Validation syntaxique de relations sémantiques pour la RI

National audienceAvec l'objectif d'améliorer la précision des systèmes de recherche d'information, c'est-à-dire les premiers résultats retrouvés par le système, des travaux se sont basés sur des indexations structurées des documents, à base d'arbres ou de graphes. La plupart de ces travaux utilisent comme index des structures uniques et certaines. Les décisions qui ont amené à la sélection de certaines informations lors de la création de la structure à partir du texte ne sont plus disponibles et ne sont pas utilisées. Ce type d'information nous parait pourtant essentiel pour obtenir des résultats précis. Nous proposons ici une méthode permettant de donner un poids d'extraction à des relations sémantiques à partir des éléments syntaxiques qui les composent dans le texte. Pour valider ce poids, nous intégrerons cette pondération dans un modèle de recherche d'information basé sur des graphes de concepts et nous évaluerons ce modèle sur la collection CLEF-Image 2005

Hashing based Answer Selection

Answer selection is an important subtask of question answering (QA), where deep models usually achieve better performance. Most deep models adopt question-answer interaction mechanisms, such as attention, to get vector representations for answers. When these interaction based deep models are deployed for online prediction, the representations of all answers need to be recalculated for each question. This procedure is time-consuming for deep models with complex encoders like BERT which usually have better accuracy than simple encoders. One possible solution is to store the matrix representation (encoder output) of each answer in memory to avoid recalculation. But this will bring large memory cost. In this paper, we propose a novel method, called hashing based answer selection (HAS), to tackle this problem. HAS adopts a hashing strategy to learn a binary matrix representation for each answer, which can dramatically reduce the memory cost for storing the matrix representations of answers. Hence, HAS can adopt complex encoders like BERT in the model, but the online prediction of HAS is still fast with a low memory cost. Experimental results on three popular answer selection datasets show that HAS can outperform existing models to achieve state-of-the-art performance

A Factoid Question Answering System for Vietnamese

In this paper, we describe the development of an end-to-end factoid question answering system for the Vietnamese language. This system combines both statistical models and ontology-based methods in a chain of processing modules to provide high-quality mappings from natural language text to entities. We present the challenges in the development of such an intelligent user interface for an isolating language like Vietnamese and show that techniques developed for inflectional languages cannot be applied "as is". Our question answering system can answer a wide range of general knowledge questions with promising accuracy on a test set.Comment: In the proceedings of the HQA'18 workshop, The Web Conference Companion, Lyon, Franc

Intégration de connaissances syntaxiques dans les modèles de langue pour la RI

International audienceEn Recherche d'Information (RI) les méthodes purement statistiques basées sur des distributions de mots-clef ont actuellement atteint une limite. Cette limite n'est franchissable que par l'apport massif de connaissances extérieures au sein du système de RI. Nos travaux portent sur l'utilisation en RI des liens de niveaux syntaxiques entre les termes. Nous considérons ainsi les dépendances syntaxiques contenues dans l'arbre de dépendance produit par des analyseurs syntaxiques de surface. Pour intégrer ces informations en RI, le contexte des modèles de langue nous semble favorable. En effet, l'aspect théorique des modèles de langue est très intéressant, il est adaptable et permet l'intégration de nouvelles connaissances. Nous présentons ici, l'intégration des liens syntaxiques au sein d'un modèle de langue. Ce modèle est évalué sur une partie de la collection de CLEF. Les résultats montrent que l'intégration des dépendances syntaxiques abaisse les performances du système de RI. Face à ces résultats, nous souhaitons pour la suite de ces travaux nous orienter vers l'apport d'information de niveau plus sémantique

The State-of-the-arts in Focused Search

The continuous influx of various text data on the Web requires search engines to improve their retrieval abilities for more specific information. The need for relevant results to a user’s topic of interest has gone beyond search for domain or type specific documents to more focused result (e.g. document fragments or answers to a query). The introduction of XML provides a format standard for data representation, storage, and exchange. It helps focused search to be carried out at different granularities of a structured document with XML markups. This report aims at reviewing the state-of-the-arts in focused search, particularly techniques for topic-specific document retrieval, passage retrieval, XML retrieval, and entity ranking. It is concluded with highlight of open problems

Review Paper on Answers Selection and Recommendation in Community Question Answers System

Nowadays, question answering system is more convenient for the users, users ask question online and then they will get the answer of that question, but as browsing is primary need for each an individual, the number of users ask question and system will provide answer but the computation time increased as well as waiting time increased and same type of questions are asked by different users, system need to give same answers repeatedly to different users. To avoid this we propose PLANE technique which may quantitatively rank answer candidates from the relevant question pool. If users ask any question, then system provide answers in ranking form, then system recommend highest rank answer to the user. We proposing expert recommendation system, an expert will provide answer of the question which is asked by the user and we also implement sentence level clustering technique in which a single question have multiple answers, system provide most suitable answer to the question which is asked by the user