Voice-QA: evaluating the impact of misrecognized words on passage retrieval

Question Answering is an Information Retrieval task where the query is posed using natural language and the expected result is a concise answer. Voice-activated Question Answering systems represent an interesting application, where the question is formulated by speech. In these systems, an Automatic Speech Recognition module can be used to transcribe the question. Thus, recognition errors may be introduced, producing a significant effect on the answer retrieval process. In this work we study the relationship between some features of misrecognized words and the retrieval results. The features considered are the redundancy of a word in the result set and its inverse document frequency calculated over the collection. The results show that the redundancy of a word may be an important clue on whether an error on it would deteriorate the retrieval results, at least if a closed model is used for speech recognition.This work was carried out in the framework of TextEnterprise (TIN2009-13391-C04-03), Timpano (TIN2011-28169-C05-01), WIQEI IRSES (grant no. 269180) within the FP 7 Marie Curie People, FPU Grant AP2010-4193 from the Spanish Ministerio de Educaci´on (first author), and the Microcluster VLC/Campus on Multimodal Intelligent Systems (third author)Calvo Lance, M.; Buscaldi, D.; Rosso, P. (2012). Voice-QA: evaluating the impact of misrecognized words on passage retrieval. En Advances in Artificial Intelligence - IBERAMIA 2012. Springer Verlag (Germany). 462-471. https://doi.org/10.1007/978-3-642-34654-5_47S462471Buscaldi, D., Gómez, J.M., Rosso, P., Sanchis, E.: N-Gram vs. Keyword-Based Passage Retrieval for Question Answering. In: Peters, C., Clough, P., Gey, F.C., Karlgren, J., Magnini, B., Oard, D.W., de Rijke, M., Stempfhuber, M. (eds.) CLEF 2006. LNCS, vol. 4730, pp. 377–384. Springer, Heidelberg (2007)Harabagiu, S., Moldovan, D., Picone, J.: Open-Domain Voice-Activated Question Answering. In: 19th International Conference on Computational Linguistics (COLING 2002), pp. 1–7 (2002)Jones, K.: Index Term Weighting. Information Storage and Retrieval 9(11), 619–633 (1973)Moldovan, D., Paşca, M., Harabagiu, S., Surdeanu, M.: Performance Issues and Error Analysis in an Open-Domain Question Answering System. ACM Transactions on Information Systems (TOIS) 21(2), 133–154 (2003)Rosso, P., Hurtado, L.F., Segarra, E., Sanchis, E.: On the Voice-Activated Question Answering. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 42(1), 75–85 (2012)Sanderson, M., Paramita, M.L., Clough, P., Kanoulas, E.: Do User Preferences and Evaluation Measures Line Up? In: 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2010), pp. 555–562. ACM, New York (2010)Turmo, J., Comas, P., Rosset, S., Galibert, O., Moreau, N., Mostefa, D., Rosso, P., Buscaldi, D.: Overview of QAST 2009. In: Peters, C., Di Nunzio, G.M., Kurimo, M., Mandl, T., Mostefa, D., Peñas, A., Roda, G. (eds.) CLEF 2009. LNCS, vol. 6241, pp. 197–211. Springer, Heidelberg (2010

Using natural language processing for question answering in closed and open domains

With regard to the growth in the amount of social, environmental, and biomedical information available digitally, there is a growing need for Question Answering (QA) systems that can empower users to master this new wealth of information. Despite recent progress in QA, the quality of interpretation and extraction of the desired answer is not adequate. We believe that striving for higher accuracy in QA systems is subject to on-going research, i.e., it is better to have no answer is better than wrong answers. However, there are diverse queries, which the state of the art QA systems cannot interpret and answer properly. The problem of interpreting a question in a way that could preserve its syntactic-semantic structure is considered as one of the most important challenges in this area. In this work we focus on the problems of semantic-based QA systems and analyzing the effectiveness of NLP techniques, query mapping, and answer inferencing both in closed (first scenario) and open (second scenario) domains. For this purpose, the architecture of Semantic-based closed and open domain Question Answering System (hereafter “ScoQAS”) over ontology resources is presented with two different prototyping: Ontology-based closed domain and an open domain under Linked Open Data (LOD) resource. The ScoQAS is based on NLP techniques combining semantic-based structure-feature patterns for question classification and creating a question syntactic-semantic information structure (QSiS). The QSiS provides an actual potential by building constraints to formulate the related terms on syntactic-semantic aspects and generating a question graph (QGraph) which facilitates making inference for getting a precise answer in the closed domain. In addition, our approach provides a convenient method to map the formulated comprehensive information into SPARQL query template to crawl in the LOD resources in the open domain. The main contributions of this dissertation are as follows: 1. Developing ScoQAS architecture integrated with common and specific components compatible with closed and open domain ontologies. 2. Analysing user’s question and building a question syntactic-semantic information structure (QSiS), which is constituted by several processes of the methodology: question classification, Expected Answer Type (EAT) determination, and generated constraints. 3. Presenting an empirical semantic-based structure-feature pattern for question classification and generalizing heuristic constraints to formulate the relations between the features in the recognized pattern in terms of syntactical and semantical. 4. Developing a syntactic-semantic QGraph for representing core components of the question. 5. Presenting an empirical graph-based answer inference in the closed domain. In a nutshell, a semantic-based QA system is presented which provides some experimental results over the closed and open domains. The efficiency of the ScoQAS is evaluated using measures such as precision, recall, and F-measure on LOD challenges in the open domain. We focus on quantitative evaluation in the closed domain scenario. Due to the lack of predefined benchmark(s) in the first scenario, we define measures that demonstrate the actual complexity of the problem and the actual efficiency of the solutions. The results of the analysis corroborate the performance and effectiveness of our approach to achieve a reasonable accuracy.Con respecto al crecimiento en la cantidad de información social, ambiental y biomédica disponible digitalmente, existe una creciente necesidad de sistemas de la búsqueda de la respuesta (QA) que puedan ofrecer a los usuarios la gestión de esta nueva cantidad de información. A pesar del progreso reciente en QA, la calidad de interpretación y extracción de la respuesta deseada no es la adecuada. Creemos que trabajar para lograr una mayor precisión en los sistemas de QA es todavía un campo de investigación abierto. Es decir, es mejor no tener respuestas que tener respuestas incorrectas. Sin embargo, existen diversas consultas que los sistemas de QA en el estado del arte no pueden interpretar ni responder adecuadamente. El problema de interpretar una pregunta de una manera que podría preservar su estructura sintáctica-semántica es considerado como uno de los desafíos más importantes en esta área. En este trabajo nos centramos en los problemas de los sistemas de QA basados en semántica y en el análisis de la efectividad de las técnicas de PNL, y la aplicación de consultas e inferencia respuesta tanto en dominios cerrados (primer escenario) como abiertos (segundo escenario). Para este propósito, la arquitectura del sistema de búsqueda de respuestas en dominios cerrados y abiertos basado en semántica (en adelante "ScoQAS") sobre ontologías se presenta con dos prototipos diferentes: en dominio cerrado basado en el uso de ontologías y un dominio abierto dirigido a repositorios de Linked Open Data (LOD). El ScoQAS se basa en técnicas de PNL que combinan patrones de características de estructura semánticas para la clasificación de preguntas y la creación de una estructura de información sintáctico-semántica de preguntas (QSiS). El QSiS proporciona una manera la construcción de restricciones para formular los términos relacionados en aspectos sintáctico-semánticos y generar un grafo de preguntas (QGraph) el cual facilita derivar inferencias para obtener una respuesta precisa en el dominio cerrado. Además, nuestro enfoque proporciona un método adecuado para aplicar la información integral formulada en la plantilla de consulta SPARQL para navegar en los recursos LOD en el dominio abierto. Las principales contribuciones de este trabajo son los siguientes: 1. El desarrollo de la arquitectura ScoQAS integrada con componentes comunes y específicos compatibles con ontologías de dominio cerrado y abierto. 2. El análisis de la pregunta del usuario y la construcción de una estructura de información sintáctico-semántica de las preguntas (QSiS), que está constituida por varios procesos de la metodología: clasificación de preguntas, determinación del Tipo de Respuesta Esperada (EAT) y las restricciones generadas. 3. La presentación de un patrón empírico basado en la estructura semántica para clasificar las preguntas y generalizar las restricciones heurísticas para formular las relaciones entre las características en el patrón reconocido en términos sintácticos y semánticos. 4. El desarrollo de un QGraph sintáctico-semántico para representar los componentes centrales de la pregunta. 5. La presentación de la respuesta inferida a partir de un grafo empírico en el dominio cerrado. En pocas palabras, se presenta un sistema semántico de QA que proporciona algunos resultados experimentales sobre los dominios cerrados y abiertos. La eficiencia del ScoQAS se evalúa utilizando medidas tales como una precisión, cobertura y la medida-F en desafíos LOD para el dominio abierto. Para el dominio cerrado, nos centramos en la evaluación cuantitativa; su precisión se analiza en una ontología empresarial. La falta de un banco la pruebas predefinidas es uno de los principales desafíos de la evaluación en el primer escenario. Por lo tanto, definimos medidas que demuestran la complejidad real del problema y la eficiencia real de las soluciones. Los resultados del análisis corroboran el rendimient

Mobile Phone Text Processing and Question-Answering

Mobile phone text messaging between mobile users and information services is a growing area of Information Systems. Users may require the service to provide an answer to queries, or may, in wikistyle, want to contribute to the service by texting in some information within the service’s domain of discourse. Given the volume of such messaging it is essential to do the processing through an automated service. Further, in the case of repeated use of the service, the quality of such a response has the potential to benefit from a dynamic user profile that the service can build up from previous texts of the same user. This project will investigate the potential for creating such intelligent mobile phone services and aims to produce a computational model to enable their efficient implementation. To make the project feasible, the scope of the automated service is considered to lie within a limited domain of, for example, information about entertainment within a specific town centre. The project will assume the existence of a model of objects within the domain of discourse, hence allowing the analysis of texts within the context of a user model and a domain model. Hence, the project will involve the subject areas of natural language processing, language engineering, machine learning, knowledge extraction, and ontological engineering

Analysis of errors in the automatic translation of questions for translingual QA systems

Purpose – This study aims to focus on the evaluation of systems for the automatic translation of questions destined to translingual question-answer (QA) systems. The efficacy of online translators when performing as tools in QA systems is analysed using a collection of documents in the Spanish language. Design/methodology/approach – Automatic translation is evaluated in terms of the functionality of actual translations produced by three online translators (Google Translator, Promt Translator, and Worldlingo) by means of objective and subjective evaluation measures, and the typology of errors produced was identified. For this purpose, a comparative study of the quality of the translation of factual questions of the CLEF collection of queries was carried out, from German and French to Spanish. Findings – It was observed that the rates of error for the three systems evaluated here are greater in the translations pertaining to the language pair German-Spanish. Promt was identified as the most reliable translator of the three (on average) for the two linguistic combinations evaluated. However, for the Spanish-German pair, a good assessment of the Google online translator was obtained as well. Most errors (46.38 percent) tended to be of a lexical nature, followed by those due to a poor translation of the interrogative particle of the query (31.16 percent). Originality/value – The evaluation methodology applied focuses above all on the finality of the translation. That is, does the resulting question serve as effective input into a translingual QA system? Thus, instead of searching for “perfection”, the functionality of the question and its capacity to lead one to an adequate response are appraised. The results obtained contribute to the development of improved translingual QA systems