Add to ORKGInternational audienceAnswering to precise questions requires applying Natural Language techniques in order to locate the answers inside retrieved documents. The QALC system, presented in this paper, participated to the Question Answering track of the TREC8 and TREC9 evaluations. QALC exploits an analysis of documents based on the search for multi-word terms and their variations. These indexes are used to select a minimal number of documents to be processed. This selection is fully significant when applying further time consuming module
This paper describes a question answering system that automatically finds answers to questions in a ...
Open domain question answering (QA) has become a popular research area in recent years. Most current...
Les problématiques abordées dans ma thèse sont de définir une adaptation unifiée entre la sélection ...
International audienceAnswering precise questions requires applying Natural Language techniques in o...
Answering open-domain factual questions requires Natural Language processing for refining document s...
International audienceThe QALC question-answering system, developed at LIMSI, has been a participant...
International audienceIn this report we describe how the QALC system (the Question-Answering program...
We present in this chapter the QALC system which has participated in the four TREC QA evaluations. W...
In Question answering (QA) system retrieves the precise information from large documents according t...
International audienceOur bilingual QA system MUSCLEF is based on QALC, the monolingual system with ...
In the QA and information retrieval domains progress has been assessed via evaluation campaigns(Clef...
In the present paper, we describe the improvement of our Question Answering System (QAS). We added k...
Question Answering System could automatically provide an answer to a question posed by human in natu...
This paper describes the retrieval experiments for the main task and list task of the TREC-10 questi...
The Question Answering (QA) task consists of providing short, relevant answers to natural language q...
This paper describes a question answering system that automatically finds answers to questions in a ...
Open domain question answering (QA) has become a popular research area in recent years. Most current...
Les problématiques abordées dans ma thèse sont de définir une adaptation unifiée entre la sélection ...
International audienceAnswering precise questions requires applying Natural Language techniques in o...
Answering open-domain factual questions requires Natural Language processing for refining document s...
International audienceThe QALC question-answering system, developed at LIMSI, has been a participant...
International audienceIn this report we describe how the QALC system (the Question-Answering program...
We present in this chapter the QALC system which has participated in the four TREC QA evaluations. W...
In Question answering (QA) system retrieves the precise information from large documents according t...
International audienceOur bilingual QA system MUSCLEF is based on QALC, the monolingual system with ...
In the QA and information retrieval domains progress has been assessed via evaluation campaigns(Clef...
In the present paper, we describe the improvement of our Question Answering System (QAS). We added k...
Question Answering System could automatically provide an answer to a question posed by human in natu...
This paper describes the retrieval experiments for the main task and list task of the TREC-10 questi...
The Question Answering (QA) task consists of providing short, relevant answers to natural language q...
This paper describes a question answering system that automatically finds answers to questions in a ...
Open domain question answering (QA) has become a popular research area in recent years. Most current...
Les problématiques abordées dans ma thèse sont de définir une adaptation unifiée entre la sélection ...