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Natural language question answering

Xem 1-20 trên 57 kết quả Natural language question answering
  • A new method is presented for simplifying the logical expressions used to represent utterance meaning in a natural language system. 1 This simplification method utilizes the encoded knowledge and the limited inference-making capability of a tax onomic knowledge representation system to reduce the constituent structure of logical expressions.

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  • We discuss the techniques we have developed and implemented for the cross-categorial treatment of comparatives in TELl, a natural language questionanswering system that's transportable among both application domains and types of backend retrieval systems.

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  • This work presents a model for learning inference procedures for story comprehension through inductive generalization and reinforcement learning, based on classified examples. The learned inference procedures (or strategies) are represented as of sequences of transformation rules. The approach is compared to three prior systems, and experimental results are presented demonstrating the efficacy of the model.

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  • We present a graph-based semi-supervised learning for the question-answering (QA) task for ranking candidate sentences. Using textual entailment analysis, we obtain entailment scores between a natural language question posed by the user and the candidate sentences returned from search engine. The textual entailment between two sentences is assessed via features representing high-level attributes of the entailment problem such as sentence structure matching, question-type named-entity matching based on a question-classifier, etc.

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  • Do natural language database systems still ,~lovide a valuable environment for further work on n~,tural language processing? Are there other systems which provide the same hard environment :for testing, but allow us to explore more interesting natural language questions? In order to answer , o to the first question and yes to the second (the position taken by our panel's chair}, there must be an interesting language problem which is more naturally studied in some other system than in the database system. ...

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  • The design and implementation of a paraphrase component for a natural language questlon-answer system (CO-OP) is presented. A major point made is the role of given and n e w information in formulating a paraphrase that differs in a meaningful way from the user's question. A description is also given of the transformational grammar used by the paraphraser to generate questions. I• INTRO~ION fact, a lexicon and database schema are the only items which contain domain-specific information.

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  • For a natural language access to database system to be practical it must achieve a good match between the capabilities of the user and the requirements of the task. The user brings his own natural language and his own style of interaction to the system. The task brings the questions that must be answered and the database domaln+s semantics. All natural language access systems achieve some degree of success. But to make progress as a field, we need to be able to evaluate the degree of this success. For too long, the best we have menaged has been to...

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  • Esfinge is a general domain Portuguese question answering system. It tries to take advantage of the great amount of information existent in the World Wide Web. Since Portuguese is one of the most used languages in the web and the web itself is a constantly growing source of updated information, this kind of techniques are quite interesting and promising.

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  • In responding to the guidelines established by the session chairman of this panel, three of the five topics he set forth will be discussed. These include aggregate functions and quantity questions, querying semantically complex fields, and multi-file queries. As we will make clear in the sequel, the transformational apparatus utilized in the TQA Question Answering System provides a principled basis for handling these and many other problems i n natural language access to databases.

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  • In Information Retrieval (IR) in general and Question Answering (QA) in particular, queries and relevant textual content often significantly differ in their properties and are therefore difficult to relate with traditional IR methods, e.g. key-word matching. In this paper we describe an algorithm that addresses this problem, but rather than looking at it on a term matching/term reformulation level, we focus on the syntactic differences between questions and relevant text passages.

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  • Introduction: Most research in machine learning has been focused on binary classification, in which the learned classifier outputs one of two possible answers. Important fundamental questions can be analyzed in terms of binary classification, but realworld natural language processing problems often involve richer output spaces. In this tutorial, we will focus on classifiers with a large number of possible outputs with interesting structure. Notable examples include information retrieval, part-of-speech tagging, NP chucking, parsing, entity extraction, and phoneme recognition. ...

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  • AS part of our long-term research into techniques for information retrieval from natural language data bases, we have developed over the past few years a natural language interface for data base retrieval [1,2]. In developing this system, we have sought general, conceptually simple, linguistically-based solutlons to problems of semantic representation and interpretation. One component of the system, which we have recently redesigned and are now implementing in its revised form, involves the generation of responses. ...

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  • I shall discuss issues in natural language (NL) access to databases in the light of an experimental NL questlon-answering system, Chat, which I wrote with Fernando Perelra at Edinburgh University, and which is described more fully elsewhere [8] [6] [5]. Our approach was strongly influenced by the work of Alaln Colmerauer [2] and Veronica Dahl [3] at Marseille University. Chat stages: processes a NL question in three main relational database system.

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  • We discuss ways of allowing the users of a natural language processor to define, examine, and modify the definitions of any domain-specific words or phrases known to the system. An implementation of this work forms a critical portion of the knowledge acquisition component of our Transportable English-Language Interface (TELl), which answers English questions about tabular (first normal-form) data files and runs on a Symbolics Lisp Machine.

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  • One of the major challenges in TRECstyle question-answering (QA) is to overcome the mismatch in the lexical representations in the query space and document space. This is particularly severe in QA as exact answers, rather than documents, are required in response to questions. Most current approaches overcome the mismatch problem by employing either data redundancy strategy through the use of Web or linguistic resources. This paper investigates the integration of lexical relations and Web knowledge to tackle this problem.

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  • AnswerBus News Engine' is a question answering system using the contents of CNN Web site 2 as its knowledge base. Comparing to other question answering systems including its previous versions, it has a totally independent crawling and indexing system and a fully functioning search engine. Because of its dynamic and continuous indexing, it is possible to answer questions on just-happened facts. Again, it reaches high correct answer rate. In this demonstration we will present the living system as well as its new technical features. ...

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  • Most question answering (QA) and information retrieval (IR) systems are insensitive to different users’ needs and preferences, and also to the existence of multiple, complex or controversial answers. We introduce adaptivity in QA and IR by creating a hybrid system based on a dialogue interface and a user model. Keywords: question answering, information retrieval, user modelling, dialogue interfaces.

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  • The paper proposes a paradigmatic approach to morphological knowledge acquisition. It addresses the problem of learning from examples rules for word-forms analysis and synthesis. These rules, established by generalizing the training data sets, are effectively used by a built-in int e r p r e t e r which acts c o n s e q u e n t l y as a morphological processor within the architecture of a natural language question-answering system. The PARADIGM system has no a priori knowledge which should restrict it to a particular natural language, but instead builds up the morphological rules...

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  • Web search is an information-seeking activity. Often times, this amounts to a user seeking answers to a question. However, queries, which encode user’s information need, are typically not expressed as full-length natural language sentences — in particular, as questions. Rather, they consist of one or more text fragments.

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  • The main aim of this project is to explore, develop and evaluate the contribution of language technologies to the development of WEBCOOP, a system that provides intelligent Cooperative responses to Web queries. Such a system requires the integration of knowledge representation and the use of advanced reasoning procedures.

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