parser in CoNLL 2016 Shared Task (Proceeding) Proc. CoNLL, Berlin, 2016, 2016. (Abstract | Links | BibTeX | Tags: Discourse) Chowdhury S. , Stepanov A. E. and Riccardi G. Predicting User Satisfaction from Turn-Taking in Spoken Conversations (Proceeding) Proc. INTERSPEECH, San Francisco, 2016., 2016. (Abstract | Links | BibTeX | Tags: Affective Computing, Conversational and Interactive Systems , Discourse, Interactive Systems, Signal Annotation and Interpretation, Speech Processing) Alam F. , Danieli M.. and Riccardi G. Can We Detect Speakers' Empathy? A Real-Life Case Study (Proceeding) Proc. IEEE International Conference on Cognitive Infocommunications, Wrocław, 2016, 2016. (Abstract | Links | BibTeX | Tags: Discourse, Interactive Systems) Alam F. , Chowdhury S. , Danieli M. and Riccardi G. How Interlocutors Coordinate with each other within Emotional Segments? (Proceeding) Proc. COLING, Osaka, 2016., 2016. (Abstract | Links | BibTeX | Tags: Affective Computing, Conversational and Interactive Systems , Discourse, Interactive Systems) Stepanov E., Bayer A. O., Riccardi G., The UniTN Discourse Parser in CoNLL 2015 Shared Task (Conference) 2015. (Abstract | Links | BibTeX | Tags: Discourse, Machine Learning, Natural Language Processing) Chowdhury A, Danieli M. and Riccardi G. Annotating and Categorizing Competition in Overlap Speech (Conference) 2015. (Abstract | Links | BibTeX | Tags: Discourse, Natural Language Processing, Signal Annotation and Interpretation) Ghosh S., Johansson R., Riccardi G. and Tonelli S. Shallow Discourse Parsing with Conditional Random Fields (Conference) 2014. (Abstract | Links | BibTeX | Tags: Discourse, Natural Language Processing) Ghosh A. and Riccardi G. Recognizing Human Activities from Smartphone Signals (Conference) 2014. (Abstract | Links | BibTeX | Tags: Discourse, Natural Language Processing) Ghosh S., Johansson R., Riccardi G. and Tonelli S. Improving the Recall of a Discourse Parser by Constraint-Based Postprocessing (Conference) 2012. (BibTeX | Tags: Discourse, Machine Learning, Natural Language Processing) Ghosh S., Tonelli S., Riccardi G. and Johansson R. End-to-End Discourse Parser Evaluation (Conference) 2011. (Abstract | Links | BibTeX | Tags: Discourse, Machine Learning, Natural Language Processing, Signal Annotation and Interpretation) Sara Tonelli S., Riccardi G., Prasad R. and Joshi A. Annotation of Discourse Relations for Conversational Spoken Dialogs (Conference) 2010. (Abstract | Links | BibTeX | Tags: Discourse, Natural Language Processing, Signal Annotation and Interpretation)2020
title = {Multifunctional ISO standard Dialogue Act tagging in Italian },
author = {Roccabruna G., Cervone A. and Riccardi G.},
editor = {Seventh Italian Conference on Computational Linguistics, 2020},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2020/12/Clicit20-ISODAItalian.pdf},
year = {2020},
date = {2020-11-02},
keywords = {Discourse, Natural Language Processing}
}
title = {Is This Dialogue Coherent ? Learning From Dialogue Acts and Entities},
author = {Cervone A. and Riccardi G.},
editor = {SIGDial, Idaho*, 2020},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2020/12/SIGDIAL20-DialogueCoherence.pdf},
year = {2020},
date = {2020-06-01},
keywords = {Conversational and Interactive Systems , Discourse, Natural Language Processing}
}
2019
title = {Automatic Classification of Speech Overlaps: Feature Representation and Algorithms},
author = {Chowdhury S. A., Stepanov E. A., Danieli M. and Riccardi G.},
url = {https://disi.unitn.it/~riccardi/papers2/CSL19-SpeechOverlapCategorization.pdf},
year = {2019},
date = {2019-05-01},
journal = {Computer Speech and Language},
volume = {55},
pages = {145-167},
keywords = {Discourse, Speech Analytics}
}
title = {Affective Behaviour Analysis of On-line User Interactions: Are On-line Support Groups more Therapeutic than Twitter? },
author = {Tortoreto G., Stepanov E. A., Cervone A., Dubiel M., Riccardi G.},
editor = {Association for Computational Linguistics Conference, Workshop on Social Media Mining for Health Applications, Florence},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2019/11/ACL19-AffectiveBehaviourOSG.pdf},
year = {2019},
date = {2019-01-01},
keywords = {Discourse, Health, Language Analytics, Machine Learning}
}
2018
title = {Annotating and Modeling Empathy in Spoken Conversations},
author = {Alam F., Danieli M. and Riccardi G.},
url = {https://www.sciencedirect.com/science/article/pii/S088523081730133X},
year = {2018},
date = {2018-07-01},
journal = {Computer Speech and Language},
volume = {50},
pages = {40-61},
keywords = {Affective Computing, Discourse, Signal Annotation and Interpretation}
}
title = {Coherence Models for Dialogue},
author = {Cervone A., Stepanov E. A. and Riccardi G.},
editor = {INTERSPEECH, Hyderabad},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2019/11/IS18-DiscourseModels.pdf},
year = {2018},
date = {2018-01-01},
keywords = {Conversational and Interactive Systems , Discourse}
}
title = {ISO-Standard Domain-Independent Dialogue Act Tagging for Conversational Agents},
author = {Mezza S., Cervone A., Stepanov E. A., Tortoreto G. and Riccardi G.},
editor = {Conference on Computational Linguistics (COLING), Santa Fe},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2019/11/Coling18-ISO-DA-Tagging.pdf},
year = {2018},
date = {2018-01-01},
keywords = {Conversational and Interactive Systems , Discourse}
}
2016
title = {Tell me who you are, I’ll tell whether you agree or disagree: Prediction of agreement/disagreement in news blogs},
author = {Celli F., Stepanov A. E. , Riccardi G,},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2016/11/IJCAI16-AgreementDisagreementBlogger.pdf},
year = {2016},
date = {2016-11-01},
publisher = {IJCAI, Proc. Workshop on Natural Language Processing Meets Journalism, New York, 2016},
abstract = {In this paper we address the problem of the automatic classification of agreement and disagreement in news blog conversations. We analyze bloggers, messages and relations between messages. We show that relational features (such as replying to a message or to an article) and information about bloggers (such as personality, stances, mood and discourse structure priors) boost the performance in the classification of agreement/disagreement more than features extracted from messages, such as sentiment, style and general discourse relation senses. We also show that bloggers exhibit reply patterns significantly correlated to the expression of agreement or disagreement. Moreover, we show that there are also discourse structures correlated to agreement (expansion relations), and to disagreement (contingency relations).},
keywords = {Discourse}
}
title = {Do We Really Need All Those Rich Linguistic Features? A Neural Network-Based Approach to Implicit Sense Labeling},
author = {Schenk N., Chiarcos C., Donandt K., Rönnqvist S., Stepanov A. E. and Riccardi G.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2016/11/CoNLL16-FrankfurtUNITNDiscourseParser.pdf},
year = {2016},
date = {2016-11-01},
publisher = {Proc. CoNLL, Berlin, 2016},
abstract = {We describe our contribution to the CoNLL 2016 Shared Task on shallow discourse parsing.1 Our system extends the two best parsers from previous year’s competition by integration of a novel implicit sense labeling component. It is grounded on a highly generic, language-independent feedforward neural network architecture incorporating weighted word embeddings for argument spans which obviates the need for (traditional) hand-crafted features.
Despite its simplicity, our system overall outperforms all results from 2015 on 5 out of 6 evaluation sets for English and achieves an absolute improvement in F1-score of 3.2% on the PDTB test section for non-explicit sense classification.},
keywords = {Discourse, Natural Language Processing}
}
Despite its simplicity, our system overall outperforms all results from 2015 on 5 out of 6 evaluation sets for English and achieves an absolute improvement in F1-score of 3.2% on the PDTB test section for non-explicit sense classification.
title = {Discourse Connective Detection in Spoken Conversations},
author = {Riccardi G., Stepanov A. E. and Chowdhury S.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2016/11/ICASSP16-DiscourseConnective.pdf},
year = {2016},
date = {2016-11-01},
publisher = {Proc. ICASSP, Shanghai, 2016},
abstract = {Discourse parsing is an important task in Language Understanding with applications to human-human and human-machine communication modeling. However, most of the research has focused on written text, and parsers heavily rely on syntactic parsers that themselves have low performance on dialog data. In our work, we address the problem of analyzing the semantic relations between discourse units in human-human spoken conversations. In particular, in this paper we focus on the detection of discourse connectives which are the predicate of such relations. The discourse relations are drawn from the Penn Discourse Treebank annotation model and adapted to a domain-specific Italian human-human spoken conversations. We study the relevance of lexical and acoustic context in predicting discourse connectives. We observe that both lexical and acoustic context have mixed effect on the prediction of specific connectives. While the oracle of using lexical and acoustic contextual feature combinations is F1 = 68.53, the lexical context alone significantly outperforms the baseline by more than 10 points with F1 = 64.93.},
keywords = {Discourse, Natural Language Processing, Speech Processing}
}
title = {The UniTN End-To-End Discourse Parser in CoNLL 2016 Shared Task},
author = {Stepanov A. E. and Riccardi G.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2016/11/CoNLL16-UNITNDiscourseParser.pdf},
year = {2016},
date = {2016-11-01},
publisher = {Proc. CoNLL, Berlin, 2016},
abstract = {Penn Discourse Treebank style discourse parsing is a composite task of detecting explicit and non-explicit discourse relations, their connective and argument spans, and assigning a sense to these relations. Due to the composite nature of the task, the end-to-end performance is greatly affected by the error propagation. This paper describes the end-to-end discourse parser for English submitted to the CoNLL 2016 Shared Task on Shallow Discourse Parsing with the main focus of the parser being on argument spans and the reduction of global error through model selection. In the end-to-end closed-track evaluation the parser achieves F-measure of 0.2510 outperforming the best system of the previous year.},
keywords = {Discourse}
}
title = {Predicting User Satisfaction from Turn-Taking in Spoken Conversations},
author = {Chowdhury S. , Stepanov A. E. and Riccardi G.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2016/11/IS16-PredictingUserSatisfactionTurnTaking.pdf},
year = {2016},
date = {2016-11-01},
publisher = {Proc. INTERSPEECH, San Francisco, 2016.},
abstract = {User satisfaction is an important aspect of the user experience while interacting with objects, systems or people. Traditionally user satisfaction is evaluated a-posteriori via spoken or written questionnaires or interviews. In automatic behavioral analysis we aim at measuring the user emotional states and its descriptions as they unfold during the interaction. In our approach, user satisfaction is modeled as the final state of a sequence of emotional states and given ternary values positive, negative, neutral. In this paper, we investigate the discriminating power of turn-taking in predicting user satisfaction in spoken conversations. Turn-taking is used for discourse organization of a conversation by means of explicit phrasing, intonation, and pausing. In this paper, we train different characterization of turn-taking, such as competitiveness of the speech overlaps. To extract turn-taking features we design a turn segmentation and labeling system that incorporates lexical and acoustic information. Given a human-human spoken dialog, our system automatically infers any of the three values of the state of the user satisfaction. We evaluate the classification system on real-life call-center human-human dialogs. The comparative performance analysis shows that the contribution of the turn-taking features outperforms both prosodic and lexical features.},
keywords = {Affective Computing, Conversational and Interactive Systems , Discourse, Interactive Systems, Signal Annotation and Interpretation, Speech Processing}
}
title = {Can We Detect Speakers' Empathy? A Real-Life Case Study},
author = {Alam F. , Danieli M.. and Riccardi G.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2016/11/CogInfo16-Detect-speakers-empathy.pdf},
year = {2016},
date = {2016-11-01},
publisher = {Proc. IEEE International Conference on Cognitive Infocommunications, Wrocław, 2016},
abstract = {In the context of automatic behavioral analysis, we aim to classify empathy in human-human spoken conversations. Empathy underlies to the human ability to recognize, understand and to react to emotions, attitudes, and beliefs of others. While empathy and its different manifestations (e.g., sympathy, compassion) have been widely studied in psychology, very little has been done in the computational research literature. In this paper, we present a case study where we investigate the occurrences of empathy in call-centers human-human conversations. In order to propose an operational definition of empathy, we adopt the modal model of emotions, where the appraisal processes of the unfolding of emotional states are modeled sequentially. We have designed a binary classification system to detect the presence of empathic manifestations in spoken conversations. The automatic classification system has been evaluated using spoken conversations by exploiting and comparing perform},
keywords = {Discourse, Interactive Systems}
}
title = {How Interlocutors Coordinate with each other within Emotional Segments?},
author = {Alam F. , Chowdhury S. , Danieli M. and Riccardi G.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2016/11/Coling16-CoordinationEmotionalSegments.pdf},
year = {2016},
date = {2016-11-01},
publisher = {Proc. COLING, Osaka, 2016.},
abstract = {In this paper, we aim to investigate the coordination of interlocutors behavior in different emotional segments. Conversational coordination between the interlocutors is the tendency of speakers to predict and adjust each other accordingly on an ongoing conversation. In order to find such a coordination, we investigated 1) lexical similarities between the speakers in each emotional segments,
2) correlation between the interlocutors using psycholinguistic features, such as linguistic styles, psychological process, personal concerns among others, and 3) relation of interlocutors turn-taking behaviors such as competitiveness. To study the degree of coordination in different emotional segments, we conducted our experiments using real dyadic conversations collected from call centers in which agent’s emotional state include empathy and customer’s emotional states include anger and frustration. Our findings suggest that the most coordination occurs between the interlocutors inside anger segments, where as, a little coordination was observed when the agent was empathic, even though an increase in the amount of non-competitive overlaps was observed. We found no significant difference between anger and frustration segment in terms of turn-taking behaviors. However, the length of pause significantly decreases in the preceding segment of anger where as it increases in the preceding segment of frustration.},
keywords = {Affective Computing, Conversational and Interactive Systems , Discourse, Interactive Systems}
}
2) correlation between the interlocutors using psycholinguistic features, such as linguistic styles, psychological process, personal concerns among others, and 3) relation of interlocutors turn-taking behaviors such as competitiveness. To study the degree of coordination in different emotional segments, we conducted our experiments using real dyadic conversations collected from call centers in which agent’s emotional state include empathy and customer’s emotional states include anger and frustration. Our findings suggest that the most coordination occurs between the interlocutors inside anger segments, where as, a little coordination was observed when the agent was empathic, even though an increase in the amount of non-competitive overlaps was observed. We found no significant difference between anger and frustration segment in terms of turn-taking behaviors. However, the length of pause significantly decreases in the preceding segment of anger where as it increases in the preceding segment of frustration.2015
title = {The UniTN Discourse Parser in CoNLL 2015 Shared Task},
author = {Stepanov E., Bayer A. O., Riccardi G.,},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2015/11/CoNLL15-UNITNDiscourseParser.pdf},
year = {2015},
date = {2015-07-30},
journal = {Proc. CoNLL, Bejiing, 2015. Runner-up Discurse Parsing Shared-Task},
abstract = {Penn Discourse Treebank style discourse parsing is a composite task of identifying discourse relations (explicit or nonexplicit), their connective and argument spans, and assigning a sense to these relations from the hierarchy of senses. In this paper we describe University of Trento parser submitted to CoNLL 2015 Shared Task on Shallow Discourse Parsing. The span detection tasks for explicit relations are cast as token-level sequence labeling.
The argument span decisions are conditioned on relations’ being intra- or intersentential.
Non-explicit relation detection and sense assignment tasks are cast as classification. In the end-to-end closedtrack evaluation, the parser ranked second with a global F-measure of 0.2184},
keywords = {Discourse, Machine Learning, Natural Language Processing}
}
The argument span decisions are conditioned on relations’ being intra- or intersentential.
Non-explicit relation detection and sense assignment tasks are cast as classification. In the end-to-end closedtrack evaluation, the parser ranked second with a global F-measure of 0.2184
title = {Annotating and Categorizing Competition in Overlap Speech},
author = {Chowdhury A, Danieli M. and Riccardi G.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2015/11/ICASSP15-OverlapClassification.pdf},
year = {2015},
date = {2015-04-19},
journal = {Proc. ICASSP, Brisbane, 2015.},
abstract = {Overlapping speech is a common and relevant phenomenon in human conversations, reflecting many aspects of discourse dynamics. In this paper, we focus on the pragmatic role of overlaps in turn-in-progress, where it can be categorized as competitive or non-competitive. Previous studies on these
two categories have mostly relied on controlled scenarios and small datasets. In our study, we focus on call center data, with customers and operators engaged in problem-solving tasks. We propose and evaluate an annotation scheme for these two overlap categories in the context of spontaneous and in-vivo human conversations. We analyze the distinctive predictive characteristics of a very large set of high-dimensional acoustic feature. We obtained a significant improvement in classification results as well as significant reduction in the feature set size.},
keywords = {Discourse, Natural Language Processing, Signal Annotation and Interpretation}
}
two categories have mostly relied on controlled scenarios and small datasets. In our study, we focus on call center data, with customers and operators engaged in problem-solving tasks. We propose and evaluate an annotation scheme for these two overlap categories in the context of spontaneous and in-vivo human conversations. We analyze the distinctive predictive characteristics of a very large set of high-dimensional acoustic feature. We obtained a significant improvement in classification results as well as significant reduction in the feature set size.2014
title = {Shallow Discourse Parsing with Conditional Random Fields},
author = {Ghosh S., Johansson R., Riccardi G. and Tonelli S.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2014/11/I11-1120.pdf},
year = {2014},
date = {2014-01-01},
journal = {International Joint Conference on Natural Language Processing, Chiang Mai, Thailand, 2011},
abstract = {Parsing discourse is a challenging natural language processing task. In this paper we take a data driven approach to identify arguments of explicit discourse connectives. In contrast to previous work we do not make any assumptions on the span of arguments and consider parsing as a token-level sequence labeling task. We design the argument segmentation task as a cascade of decisions based on conditional random fields (CRFs). We train the CRFs on lexical, syntactic and semantic features extracted from the Penn Discourse Treebank and evaluate feature combinations on the commonly used test split. We show that the best combination of features includes syntactic and semantic features. The comparative error analysis investigates the performance variability over connective types and argument positions.},
keywords = {Discourse, Natural Language Processing}
}
title = {Recognizing Human Activities from Smartphone Signals},
author = {Ghosh A. and Riccardi G.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2014/11/ACMM14-RecognizingHumanActivitiesSensor-Signals.pdf},
year = {2014},
date = {2014-01-01},
journal = {ACM International Conference on Multimedia, Orlando, 2014},
abstract = {In context-aware computing, Human Activity Recognition (HAR) aims to understand the current activity of users from their connected sensors. Smartphones with their various sensors are opening a new frontier in building human-centered applications for understanding users’ personal and world contexts. While in-lab and controlled activity recognition systems have yielded very good results, they do not perform well under in-the-wild scenarios. The objective of this paper is to 1) Investigate how audio signal can complement and improve other on-board sensors (accelerometer and gyroscope) for activity recognition; 2) Design and evaluate the fusion of such multiple signal streams to optimize performance and sampling rate. We show that fusion of these signal streams, including audio, achieves high performance even at very low sampling rates; 3) Evaluate the performance of the multistream human activity recognition under different real enduser activity conditions.},
keywords = {Discourse, Natural Language Processing}
}
2012
title = {Improving the Recall of a Discourse Parser by Constraint-Based Postprocessing},
author = {Ghosh S., Johansson R., Riccardi G. and Tonelli S.},
year = {2012},
date = {2012-01-01},
journal = {LREC Istanbul, 2012},
keywords = {Discourse, Machine Learning, Natural Language Processing}
}
2011
title = {End-to-End Discourse Parser Evaluation},
author = {Ghosh S., Tonelli S., Riccardi G. and Johansson R.},
url = {https://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6061347&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D6061347},
year = {2011},
date = {2011-01-01},
journal = {IEEE International Conference on Semantic Computing, Menlo Park, USA, 2011},
abstract = {We are interested in the problem of discourse parsing of textual documents. We present a novel end-to-end discourse parser that, given a plain text document in input, identifies the discourse relations in the text, assigns them a semantic label and detects discourse arguments spans. The parsing architecture is based on a cascade of decisions supported by Conditional Random Fields (CRF). We train and evaluate three different parsers using the PDTB corpus. The three system versions are compared to evaluate their robustness with respect to deep/shallow and automatically extracted syntactic features.},
keywords = {Discourse, Machine Learning, Natural Language Processing, Signal Annotation and Interpretation}
}
2010
title = {Annotation of Discourse Relations for Conversational Spoken Dialogs},
author = {Sara Tonelli S., Riccardi G., Prasad R. and Joshi A.},
url = {https://sisl.disi.unitn.it/wp-content/uploads/2014/11/LREC10-DialoguePDTBAnnotation.pdf},
year = {2010},
date = {2010-01-01},
journal = {LREC Valletta, 2010},
abstract = {In this paper, we make a qualitative and quantitative analysis of discourse relations within the LUNA conversational spoken dialog corpus. In particular, we describe the adaptation of the Penn Discourse Treebank (PDTB) annotation scheme to the LUNA dialogs. We discuss similarities and differences between our approach and the PDTB paradigm and point out the peculiarities of spontaneous dialogs w.r.t. written text, which motivated some changes in the sense hierarchy. Then, we present corpus statistics about the discourse relations within a representative set of annotated dialogs.},
keywords = {Discourse, Natural Language Processing, Signal Annotation and Interpretation}
}