Papers by Andrei Popescu-belis
Psycho-oncology, 2010
Résumé: This paper describes an ISO project which aims at developing a standard for annotating sp... more Résumé: This paper describes an ISO project which aims at developing a standard for annotating spoken and multimodal dialogue with semantic information concerning the communicative functions of utterances, the kind of semantic content they address, and their relations with what was said and done earlier in the dialogue. The project, ISO 24617-2" Semantic annotation framework, Part 2: Dialogue acts", is currently at DIS stage. The proposed annotation schema distinguishes 9 orthogonal dimensions, allowing each ...
This paper analyzes the results of the French MT Evaluation Campaign, CESTA (2003-2006). The deta... more This paper analyzes the results of the French MT Evaluation Campaign, CESTA (2003-2006). The details of the campaign are first briefly described. The paper then focuses on the results of the two runs, which used human metrics, such as fluency or adequacy, as well as automated metrics, mainly based on n-gram comparison and word error rates. The results show that
This report describes the user requirements elicitation process and presents a number of user int... more This report describes the user requirements elicitation process and presents a number of user interface concepts for the proposed concept of a meeting assistant. The report both addresses the active role the meeting assistant can play, and how it can support remote meeting aspects. It starts with describing the method that has been followed during the user requirements work. Next,
Psycho-oncology, 2010
This paper describes an ISO project developing an international standard for annotating dialogue ... more This paper describes an ISO project developing an international standard for annotating dialogue with semantic information, in particular concerning the communicative functions of the utterances, the kind of content they address, and the dependency relations to what was said and done earlier in the dialogue. The project, registered as ISO 24617-2 Semantic annotation framework, Part 2: Dialogue acts", is currently at DIS stage.
ABSTRACT The Automatic Content Linking Device (ACLD) is a just-in-time retrieval system that moni... more ABSTRACT The Automatic Content Linking Device (ACLD) is a just-in-time retrieval system that monitors an ongoing conversation or a monologue and enriches it with potentially related documents, including transcripts of past meetings, from local repositories or from the Internet. The linked content is displayed in real-time to the participants in the conversation, or to users watching a recorded conversation or talk. The system can be demonstrated in both settings, using real-time automatic speech recognition (ASR) or replaying offline ASR, via a flexible user interface that displays results and provides access to the content of past meetings and documents.
This paper introduces a new dataset and compares several methods for the recommendation of non-fi... more This paper introduces a new dataset and compares several methods for the recommendation of non-fiction audio visual material, namely lectures from the TED website. The TED dataset contains 1,149 talks and 69,023 profiles of users, who have made more than 100,000 ratings and 200,000 comments. The corresponding metadata, which we make available, can be used for training and testing generic or personalized recommender systems. We define content-based, collaborative, and combined recommendation methods for TED lectures and use cross-validation to select the best parameters of keyword-based (TFIDF) and semantic vector space-based methods (LSI, LDA, RP, and ESA). We compare these methods on a personalized recommendation task in two settings, a cold-start and a non-cold-start one. In the cold-start setting, semantic vector spaces perform better than keywords. In the noncold-start setting, where collaborative information can be exploited, content-based methods are outperformed by collaborative filtering ones, but the proposed combined method shows acceptable performances, and can be used in both settings. For the generic recommendation task, LSI and RP again outperform TF-IDF.
ABSTRACT This paper addresses the problem of keyword extraction from conversations, with the goal... more ABSTRACT This paper addresses the problem of keyword extraction from conversations, with the goal of using these keywords to retrieve, for each short conversation fragment, a small number of potentially relevant documents, which can be recommended to participants. However, even a short fragment contains a variety of words, which are potentially related to several topics; moreover, using an automatic speech recognition (ASR) system introduces errors among them. Therefore, it is difficult to infer precisely the information needs of the conversation participants. We first propose an algorithm to extract keywords from the output of an ASR system (or a manual transcript for testing), which makes use of topic modeling techniques and of a submodular reward function which favors diversity in the keyword set, to match the potential diversity of topics and reduce ASR noise. Then, we propose a method to derive multiple topically separated queries from this keyword set, in order to maximize the chances of making at least one relevant recommendation when using these queries to search over the English Wikipedia. The proposed methods are evaluated in terms of relevance with respect to conversation fragments from the Fisher, AMI, and ELEA conversational corpora, rated by several human judges. The scores show that our proposal improves over previous methods that consider only word frequency or topic similarity, and represents a promising solution for a document recommender system to be used in conversations.
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Papers by Andrei Popescu-belis