Factoid question answering for spoken documents

In this dissertation, we present a factoid question answering system, specifically tailored for Question Answering (QA) on spoken documents. This work explores, for the first time, which techniques can be robustly adapted from the usual QA on written documents to the more difficult spoken documents scenario. More specifically, we study new information retrieval (IR) techniques designed for speech, and utilize several levels of linguistic information for the speech-based QA task. These include named-entity detection with phonetic information, syntactic parsing applied to speech transcripts, and the use of coreference resolution. Our approach is largely based on supervised machine learning techniques, with special focus on the answer extraction step, and makes little use of handcrafted knowledge. Consequently, it should be easily adaptable to other domains and languages. In the work resulting of this Thesis, we have impulsed and coordinated the creation of an evaluation framework for the task of QA on spoken documents. The framework, named QAst, provides multi-lingual corpora, evaluation questions, and answers key. These corpora have been used in the QAst evaluation that was held in the CLEF workshop for the years 2007, 2008 and 2009, thus helping the developing of state-of-the-art techniques for this particular topic. The presentend QA system and all its modules are extensively evaluated on the European Parliament Plenary Sessions English corpus composed of manual transcripts and automatic transcripts obtained by three different Automatic Speech Recognition (ASR) systems that exhibit significantly different word error rates. This data belongs to the CLEF 2009 track for QA on speech transcripts. The main results confirm that syntactic information is very useful for learning to rank question candidates, improving results on both manual and automatic transcripts unless the ASR quality is very low. Overall, the performance of our system is comparable or better than the state-of-the-art on this corpus, confirming the validity of our approach.En aquesta Tesi, presentem un sistema de Question Answering (QA) factual, especialment ajustat per treballar amb documents orals. En el desenvolupament explorem, per primera vegada, quines tècniques de les habitualment emprades en QA per documents escrit són suficientment robustes per funcionar en l'escenari més difícil de documents orals. Amb més especificitat, estudiem nous mètodes de Information Retrieval (IR) dissenyats per tractar amb la veu, i utilitzem diversos nivells d'informació linqüística. Entre aquests s'inclouen, a saber: detecció de Named Entities utilitzant informació fonètica, "parsing" sintàctic aplicat a transcripcions de veu, i també l'ús d'un sub-sistema de detecció i resolució de la correferència. La nostra aproximació al problema es recolza en gran part en tècniques supervisades de Machine Learning, estant aquestes enfocades especialment cap a la part d'extracció de la resposta, i fa servir la menor quantitat possible de coneixement creat per humans. En conseqüència, tot el procés de QA pot ser adaptat a altres dominis o altres llengües amb relativa facilitat. Un dels resultats addicionals de la feina darrere d'aquesta Tesis ha estat que hem impulsat i coordinat la creació d'un marc d'avaluació de la taska de QA en documents orals. Aquest marc de treball, anomenat QAst (Question Answering on Speech Transcripts), proporciona un corpus de documents orals multi-lingüe, uns conjunts de preguntes d'avaluació, i les respostes correctes d'aquestes. Aquestes dades han estat utilitzades en les evaluacionis QAst que han tingut lloc en el si de les conferències CLEF en els anys 2007, 2008 i 2009; d'aquesta manera s'ha promogut i ajudat a la creació d'un estat-de-l'art de tècniques adreçades a aquest problema en particular. El sistema de QA que presentem i tots els seus particulars sumbòduls, han estat avaluats extensivament utilitzant el corpus EPPS (transcripcions de les Sessions Plenaries del Parlament Europeu) en anglès, que cónté transcripcions manuals de tots els discursos i també transcripcions automàtiques obtingudes mitjançant tres reconeixedors automàtics de la parla (ASR) diferents. Els reconeixedors tenen característiques i resultats diferents que permetes una avaluació quantitativa i qualitativa de la tasca. Aquestes dades pertanyen a l'avaluació QAst del 2009. Els resultats principals de la nostra feina confirmen que la informació sintàctica és mol útil per aprendre automàticament a valorar la plausibilitat de les respostes candidates, millorant els resultats previs tan en transcripcions manuals com transcripcions automàtiques, descomptat que la qualitat de l'ASR sigui molt baixa. En general, el rendiment del nostre sistema és comparable o millor que els altres sistemes pertanyents a l'estat-del'art, confirmant així la validesa de la nostra aproximació

Prism : an answer projection system

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.MIT Institute Archives copy: p. 29-56 bound in reverse order.Includes bibliographical references (p. 54-56).Prism is an answer projection system that combines the best attributes of traditional and Web-based question answering systems. Traditional question answering systems retrieve answers with pinpoint accuracy but limited coverage, using a small and reliable data source, while Web-based systems retrieve answers with broad coverage but limited accuracy, using a large but noisy data source. By taking advantage of the strengths of each system, an answer projection system can answer as many questions as a Web-based system while still being as accurate as a traditional system. In fact, Prism improves the performance of a traditional question answering system by 25%. It improves the accuracy of answers retrieved from the World Wide Web by 10%, and more importantly, it verifies answers retrieved from the Web by providing reliable supporting documents. By combining Prism with traditional and Web-based systems, we obtain a question answering system with high accuracy and broad coverage.by Lynn Wu.M.Eng

Coping with Alternate Formulations of Questions and Answers

We present in this chapter the QALC system which has participated in the four TREC QA evaluations. We focus here on the problem of linguistic variation in order to be able to relate questions and answers. We present first, variation at the term level which consists in retrieving questions terms in document sentences even if morphologic, syntactic or semantic variations alter them. Our second subject matter concerns variation at the sentence level that we handle as different partial reformulations of questions. Questions are associated with extraction patterns based on the question syntactic type and the object that is under query. We present the whole system thus allowing situating how QALC deals with variation, and different evaluations

Information fusion for automated question answering

Until recently, research efforts in automated Question Answering (QA) have mainly focused on getting a good understanding of questions to retrieve correct answers. This includes deep parsing, lookups in ontologies, question typing and machine learning of answer patterns appropriate to question forms. In contrast, I have focused on the analysis of the relationships between answer candidates as provided in open domain QA on multiple documents. I argue that such candidates have intrinsic properties, partly regardless of the question, and those properties can be exploited to provide better quality and more user-oriented answers in QA.Information fusion refers to the technique of merging pieces of information from different sources. In QA over free text, it is motivated by the frequency with which different answer candidates are found in different locations, leading to a multiplicity of answers. The reason for such multiplicity is, in part, the massive amount of data used for answering, and also its unstructured and heterogeneous content: Besides am¬ biguities in user questions leading to heterogeneity in extractions, systems have to deal with redundancy, granularity and possible contradictory information. Hence the need for answer candidate comparison. While frequency has proved to be a significant char¬ acteristic of a correct answer, I evaluate the value of other relationships characterizing answer variability and redundancy.Partially inspired by recent developments in multi-document summarization, I re¬ define the concept of "answer" within an engineering approach to QA based on the Model-View-Controller (MVC) pattern of user interface design. An "answer model" is a directed graph in which nodes correspond to entities projected from extractions and edges convey relationships between such nodes. The graph represents the fusion of information contained in the set of extractions. Different views of the answer model can be produced, capturing the fact that the same answer can be expressed and pre¬ sented in various ways: picture, video, sound, written or spoken language, or a formal data structure. Within this framework, an answer is a structured object contained in the model and retrieved by a strategy to build a particular view depending on the end user (or taskj's requirements.I describe shallow techniques to compare entities and enrich the model by discovering four broad categories of relationships between entities in the model: equivalence, inclusion, aggregation and alternative. Quantitatively, answer candidate modeling im¬ proves answer extraction accuracy. It also proves to be more robust to incorrect answer candidates than traditional techniques. Qualitatively, models provide meta-information encoded by relationships that allow shallow reasoning to help organize and generate the final output

Question answering using document tagging and question classification

viii, 139 leaves ; 29 cm.Question answering (QA) is a relatively new area of research. QA is retriecing answers to questions rather than information retrival systems (search engines), which retrieve documents. This means that question answering systems will possibly be the next generation of search engines. What is left to be done to allow QA to be the next generation of search engines? The answer is higher accuracy, which can be achieved by investigating methods of questions answering. I took the approach of designing a question answering system that is based on document tagging and question classification. Question classification extracts useful information from the question about how to answer the question. Document tagging extracts useful information from the documents, which will be used in finding the answer to the question. We used different available systems to tage the documents. Our system classifies the questions using manually developed rules. I also investigated different ways which can use both these methods to answer questions and found that our methods had a comparable accuracy to some systems that use deeper processing techniques. This thesis includes investigations into modules of a question answering system and gives insights into how to go about developing a question answering system based on document tagging and question classification. I also evaluated our current system with the questions from the TREC 2004 question answering track

Implementation of an information retrieval system within a central knowledge management system

Páginas numeradas: I-XIII, 14-126Estágio realizado na Wipro Portugal SA e orientado pelo Eng.º Hugo NetoTese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201

Artificial Intelligence as Evidence

This article explores issues that govern the admissibility of Artificial Intelligence (“AI”) applications in civil and criminal cases, from the perspective of a federal trial judge and two computer scientists, one of whom also is an experienced attorney. It provides a detailed yet intelligible discussion of what AI is and how it works, a history of its development, and a description of the wide variety of functions that it is designed to accomplish, stressing that AI applications are ubiquitous, both in the private and public sectors. Applications today include: health care, education, employment-related decision-making, finance, law enforcement, and the legal profession. The article underscores the importance of determining the validity of an AI application (i.e., how accurately the AI measures, classifies, or predicts what it is designed to), as well as its reliability (i.e., the consistency with which the AI produces accurate results when applied to the same or substantially similar circumstances), in deciding whether it should be admitted into evidence in civil and criminal cases. The article further discusses factors that can affect the validity and reliability of AI evidence, including bias of various types, “function creep,” lack of transparency and explainability, and the sufficiency of the objective testing of AI applications before they are released for public use. The article next provides an in-depth discussion of the evidentiary principles that govern whether AI evidence should be admitted in court cases, a topic which, at present, is not the subject of comprehensive analysis in decisional law. The focus of this discussion is on providing a step-by-step analysis of the most important issues, and the factors that affect decisions on whether to admit AI evidence. Finally, the article concludes with a discussion of practical suggestions intended to assist lawyers and judges as they are called upon to introduce, object to, or decide on whether to admit AI evidence