Web 2.0, language resources and standards to automatically build a multilingual named entity lexicon

This paper proposes to advance in the current state-of-the-art of automatic Language Resource (LR) building by taking into consideration three elements: (i) the knowledge available in existing LRs, (ii) the vast amount of information available from the collaborative paradigm that has emerged from the Web 2.0 and (iii) the use of standards to improve interoperability. We present a case study in which a set of LRs for different languages (WordNet for English and Spanish and Parole-Simple-Clips for Italian) are extended with Named Entities (NE) by exploiting Wikipedia and the aforementioned LRs. The practical result is a multilingual NE lexicon connected to these LRs and to two ontologies: SUMO and SIMPLE. Furthermore, the paper addresses an important problem which affects the Computational Linguistics area in the present, interoperability, by making use of the ISO LMF standard to encode this lexicon. The different steps of the procedure (mapping, disambiguation, extraction, NE identification and postprocessing) are comprehensively explained and evaluated. The resulting resource contains 974,567, 137,583 and 125,806 NEs for English, Spanish and Italian respectively. Finally, in order to check the usefulness of the constructed resource, we apply it into a state-of-the-art Question Answering system and evaluate its impact; the NE lexicon improves the system’s accuracy by 28.1%. Compared to previous approaches to build NE repositories, the current proposal represents a step forward in terms of automation, language independence, amount of NEs acquired and richness of the information represented

Encyclopaedic question answering

Open-domain question answering (QA) is an established NLP task which enables users to search for speciVc pieces of information in large collections of texts. Instead of using keyword-based queries and a standard information retrieval engine, QA systems allow the use of natural language questions and return the exact answer (or a list of plausible answers) with supporting snippets of text. In the past decade, open-domain QA research has been dominated by evaluation fora such as TREC and CLEF, where shallow techniques relying on information redundancy have achieved very good performance. However, this performance is generally limited to simple factoid and deVnition questions because the answer is usually explicitly present in the document collection. Current approaches are much less successful in Vnding implicit answers and are diXcult to adapt to more complex question types which are likely to be posed by users. In order to advance the Veld of QA, this thesis proposes a shift in focus from simple factoid questions to encyclopaedic questions: list questions composed of several constraints. These questions have more than one correct answer which usually cannot be extracted from one small snippet of text. To correctly interpret the question, systems need to combine classic knowledge-based approaches with advanced NLP techniques. To Vnd and extract answers, systems need to aggregate atomic facts from heterogeneous sources as opposed to simply relying on keyword-based similarity. Encyclopaedic questions promote QA systems which use basic reasoning, making them more robust and easier to extend with new types of constraints and new types of questions. A novel semantic architecture is proposed which represents a paradigm shift in open-domain QA system design, using semantic concepts and knowledge representation instead of words and information retrieval. The architecture consists of two phases, analysis – responsible for interpreting questions and Vnding answers, and feedback – responsible for interacting with the user. This architecture provides the basis for EQUAL, a semantic QA system developed as part of the thesis, which uses Wikipedia as a source of world knowledge and iii employs simple forms of open-domain inference to answer encyclopaedic questions. EQUAL combines the output of a syntactic parser with semantic information from Wikipedia to analyse questions. To address natural language ambiguity, the system builds several formal interpretations containing the constraints speciVed by the user and addresses each interpretation in parallel. To Vnd answers, the system then tests these constraints individually for each candidate answer, considering information from diUerent documents and/or sources. The correctness of an answer is not proved using a logical formalism, instead a conVdence-based measure is employed. This measure reWects the validation of constraints from raw natural language, automatically extracted entities, relations and available structured and semi-structured knowledge from Wikipedia and the Semantic Web. When searching for and validating answers, EQUAL uses the Wikipedia link graph to Vnd relevant information. This method achieves good precision and allows only pages of a certain type to be considered, but is aUected by the incompleteness of the existing markup targeted towards human readers. In order to address this, a semantic analysis module which disambiguates entities is developed to enrich Wikipedia articles with additional links to other pages. The module increases recall, enabling the system to rely more on the link structure of Wikipedia than on word-based similarity between pages. It also allows authoritative information from diUerent sources to be linked to the encyclopaedia, further enhancing the coverage of the system. The viability of the proposed approach was evaluated in an independent setting by participating in two competitions at CLEF 2008 and 2009. In both competitions, EQUAL outperformed standard textual QA systems as well as semi-automatic approaches. Having established a feasible way forward for the design of open-domain QA systems, future work will attempt to further improve performance to take advantage of recent advances in information extraction and knowledge representation, as well as by experimenting with formal reasoning and inferencing capabilities.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Knowledge-based and data-driven approaches for geographical information access

Geographical Information Access (GeoIA) can be defined as a way of retrieving information from textual collections that includes the automatic analysis and interpretation of the geographical constraints and terms present in queries and documents. This PhD thesis presents, describes and evaluates several heterogeneous approaches for the following three GeoIA tasks: Geographical Information Retrieval (GIR), Geographical Question Answering (GeoQA), and Textual Georeferencing (TG). The GIR task deals with user queries that search over documents (e.g. ¿vineyards in California?) and the GeoQA task treats questions that retrieve answers (e.g. ¿What is the capital of France?). On the other hand, TG is the task of associate one or more georeferences (such as polygons or coordinates in a geodetic reference system) to electronic documents. Current state-of-the-art AI algorithms are not yet fully understanding the semantic meaning and the geographical constraints and terms present in queries and document collections. This thesis attempts to improve the effectiveness results of GeoIA tasks by: 1) improving the detection, understanding, and use of a part of the geographical and the thematic content of queries and documents with Toponym Recognition, Toponym Disambiguation and Natural Language Processing (NLP) techniques, and 2) combining Geographical Knowledge-Based Heuristics based on common sense with Data-Driven IR algorithms. The main contributions of this thesis to the state-of-the-art of GeoIA tasks are: 1) The presentation of 10 novel approaches for GeoIA tasks: 3 approaches for GIR, 3 for GeoQA, and 4 for Textual Georeferencing (TG). 2) The evaluation of these novel approaches in these contexts: within official evaluation benchmarks, after evaluation benchmarks with the test collections, and with other specific datasets. Most of these algorithms have been evaluated in international evaluations and some of them achieved top-ranked state-of-the-art results, including top-performing results in GIR (GeoCLEF 2007) and TG (MediaEval 2014) benchmarks. 3) The experiments reported in this PhD thesis show that the approaches can combine effectively Geographical Knowledge and NLP with Data-Driven techniques to improve the efectiveness measures of the three Geographical Information Access tasks investigated. 4) TALPGeoIR: a novel GIR approach that combines Geographical Knowledge ReRanking (GeoKR), NLP and Relevance Feedback (RF) that achieved state-of-the-art results in official GeoCLEF benchmarks (Ferrés and Rodríguez, 2008; Mandl et al., 2008) and posterior experiments (Ferrés and Rodríguez, 2015a). This approach has been evaluated with the full GeoCLEF corpus (100 topics) and showed that GeoKR, NLP, and RF techniques evaluated separately or in combination improve the results in MAP and R-Precision effectiveness measures of the state-of-the-art IR algorithms TF-IDF, BM25 and InL2 and show statistical significance in most of the experiments. 5) GeoTALP-QA: a scope-based GeoQA approach for Spanish and English and its evaluation with a set of questions of the Spanish geography (Ferrés and Rodríguez, 2006). 6) Four state-of-the-art Textual Georeferencing approaches for informal and formal documents that achieved state-of-the-art results in evaluation benchmarks (Ferrés and Rodríguez, 2014) and posterior experiments (Ferrés and Rodríguez, 2011; Ferrés and Rodríguez, 2015b).L'Accés a la Informació Geogràfica (GeoAI) pot ser definit com una forma de recuperar informació de col·lecions textuals que inclou l'anàlisi automàtic i la interpretació dels termes i restriccions geogràfiques que apareixen en consultes i documents. Aquesta tesi doctoral presenta, descriu i avalua varies aproximacions heterogènies a les seguents tasques de GeoAI: Recuperació de la Informació Geogràfica (RIG), Cerca de la Resposta Geogràfica (GeoCR), i Georeferenciament Textual (GT). La tasca de RIG tracta amb consultes d'usuari que cerquen documents (e.g. ¿vinyes a California?) i la tasca GeoCR tracta de recuperar respostes concretes a preguntes (e.g. ¿Quina és la capital de França?). D'altra banda, GT es la tasca de relacionar una o més referències geogràfiques (com polígons o coordenades en un sistema de referència geodètic) a documents electrònics. Els algoritmes de l'estat de l'art actual en Intel·ligència Artificial encara no comprenen completament el significat semàntic i els termes i les restriccions geogràfiques presents en consultes i col·leccions de documents. Aquesta tesi intenta millorar els resultats en efectivitat de les tasques de GeoAI de la seguent manera: 1) millorant la detecció, comprensió, i la utilització d'una part del contingut geogràfic i temàtic de les consultes i documents amb tècniques de reconeixement de topònims, desambiguació de topònims, i Processament del Llenguatge Natural (PLN), i 2) combinant heurístics basats en Coneixement Geogràfic i en el sentit comú humà amb algoritmes de Recuperació de la Informació basats en dades. Les principals contribucions d'aquesta tesi a l'estat de l'art de les tasques de GeoAI són: 1) La presentació de 10 noves aproximacions a les tasques de GeoAI: 3 aproximacions per RIG, 3 per GeoCR, i 4 per Georeferenciament Textual (GT). 2) L'avaluació d'aquestes noves aproximacions en aquests contexts: en el marc d'avaluacions comparatives internacionals, posteriorment a avaluacions comparatives internacionals amb les col·lections de test, i amb altres conjunts de dades específics. La majoria d'aquests algoritmes han estat avaluats en avaluacions comparatives internacionals i alguns d'ells aconseguiren alguns dels millors resultats en l'estat de l'art, com per exemple els resultats en comparatives de RIG (GeoCLEF 2007) i GT (MediaEval 2014). 3) Els experiments descrits en aquesta tesi mostren que les aproximacions poden combinar coneixement geogràfic i PLN amb tècniques basades en dades per millorar les mesures d'efectivitat en les tres tasques de l'Accés a la Informació Geogràfica investigades. 4) TALPGeoIR: una nova aproximació a la RIG que combina Re-Ranking amb Coneixement Geogràfic (GeoKR), PLN i Retroalimentació de Rellevancia (RR) que aconseguí resultats en l'estat de l'art en comparatives oficials GeoCLEF (Ferrés and Rodríguez, 2008; Mandl et al., 2008) i en experiments posteriors (Ferrés and Rodríguez, 2015a). Aquesta aproximació ha estat avaluada amb el conjunt complert del corpus GeoCLEF (100 topics) i ha mostrat que les tècniques GeoKR, PLN i RR avaluades separadament o en combinació milloren els resultats en les mesures efectivitat MAP i R-Precision dels algoritmes de l'estat de l'art en Recuperació de la Infomació TF-IDF, BM25 i InL2 i a més mostren significació estadística en la majoria dels experiments. 5) GeoTALP-QA: una aproximació basada en l'àmbit geogràfic per espanyol i anglès i la seva avaluació amb un conjunt de preguntes de la geografía espanyola (Ferrés and Rodríguez, 2006). 6) Quatre aproximacions per al georeferenciament de documents formals i informals que obtingueren resultats en l'estat de l'art en avaluacions comparatives (Ferrés and Rodríguez, 2014) i en experiments posteriors (Ferrés and Rodríguez, 2011; Ferrés and Rodríguez, 2015b)

Knowledge-based and data-driven approaches for geographical information access

Geographical Information Access (GeoIA) can be defined as a way of retrieving information from textual collections that includes the automatic analysis and interpretation of the geographical constraints and terms present in queries and documents. This PhD thesis presents, describes and evaluates several heterogeneous approaches for the following three GeoIA tasks: Geographical Information Retrieval (GIR), Geographical Question Answering (GeoQA), and Textual Georeferencing (TG). The GIR task deals with user queries that search over documents (e.g. ¿vineyards in California?) and the GeoQA task treats questions that retrieve answers (e.g. ¿What is the capital of France?). On the other hand, TG is the task of associate one or more georeferences (such as polygons or coordinates in a geodetic reference system) to electronic documents. Current state-of-the-art AI algorithms are not yet fully understanding the semantic meaning and the geographical constraints and terms present in queries and document collections. This thesis attempts to improve the effectiveness results of GeoIA tasks by: 1) improving the detection, understanding, and use of a part of the geographical and the thematic content of queries and documents with Toponym Recognition, Toponym Disambiguation and Natural Language Processing (NLP) techniques, and 2) combining Geographical Knowledge-Based Heuristics based on common sense with Data-Driven IR algorithms. The main contributions of this thesis to the state-of-the-art of GeoIA tasks are: 1) The presentation of 10 novel approaches for GeoIA tasks: 3 approaches for GIR, 3 for GeoQA, and 4 for Textual Georeferencing (TG). 2) The evaluation of these novel approaches in these contexts: within official evaluation benchmarks, after evaluation benchmarks with the test collections, and with other specific datasets. Most of these algorithms have been evaluated in international evaluations and some of them achieved top-ranked state-of-the-art results, including top-performing results in GIR (GeoCLEF 2007) and TG (MediaEval 2014) benchmarks. 3) The experiments reported in this PhD thesis show that the approaches can combine effectively Geographical Knowledge and NLP with Data-Driven techniques to improve the efectiveness measures of the three Geographical Information Access tasks investigated. 4) TALPGeoIR: a novel GIR approach that combines Geographical Knowledge ReRanking (GeoKR), NLP and Relevance Feedback (RF) that achieved state-of-the-art results in official GeoCLEF benchmarks (Ferrés and Rodríguez, 2008; Mandl et al., 2008) and posterior experiments (Ferrés and Rodríguez, 2015a). This approach has been evaluated with the full GeoCLEF corpus (100 topics) and showed that GeoKR, NLP, and RF techniques evaluated separately or in combination improve the results in MAP and R-Precision effectiveness measures of the state-of-the-art IR algorithms TF-IDF, BM25 and InL2 and show statistical significance in most of the experiments. 5) GeoTALP-QA: a scope-based GeoQA approach for Spanish and English and its evaluation with a set of questions of the Spanish geography (Ferrés and Rodríguez, 2006). 6) Four state-of-the-art Textual Georeferencing approaches for informal and formal documents that achieved state-of-the-art results in evaluation benchmarks (Ferrés and Rodríguez, 2014) and posterior experiments (Ferrés and Rodríguez, 2011; Ferrés and Rodríguez, 2015b).L'Accés a la Informació Geogràfica (GeoAI) pot ser definit com una forma de recuperar informació de col·lecions textuals que inclou l'anàlisi automàtic i la interpretació dels termes i restriccions geogràfiques que apareixen en consultes i documents. Aquesta tesi doctoral presenta, descriu i avalua varies aproximacions heterogènies a les seguents tasques de GeoAI: Recuperació de la Informació Geogràfica (RIG), Cerca de la Resposta Geogràfica (GeoCR), i Georeferenciament Textual (GT). La tasca de RIG tracta amb consultes d'usuari que cerquen documents (e.g. ¿vinyes a California?) i la tasca GeoCR tracta de recuperar respostes concretes a preguntes (e.g. ¿Quina és la capital de França?). D'altra banda, GT es la tasca de relacionar una o més referències geogràfiques (com polígons o coordenades en un sistema de referència geodètic) a documents electrònics. Els algoritmes de l'estat de l'art actual en Intel·ligència Artificial encara no comprenen completament el significat semàntic i els termes i les restriccions geogràfiques presents en consultes i col·leccions de documents. Aquesta tesi intenta millorar els resultats en efectivitat de les tasques de GeoAI de la seguent manera: 1) millorant la detecció, comprensió, i la utilització d'una part del contingut geogràfic i temàtic de les consultes i documents amb tècniques de reconeixement de topònims, desambiguació de topònims, i Processament del Llenguatge Natural (PLN), i 2) combinant heurístics basats en Coneixement Geogràfic i en el sentit comú humà amb algoritmes de Recuperació de la Informació basats en dades. Les principals contribucions d'aquesta tesi a l'estat de l'art de les tasques de GeoAI són: 1) La presentació de 10 noves aproximacions a les tasques de GeoAI: 3 aproximacions per RIG, 3 per GeoCR, i 4 per Georeferenciament Textual (GT). 2) L'avaluació d'aquestes noves aproximacions en aquests contexts: en el marc d'avaluacions comparatives internacionals, posteriorment a avaluacions comparatives internacionals amb les col·lections de test, i amb altres conjunts de dades específics. La majoria d'aquests algoritmes han estat avaluats en avaluacions comparatives internacionals i alguns d'ells aconseguiren alguns dels millors resultats en l'estat de l'art, com per exemple els resultats en comparatives de RIG (GeoCLEF 2007) i GT (MediaEval 2014). 3) Els experiments descrits en aquesta tesi mostren que les aproximacions poden combinar coneixement geogràfic i PLN amb tècniques basades en dades per millorar les mesures d'efectivitat en les tres tasques de l'Accés a la Informació Geogràfica investigades. 4) TALPGeoIR: una nova aproximació a la RIG que combina Re-Ranking amb Coneixement Geogràfic (GeoKR), PLN i Retroalimentació de Rellevancia (RR) que aconseguí resultats en l'estat de l'art en comparatives oficials GeoCLEF (Ferrés and Rodríguez, 2008; Mandl et al., 2008) i en experiments posteriors (Ferrés and Rodríguez, 2015a). Aquesta aproximació ha estat avaluada amb el conjunt complert del corpus GeoCLEF (100 topics) i ha mostrat que les tècniques GeoKR, PLN i RR avaluades separadament o en combinació milloren els resultats en les mesures efectivitat MAP i R-Precision dels algoritmes de l'estat de l'art en Recuperació de la Infomació TF-IDF, BM25 i InL2 i a més mostren significació estadística en la majoria dels experiments. 5) GeoTALP-QA: una aproximació basada en l'àmbit geogràfic per espanyol i anglès i la seva avaluació amb un conjunt de preguntes de la geografía espanyola (Ferrés and Rodríguez, 2006). 6) Quatre aproximacions per al georeferenciament de documents formals i informals que obtingueren resultats en l'estat de l'art en avaluacions comparatives (Ferrés and Rodríguez, 2014) i en experiments posteriors (Ferrés and Rodríguez, 2011; Ferrés and Rodríguez, 2015b).Postprint (published version

Rapport : a fact-based question answering system for portuguese

Question answering is one of the longest-standing problems in natural language processing. Although natural language interfaces for computer systems can be considered more common these days, the same still does not happen regarding access to specific textual information. Any full text search engine can easily retrieve documents containing user specified or closely related terms, however it is typically unable to answer user questions with small passages or short answers. The problem with question answering is that text is hard to process, due to its syntactic structure and, to a higher degree, to its semantic contents. At the sentence level, although the syntactic aspects of natural language have well known rules, the size and complexity of a sentence may make it difficult to analyze its structure. Furthermore, semantic aspects are still arduous to address, with text ambiguity being one of the hardest tasks to handle. There is also the need to correctly process the question in order to define its target, and then select and process the answers found in a text. Additionally, the selected text that may yield the answer to a given question must be further processed in order to present just a passage instead of the full text. These issues take also longer to address in languages other than English, as is the case of Portuguese, that have a lot less people working on them. This work focuses on question answering for Portuguese. In other words, our field of interest is in the presentation of short answers, passages, and possibly full sentences, but not whole documents, to questions formulated using natural language. For that purpose, we have developed a system, RAPPORT, built upon the use of open information extraction techniques for extracting triples, so called facts, characterizing information on text files, and then storing and using them for answering user queries done in natural language. These facts, in the form of subject, predicate and object, alongside other metadata, constitute the basis of the answers presented by the system. Facts work both by storing short and direct information found in a text, typically entity related information, and by containing in themselves the answers to the questions already in the form of small passages. As for the results, although there is margin for improvement, they are a tangible proof of the adequacy of our approach and its different modules for storing information and retrieving answers in question answering systems. In the process, in addition to contributing with a new approach to question answering for Portuguese, and validating the application of open information extraction to question answering, we have developed a set of tools that has been used in other natural language processing related works, such as is the case of a lemmatizer, LEMPORT, which was built from scratch, and has a high accuracy. Many of these tools result from the improvement of those found in the Apache OpenNLP toolkit, by pre-processing their input, post-processing their output, or both, and by training models for use in those tools or other, such as MaltParser. Other tools include the creation of interfaces for other resources containing, for example, synonyms, hypernyms, hyponyms, or the creation of lists of, for instance, relations between verbs and agents, using rules

Open-domain web-based multiple document : question answering for list questions with support for temporal restrictors

Tese de doutoramento, Informática (Ciências da Computação), Universidade de Lisboa, Faculdade de Ciências, 2015With the growth of the Internet, more people are searching for information on the Web. The combination of web growth and improvements in Information Technology has reignited the interest in Question Answering (QA) systems. QA is a type of information retrieval combined with natural language processing techniques that aims at finding answers to natural language questions. List questions have been widely studied in the QA field. These are questions that require a list of correct answers, making the task of correctly answering them more complex. In List questions, the answers may lie in the same document or spread over multiple documents. In the latter case, a QA system able to answer List questions has to deal with the fusion of partial answers. The current Question Answering state-of-the-art does not provide yet a good way to tackle this complex problem of collecting the exact answers from multiple documents. Our goal is to provide better QA solutions to users, who desire direct answers, using approaches that deal with the complex problem of extracting answers found spread over several documents. The present dissertation address the problem of answering Open-domain List questions by exploring redundancy and combining it with heuristics to improve QA accuracy. Our approach uses the Web as information source, since it is several orders of magnitude larger than other document collections. Besides handling List questions, we develop an approach with special focus on questions that include temporal information. In this regard, the current work addresses a topic that was lacking specific research. A additional purpose of this dissertation is to report on important results of the research combining Web-based QA, List QA and Temporal QA. Besides the evaluation of our approach itself we compare our system with other QA systems in order to assess its performance relative to the state-of-the-art. Finally, our approaches to answer List questions and List questions with temporal information are implemented into a fully-fledged Open-domain Web-based Question Answering System that provides answers retrieved from multiple documents.Com o crescimento da Internet cada vez mais pessoas buscam informações usando a Web. A combinação do crescimento da Internet com melhoramentos na Tecnologia da Informação traz como consequência o renovado interesse em Sistemas de Respostas a Perguntas (SRP). SRP combina técnicas de recuperação de informação com ferramentas de apoio à linguagem natural com o objetivo de encontrar respostas para perguntas em linguagem natural. Perguntas do tipo lista têm sido largamente estudadas nesta área. Neste tipo de perguntas é esperada uma lista de respostas corretas, o que torna a tarefa de responder a perguntas do tipo lista ainda mais complexa. As respostas para este tipo de pergunta podem ser encontradas num único documento ou espalhados em múltiplos documentos. No último caso, um SRP deve estar preparado para lidar com a fusão de respostas parciais. Os SRP atuais ainda não providenciam uma boa forma de lidar com este complexo problema de coletar respostas de múltiplos documentos. Nosso objetivo é prover melhores soluções para utilizadores que desejam buscar respostas diretas usando abordagens para extrair respostas de múltiplos documentos. Esta dissertação aborda o problema de responder a perguntas de domínio aberto explorando redundância combinada com heurísticas. Nossa abordagem usa a Internet como fonte de informação uma vez que a Web é a maior coleção de documentos da atualidade. Para além de responder a perguntas do tipo lista, nós desenvolvemos uma abordagem para responder a perguntas com restrição temporal. Neste sentido, o presente trabalho aborda este tema onde há pouca investigação específica. Adicionalmente, esta dissertação tem o propósito de informar sobre resultados importantes desta pesquisa que combina várias áreas: SRP com base na Web, SRP especialmente desenvolvidos para responder perguntas do tipo lista e também com restrição temporal. Além da avaliação da nossa própria abordagem, comparamos o nosso sistema com outros SRP, a fim de avaliar o seu desempenho em relação ao estado da arte. Por fim, as nossas abordagens para responder a perguntas do tipo lista e perguntas do tipo lista com informações temporais são implementadas em um Sistema online de Respostas a Perguntas de domínio aberto que funciona diretamente sob a Web e que fornece respostas extraídas de múltiplos documentos.Fundação para a Ciência e a Tecnologia (FCT), SFRH/BD/65647/2009; European Commission, projeto QTLeap (Quality Translation by Deep Language Engineering Approache

Cross-lingual question answering

Question Answering has become an intensively researched area in the last decade, being seen as the next step beyond Information Retrieval in the attempt to provide more concise and better access to large volumes of available information. Question Answering builds on Information Retrieval technology for a first touch of possible relevant data and uses further natural language processing techniques to search for candidate answers and to look for clues that accept or invalidate the candidates as right answers to the question. Though most of the research has been carried out in monolingual settings, where the question and the answer-bearing documents share the same natural language, current approaches concentrate on cross-language scenarios, where the question and the documents are in different languages. Known in this context and common with the Information Retrieval research are three methods of crossing the language barrier: by translating the question, by translating the documents or by aligning both the question and the documents to a common inter-lingual representation. We present a cross-lingual English to German Question Answering system, for both factoid and definition questions, using a German monolingual system and translating the questions from English to German. Two different techniques of translation are evaluated: • direct translation of the English input question into German and • transfer-based translation, by using an intermediate representation that captures the “meaning” of the original question and is translated into the target language. For both translation techniques two types of translation tools are used: bilingual dictionaries and machine translation. The intermediate representation captures the semantic meaning of the question in terms of Question Type (QType), Expected Answer Type (EAType) and Focus, information that steers the workflow of the question answering process. The German monolingual Question Answering system can answer both factoid and definition questions and is based on several premises: • facts and definitions are usually expressed locally at the level of a sentence and its surroundings; • proximity of concepts within a sentence can be related to their semantic dependency; • for factoid questions, redundancy of candidate answers is a good indicator of their suitability; • definitions of concepts are expressed using fixed linguistic structures such as appositions, modifiers, and abbreviation extensions. Extensive evaluations of the monolingual system have shown that the above mentioned hypothesis holds true in most of the cases when dealing with a fairly large collection of documents, like the one used in the CLEF evaluation forum.Innerhalb der letzten zehn Jahre hat sich Question Answering zu einem intensiv erforschten Themengebiet gewandelt, es stellt den nächsten Schritt des Information Retrieval dar, mit dem Bestreben einen präziseren Zugang zu großen Datenbeständen von verfügbaren Informationen bereitzustellen. Das Question Answering setzt auf die Information Retrieval-Technologie, um mögliche relevante Daten zu suchen, kombiniert mit weiteren Techniken zur Verarbeitung von natürlicher Sprache, um mögliche Antwortkandidaten zu identifizieren und diese anhand von Hinweisen oder Anhaltspunkten entsprechend der Frage als richtige Antwort zu akzeptieren oder als unpassend zu erklären. Während ein Großteil der Forschung den einsprachigen Kontext voraussetzt, wobei Frage- und Antwortdokumente ein und dieselbe Sprache teilen, konzentrieren sich aktuellere Ansätze auf sprachübergreifende Szenarien, in denen die Frage- und Antwortdokumente in unterschiedlichen Sprachen vorliegen. Im Kontext des Information Retrieval existieren drei bekannte Ansätze, die versuchen auf unterschiedliche Art und Weise die Sprachbarriere zu überwinden: durch die Übersetzung der Frage, durch die Übersetzung der Dokumente oder durch eine Angleichung von sowohl der Frage als auch der Dokumente zu einer gemeinsamen interlingualen Darstellung. Wir präsentieren ein sprachübergreifendes Question Answering System vom Englischen ins Deutsche, das sowohl für Faktoid- als auch für Definitionsfragen funktioniert. Dazu verwenden wir ein einsprachiges deutsches System und übersetzen die Fragen vom Englischen ins Deutsche. Zwei unterschiedliche Techniken der Übersetzung werden untersucht: • die direkte Übersetzung der englischen Fragestellung ins Deutsche und • die Abbildungs-basierte Übersetzung, die eine Zwischendarstellung verwendet, um die „Semantik“ der ursprünglichen Frage zu erfassen und in die Zielsprache zu übersetzen. Für beide aufgelisteten Übersetzungstechniken werden zwei Übersetzungsquellen verwendet: zweisprachige Wörterbücher und maschinelle Übersetzung. Die Zwischendarstellung erfasst die Semantik der Frage in Bezug auf die Art der Frage (QType), den erwarteten Antworttyp (EAType) und Fokus, sowie die Informationen, die den Ablauf des Frage-Antwort-Prozesses steuern. Das deutschsprachige Question Answering System kann sowohl Faktoid- als auch Definitionsfragen beantworten und basiert auf mehreren Prämissen: • Fakten und Definitionen werden in der Regel lokal auf Satzebene ausgedrückt; • Die Nähe von Konzepten innerhalb eines Satzes kann auf eine semantische Verbindung hinweisen; • Bei Faktoidfragen ist die Redundanz der Antwortkandidaten ein guter Indikator für deren Eignung; • Definitionen von Begriffen werden mit festen sprachlichen Strukturen ausgedrückt, wie Appositionen, Modifikatoren, Abkürzungen und Erweiterungen. Umfangreiche Auswertungen des einsprachigen Systems haben gezeigt, dass die oben genannten Hypothesen in den meisten Fällen wahr sind, wenn es um eine ziemlich große Sammlung von Dokumenten geht, wie bei der im CLEF Evaluationsforum verwendeten Version

A model for information retrieval driven by conceptual spaces

A retrieval model describes the transformation of a query into a set of documents. The question is: what drives this transformation? For semantic information retrieval type of models this transformation is driven by the content and structure of the semantic models. In this case, Knowledge Organization Systems (KOSs) are the semantic models that encode the meaning employed for monolingual and cross-language retrieval. The focus of this research is the relationship between these meanings’ representations and their role and potential in augmenting existing retrieval models effectiveness. The proposed approach is unique in explicitly interpreting a semantic reference as a pointer to a concept in the semantic model that activates all its linked neighboring concepts. It is in fact the formalization of the information retrieval model and the integration of knowledge resources from the Linguistic Linked Open Data cloud that is distinctive from other approaches. The preprocessing of the semantic model using Formal Concept Analysis enables the extraction of conceptual spaces (formal contexts)that are based on sub-graphs from the original structure of the semantic model. The types of conceptual spaces built in this case are limited by the KOSs structural relations relevant to retrieval: exact match, broader, narrower, and related. They capture the definitional and relational aspects of the concepts in the semantic model. Also, each formal context is assigned an operational role in the flow of processes of the retrieval system enabling a clear path towards the implementations of monolingual and cross-lingual systems. By following this model’s theoretical description in constructing a retrieval system, evaluation results have shown statistically significant results in both monolingual and bilingual settings when no methods for query expansion were used. The test suite was run on the Cross-Language Evaluation Forum Domain Specific 2004-2006 collection with additional extensions to match the specifics of this model