Utilisation de la syntaxe pour valider les réponses à des questions par plusieurs documents.

National audienceCet article présente FIDJI, un système de questions-réponses pour le français, combinant des informations syntaxiques sur la question et les documents avec des techniques plus traditionnelles du domaine, telles que la reconnaissance des entités nommées et la pondération des termes. Nous expérimentons notament dans ce système la validation des réponses dans plusieurs documents, ainsi que des techniques spécifiques permettant de répondre à différents types de questions (comme les questions attendant des réponses multiples (liste) ou une réponse booléenne)

Finding answers to questions, in text collections or web, in open domain or specialty domains

International audienceThis chapter is dedicated to factual question answering, i.e. extracting precise and exact answers to question given in natural language from texts. A question in natural language gives more information than a bag of word query (i.e. a query made of a list of words), and provides clues for finding precise answers. We will first focus on the presentation of the underlying problems mainly due to the existence of linguistic variations between questions and their answerable pieces of texts for selecting relevant passages and extracting reliable answers. We will first present how to answer factual question in open domain. We will also present answering questions in specialty domain as it requires dealing with semi-structured knowledge and specialized terminologies, and can lead to different applications, as information management in corporations for example. Searching answers on the Web constitutes another application frame and introduces specificities linked to Web redundancy or collaborative usage. Besides, the Web is also multilingual, and a challenging problem consists in searching answers in target language documents other than the source language of the question. For all these topics, we present main approaches and the remaining problems

Selecting answers to questions from Web documents by a robust validation process

International audienceQuestion answering (QA) systems aim at finding answers to question posed in natural language using a collection of documents. When the collection is extracted from the Web, the structure and style of the texts are quite different from those of newspaper articles. We developed a QA system based on an answer validation process able to handle Web specificity. A large number of candidate answers are extracted from short passages in order to be validated according to question and passages characteristics. The validation module is based on a machine learning approach. It takes into account criteria characterizing both the passage and answer relevance at the surface, lexical, syntactic and semantic levels to deal with different types of texts. We present and compare results obtained for factual questions posed on a Web and on a newspaper collection. We show that our system outperforms a baseline by up to 48% in MRR

Boosting Chinese Question Answering with Two Lightweight Methods: ABSPs and SCO-QAT

[[abstract]]Question Answering (QA) research has been conducted in many languages. Nearly all the top performing systems use heavy methods that require sophisticated techniques, such as parsers or logic provers. However, such techniques are usually unavailable or unaffordable for under-resourced languages or in resource-limited situations. In this article, we describe how a top-performing Chinese QA system can be designed by using lightweight methods effectively. We propose two lightweight methods, namely the Sum of Co-occurrences of Question and Answer Terms (SCO-QAT) and Alignment-based Surface Patterns (ABSPs). SCO-QAT is a co-occurrence-based answer-ranking method that does not need extra knowledge, word-ignoring heuristic rules, or tools. It calculates co-occurrence scores based on the passage retrieval results. ABSPs are syntactic patterns trained from question-answer pairs with a multiple alignment algorithm. They are used to capture the relations between terms and then use the relations to filter answers. We attribute the success of the ABSPs and SCO-QAT methods to the effective use of local syntactic information and global co-occurrence information. By using SCO-QAT and ABSPs, we improved the RU-Accuracy of our testbed QA system, ASQA, from 0.445 to 0.535 on the NTCIR-5 dataset. It also achieved the top 0.5 RU-Accuracy on the NTCIR-6 dataset. The result shows that lightweight methods are not only cheaper to implement, but also have the potential to achieve state-of-the-art performances.[[notice]]補正完畢[[journaltype]]國外[[incitationindex]]E

Improvements to GeoQA, a Question Answering system for Geospatial Questions

Η παρούσα εργασία αποτελεί μια προσπάθεια για συγκέντρωση, μελέτη και σύγκριση συστημάτων απάντησης ερωτήσεων όπως τα QUINT, TEMPO και NEQA και του σκελετού συστημάτων απάντησης ερωτήσεων Frankenstein. Η μελέτη επικεντρώνεται στην απάντηση ερωτήσεων σε γεωχωρικά δεδομένα και πιο στο σύστημα GeoQA. Το σύστημα αυτό έχει προταθεί πρόσφατα και ειναι το πρώτο σύστημα απάντησης ερωτήσεων πάνω σε συνδεδεμένα γεωχωρικά δεδομένα βασιζόμενο σε πρότυπα. Βελτιώνουμε το παραπάνω σύστημα χρησιμοποιώντας τα δεδομένα για το σχήμα των βάσεων γνώσης του, προσθέτοντας πρότυπα για πιο σύνθετες ερωτήσεις και αναπτύσσοντας το υποσύστημα για την επεξεργασία φυσικής γλώσσας.We study the question-answering GeoQA which was proposed recently. GeoQA is the first template-based question answering system for linked geospatial data. We improve this system by exploiting the data schema information of the kb’s it’s using, adding more templates for more complex queries and by improving the natural language processing module in order to recognize the patterns. The current work is also an attempt to concentrate, study and compare some other question-answering systems like QUINT, Qanary methodology and Frankenstein framework for question answering systems

RECUPERACIÓN DE PASAJES EN TEXTOS LEGALES Y PATENTES MULTILINGÜES

En este trabajo se expone: la problemática de la recuperación de pasajes, el dominio de los textos legales y las patentes y su característica de diversidad idiomática. Se presentan técnicas para solucionar problemas de recuperación de información y se analizan dos participaciones en competencias con prepuestas de enfoques novedosos.Correa García, S. (2010). RECUPERACIÓN DE PASAJES EN TEXTOS LEGALES Y PATENTES MULTILINGÜES. http://hdl.handle.net/10251/14084Archivo delegad

Arabic named entity recognition

En esta tesis doctoral se describen las investigaciones realizadas con el objetivo de determinar las mejores tecnicas para construir un Reconocedor de Entidades Nombradas en Arabe. Tal sistema tendria la habilidad de identificar y clasificar las entidades nombradas que se encuentran en un texto arabe de dominio abierto. La tarea de Reconocimiento de Entidades Nombradas (REN) ayuda a otras tareas de Procesamiento del Lenguaje Natural (por ejemplo, la Recuperacion de Informacion, la Busqueda de Respuestas, la Traduccion Automatica, etc.) a lograr mejores resultados gracias al enriquecimiento que a~nade al texto. En la literatura existen diversos trabajos que investigan la tarea de REN para un idioma especifico o desde una perspectiva independiente del lenguaje. Sin embargo, hasta el momento, se han publicado muy pocos trabajos que estudien dicha tarea para el arabe. El arabe tiene una ortografia especial y una morfologia compleja, estos aspectos aportan nuevos desafios para la investigacion en la tarea de REN. Una investigacion completa del REN para elarabe no solo aportaria las tecnicas necesarias para conseguir un alto rendimiento, sino que tambien proporcionara un analisis de los errores y una discusion sobre los resultados que benefician a la comunidad de investigadores del REN. El objetivo principal de esta tesis es satisfacer esa necesidad. Para ello hemos: 1. Elaborado un estudio de los diferentes aspectos del arabe relacionados con dicha tarea; 2. Analizado el estado del arte del REN; 3. Llevado a cabo una comparativa de los resultados obtenidos por diferentes tecnicas de aprendizaje automatico; 4. Desarrollado un metodo basado en la combinacion de diferentes clasificadores, donde cada clasificador trata con una sola clase de entidades nombradas y emplea el conjunto de caracteristicas y la tecnica de aprendizaje automatico mas adecuados para la clase de entidades nombradas en cuestion. Nuestros experimentos han sido evaluados sobre nueve conjuntos de test.Benajiba, Y. (2009). Arabic named entity recognition [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8318Palanci

Questions-Réponses en domaine ouvert (sélection pertinente de documents en fonction du contexte de la question)

Les problématiques abordées dans ma thèse sont de définir une adaptation unifiée entre la sélection des documents et les stratégies de recherche de la réponse à partir du type des documents et de celui des questions, intégrer la solution au système de Questions-Réponses (QR) RITEL du LIMSI et évaluer son apport. Nous développons et étudions une méthode basée sur une approche de Recherche d Information pour la sélection de documents en QR. Celle-ci s appuie sur un modèle de langue et un modèle de classification binaire de texte en catégorie pertinent ou non pertinent d un point de vue QR. Cette méthode permet de filtrer les documents sélectionnés pour l extraction de réponses par un système QR. Nous présentons la méthode et ses modèles, et la testons dans le cadre QR à l aide de RITEL. L évaluation est faite en français en contexte web sur un corpus de 500 000 pages web et de questions factuelles fournis par le programme Quaero. Celle-ci est menée soit sur des documents complets, soit sur des segments de documents. L hypothèse suivie est que le contenu informationnel des segments est plus cohérent et facilite l extraction de réponses. Dans le premier cas, les gains obtenus sont faibles comparés aux résultats de référence (sans filtrage). Dans le second cas, les gains sont plus élevés et confortent l hypothèse, sans pour autant être significatifs. Une étude approfondie des liens existant entre les performances de RITEL et les paramètres de filtrage complète ces évaluations. Le système de segmentation créé pour travailler sur des segments est détaillé et évalué. Son évaluation nous sert à mesurer l impact de la variabilité naturelle des pages web (en taille et en contenu) sur la tâche QR, en lien avec l hypothèse précédente. En général, les résultats expérimentaux obtenus suggèrent que notre méthode aide un système QR dans sa tâche. Cependant, de nouvelles évaluations sont à mener pour rendre ces résultats significatifs, et notamment en utilisant des corpus de questions plus importants.This thesis aims at defining a unified adaptation of the document selection and answer extraction strategies, based on the document and question types, in a Question-Answering (QA) context. The solution is integrated in RITEL (a LIMSI QA system) to assess the contribution. We develop and investigate a method based on an Information Retrieval approach for the selection of relevant documents in QA. The method is based on a language model and a binary model of textual classification in relevant or irrelevant category. It is used to filter unusable documents for answer extraction by matching lists of a priori relevant documents to the question type automatically. First, we present the method along with its underlying models and we evaluate it on the QA task with RITEL in French. The evaluation is done on a corpus of 500,000 unsegmented web pages with factoid questions provided by the Quaero program (i.e. evaluation at the document level or D-level). Then, we evaluate the methodon segmented web pages (i.e. evaluation at the segment level or S-level). The idea is that information content is more consistent with segments, which facilitates answer extraction. D-filtering brings a small improvement over the baseline (no filtering). S-filtering outperforms both the baseline and D-filtering but not significantly. Finally, we study at the S-level the links between RITEL s performances and the key parameters of the method. In order to apply the method on segments, we created a system of web page segmentation. We present and evaluate it on the QA task with the same corpora used to evaluate our document selection method. This evaluation follows the former hypothesis and measures the impact of natural web page variability (in terms of size and content) on RITEL in its task. In general, the experimental results we obtained suggest that our IR-based method helps a QA system in its task, however further investigations should be conducted especially with larger corpora of questions to make them significant.PARIS11-SCD-Bib. électronique (914719901) / SudocSudocFranceF

Methods for Answer Extraction in Textual Question Answering

In this thesis we present and evaluate two pattern matching based methods for answer extraction in textual question answering systems. A textual question answering system is a system that seeks answers to natural language questions from unstructured text. Textual question answering systems are an important research problem because as the amount of natural language text in digital format grows all the time, the need for novel methods for pinpointing important knowledge from the vast textual databases becomes more and more urgent. We concentrate on developing methods for the automatic creation of answer extraction patterns. A new type of extraction pattern is developed also. The pattern matching based approach chosen is interesting because of its language and application independence. The answer extraction methods are developed in the framework of our own question answering system. Publicly available datasets in English are used as training and evaluation data for the methods. The techniques developed are based on the well known methods of sequence alignment and hierarchical clustering. The similarity metric used is based on edit distance. The main conclusions of the research are that answer extraction patterns consisting of the most important words of the question and of the following information extracted from the answer context: plain words, part-of-speech tags, punctuation marks and capitalization patterns, can be used in the answer extraction module of a question answering system. This type of patterns and the two new methods for generating answer extraction patterns provide average results when compared to those produced by other systems using the same dataset. However, most answer extraction methods in the question answering systems tested with the same dataset are both hand crafted and based on a system-specific and fine-grained question classification. The the new methods developed in this thesis require no manual creation of answer extraction patterns. As a source of knowledge, they require a dataset of sample questions and answers, as well as a set of text documents that contain answers to most of the questions. The question classification used in the training data is a standard one and provided already in the publicly available data.Tekstuaalinen kysymysvastausjärjestelmä on tietokoneohjelma, joka vastaa käyttäjän esittämiin kysymyksiin tekstidokumenteista eristämillään vastauksilla. Tekstuaaliset kysymysvastausjärjestelmät ovat tärkeä tutkimusongelma, sillä digitaalisessa muodossa olevien tekstidokumenttien määrä lisääntyy jatkuvasti. Samalla kasvaa myös sellaisten tiedonhakumenetelmien tarve, joiden avulla käyttäjä löytää tekstidokumenteista olleellisen tiedon nopeasti ja helposti. Kysymysvastausjärjestelmiä on tutkittu jo 1960-luvulta alkaen. Ensimmäiset järjestelmät osasivat vastata suppeaan joukkoon määrämuotoisia kysymyksiä, jotka koskivat jotakin tarkasti rajattua aihepiiriä kuten pesäpallotuloksia. Nykyään kysymysvastausjärjestelmien tutkimuksessa keskitytään järjestelmiin, joissa kysymykset voivat olla melko vapaasti muotoiltuja ja ne voivat liittyä mihin tahansa aihepiiriin. Nykyjärjestelmissä tiedonhaku kohdistuu usein laajoihin tekstidokumenttikokoelmiin kuten WWW:hen ja sanomalehtien uutisarkistoihin. Toisaalta myös rajatun aihepiirin järjestelmät ovat yhä tärkeä tutkimuskohde. Käytännön esimerkkejä rajatun aihepiirin järjestelmistä ovat yritysten asiakaspalvelua helpottavat järjestelmät. Nämä järjestelmät käsittelevät automaattisesti osan asiakkaiden yritykselle osoittamista kysymyksistä tai toimivat asiakasneuvojan apuvälineenä hänen etsiessään tietoa asiakkaan kysymykseen. Tässä väitöskirjassa kehitetyt menetelmät ovat sovellettavissa sekä avoimen että rajatun aihepiirin kysymysvastausjärjestelmiin. Väitöskirjassa on kehitetty kaksi uutta menetelmää vastausten eristämiseksi tekstistä ja tekstuaalinen kysymysvastausjärjestelmä, joka käyttää molempia menetelmiä. Menetelmät on arvioitu julkisesti saatavilla olevalla testidatalla. Väitöskirjassa kehitetyt vastauksen eristämismenetelmät ovat oppivia. Oppivuudella tarkoitetaan sitä, että vastausten eristämiseen käytettäviä hahmoja ei tarvitse ohjelmoida, vaan ne tuotetaan automaattisesti esimerkkidatan perusteella. Oppivuudella tehostetaan uusien kysymysvastausjärjestelmien kehittämistä. Tehokas järjestelmäkehitys on erityisen tärkeää silloin kun järjestelmästä tarvitaan useita kieliversioita. Myös uusien kysymys- ja tekstityyppien lisääminen järjestelmään helpottuu oppivan menetelmän ansiosta