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Natural language understanding

Xem 1-20 trên 142 kết quả Natural language understanding
  • The Natural Language Understanding Engine Test Environment (ETE) is a GUI software tool that aids in the development and maintenance of large, modular, natural language understanding (NLU) systems. Natural language understanding systems are composed of modules (such as partof-speech taggers, parsers and semantic analyzers) which are difficult to test individually because of the complexity of their output data structures.

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  • Knowledge acquisition is a serious bottleneck for natural language understanding systems. For this reason, large-scale linguistic resources have been compiled and made available by organizations such as the Linguistic Data Consortium (Comlex) and Princeton University (WordNet). Systems making use of these resources can greatly accelerate the development process by avoiding the need for the developer to re-create this information. In this paper we describe how we integrated these large scale linguistic resources into our natural language understanding system. ...

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  • Several recent efforts in statistical natural language understanding (NLU) have focused on generating clumps of English words from semantic meaning concepts (Miller et al., 1995; Levin and Pieraccini, 1995; Epstein et al., 1996; Epstein, 1996). This paper extends the IBM Machine Translation Group's concept of fertility (Brown et al., 1993) to the generation of clumps for natural language understanding. The basic underlying intuition is that a single concept may be expressed in English as many disjoint clump of words. ...

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  • Semantically clean inheritance of structured descriptions, taxonomic classification of gpneric knowledge, intensional structures for functional roles (including the possibility of multiple fillers), and procedural attachment (with automatic invocation) make it particularly useful in computer-based natural language understanding. We have implemented a prototype natural language system that uses KLONE extensively in several facets of its operation.

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  • This paper describes the NLMenu System, a menu-based natural language understanding system. Rather than requiring the user to type his input to the system, input to NLMenu is made by selecting items from a set of dynamically changing menus. Active menus and items are determined by a predictive left-corner parser that accesses a semantic grammar and lexicon. The advantage of this approach is that a l l inputs to the NLMenu System can be understood thus giving a 0% f a i l u r e rate.

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  • In this paper we compare two approaches to natural language understanding (NLU). The first approach is derived from the field of statistical machine translation (MT), whereas the other uses the maximum entropy (ME) framework. Starting with an annotated corpus, we describe the problem of NLU as a translation from a source sentence to a formal language target sentence. We mainly focus on the quality of the different alignment and ME models and show that the direct ME approach outperforms the alignment templates method. ...

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  • In the field of knowledge based systems for natural language processing, one of the most challenging aims is to use parts of an existing knowledge base for different domains and/or different tasks. We support the point that this problem can only be solved by using adequate metainformation about the content and structuring principles of the representational systems concerned. One of the prerequisites in this respect is the transparency of modelling decisions.

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  • A computer system is being developed to handle the acquisition, representation, and use of linguistic knowledge. The computer system is rule-based and utilizes a semantic network for knowledge storage and representation. In order to facilitate the interaction between user and system, input of linguistic knowledge and computer responses are in natural language. Knowledge of various types can be entered and utilized: syntactic and semantic; assertions and rules.

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  • That are ill-formed with respect to the grammar will be received, both because p e o p l e regularly form ungra=cmatical utterances and because there are a variety of forms that cannot be readily included in current grammatical models and are hence "extra-grammatical". These might be rejected, but as Wilks stresses, "...understanding requires, at the very least, ... some attempt to interpret, rather than merely reject, what seem to be ill-formed utterances." [WIL76] This paper i n v e s t i g a t e s several language phenomena commonly considered ungrammatical....

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  • IV Familiar with the database and the interaction language Ill Familiar with the contents of database II Familiar with the domain of application I Passing knowledge of the domain of application Of course, as users gain experience with a system, they will continually attempt to adapt to its quirks. If the purpose of the evaluation is to demonstrate that the natural language understanding system is merely useable.

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  • In BBN's natural language understanding and generation system (Janus), we have used a hybrid approach to representation, employing an intensional logic for the representation of the semantics of utterances and a taxonomic language with formal semantics for specification of descriptive constants and axioms relating them. Remarkably, 99.9% of 7,000 vocabulary items in our natural language applications could be adequately axiomatlzed in the taxonomic language.

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  • BOOK DESCRIPTION This book offers a highly accessible introduction to Natural Language Processing, the field that underpins a variety of language technologies, ranging from predictive text and email filtering to automatic summarization and translation. With Natural Language Processing with Python, you’ll learn how to write Python programs to work with large collections of unstructured text. You’ll access richly-annotated datasets using a comprehensive range of linguistic data structures.

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  • Logic programming, an important new method of compute programming resulting from recent research in artifucial intelligence and computer science, has proved to be especially appropriate for solving problems in natrual-language processing. "Prolog and Natural Language Analysis" provides a concise and practical introduction to logic programming and the logic-programming language Prolog both as vehicles for understanding elementary computational linguistics and as tools for implementing the basic components of natural-language-processing systems....

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  • This opinion paper discusses subjective natural language problems in terms of their motivations, applications, characterizations, and implications. It argues that such problems deserve increased attention because of their potential to challenge the status of theoretical understanding, problem-solving methods, and evaluation techniques in computational linguistics. The author supports a more holistic approach to such problems; a view that extends beyond opinion mining or sentiment analysis.

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  • Human understanding of spoken language appears to integrate the use of contextual expectations with acoustic level perception in a tightly-coupled, sequential fashion. Yet computer speech understanding systems typically pass the transcript produced by a speech recognizer into a natural language parser with no integration of acoustic and grammatical constraints. One reason for this is the complexity of implementing that integration.

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  • This hybrid system participated in the 1993 ATIS natural language evaluation. Although only four months old, the scores achieved by the combined system were quite respectable. Because of differences between language understanding and speech recognition, significant changes are required in the hidden Markov model methodology. Unlike speech, where each phoneme results in a local sequence of spectra, the relation between the meaning of a sentence and the sequence of words is not a simple linear sequential model. ...

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  • In this paper, we exploit non-local features as an estimate of long-distance dependencies to improve performance on the statistical spoken language understanding (SLU) problem. The statistical natural language parsers trained on text perform unreliably to encode non-local information on spoken language. An alternative method we propose is to use trigger pairs that are automatically extracted by a feature induction algorithm. We describe a light version of the inducer in which a simple modification is efficient and successful. ...

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  • Spoken Language Understanding (SLU) addresses the problem of extracting semantic meaning conveyed in an utterance. The traditional knowledge-based approach to this problem is very expensive -- it requires joint expertise in natural language processing and speech recognition, and best practices in language engineering for every new domain. On the other hand, a statistical learning approach needs a large amount of annotated data for model training, which is seldom available in practical applications outside of large research labs. ...

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  • This paper presents an analysis of purpose clauses in the context of instruction understanding. Such analysis shows that goals affect the interpretation and / or execution of actions, lends support to the proposal of using generation and enablement to model relations between actions, and sheds light on some inference processes necessary to interpret purpose clauses. which, as its name says, expresses the agent's purpose in performing a certain action. The analysis of purpose clauses is relevant to the problem of understanding Natural Language instructions, because: ...

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  • Natural language instructions, though prevalent in many spheres of communication, have only recently begun to receive attention within computational linguistics[5]. Instructions are often accompanied by language intended to signal repetition of the action that they instruct. In order to develop a system that is able to understand instructions, with the goal of executing them, it is necessary to investigate what is meant by various types of repetition, and the different ways in which repetition can be expressed. ...

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