Predicting the Semantic Textual Similarity with Siamese CNN and LSTM

National audienceSemantic Textual Similarity (STS) is the basis of many applications in Natural Language Processing (NLP). Our system combines convolution and recurrent neural networks to measure the semantic similarity of sentences. It uses a convolution network to take account of the local context of words and an LSTM to consider the global context of sentences. This combination of networks helps to preserve the relevant information of sentences and improves the calculation of the similarity between sentences. Our model has achieved good results and is competitive with the best state-of-the-art systems.La Similarité Textuelle Sémantique (STS) est la base de nombreuses applications dans le Traitement Automatique du Langage Naturel (TALN). Notre système combine des réseaux neuronaux convolutifs et récurrents pour mesurer la similarité sémantique des phrases. Il utilise un réseau convolutif pour tenir compte du contexte local des mots et un LSTM pour prendre en considération le contexte global d'une phrase. Cette combinaison des réseaux préserve mieux les informations significatives des phrases et améliore le calcul de la similarité entre les phrases. Notre modèle a obtenu de bons résultats et est compétitif avec les meilleurs systèmes de l'état de l'art

Query Expansion by Local Context Analysis

International audienceQuery expansion (QE) aims at improving information retrieval (IR) effectiveness by enhancing the query formulation. Because users' queries are generally short and because of the language ambiguity, some information needs are difficult to answer. Query reformulation and QE methods have been developed to face this issue. Relevance feedback (RF) is one of the most popular QE techniques. In its manual version, the system uses the information on the relevance -manually judged- of retrieved documents in order to expand the initial query. Rather than using users' judgment on the document relevance, blind RF considers the first retrieved documents as relevant. Generally speaking, RF methods consider the terms that cooccur with query terms within positive feedback documents as candidates for the expansion. Rather than considering feedback documents in their all, it is possible to analyze local information. This paper presents a new method that uses local context from feedback documents for QE. The method uses POS information as well as the remoteness from query terms within feedback documents. We show that the method significantly improves precision on TREC collections