Question answering using ontological semantics

Beale, Stephen; Lavoie, Benoit; McShane, Marjorie; Ниренбург, Сергей; Korelsky, Tanya

doi:10.3115/1628275.1628281

Cited by 15 publications

(11 citation statements)

References 5 publications

(4 reference statements)

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“…OntoSem is a multilingual text processing environment that takes as input unrestricted text and, using a suite of static resources and processors, automatically creates text-meaning representations (TMRs), which can then be used as the basis for many NLP applications, including MT, question answering, knowledge extraction, automated reasoning (see for example Beale et al 1995Beale et al , 2004. At various stages in its development, OntoSem has processed texts in languages including English, Spanish, Chinese, Arabic and Persian, to varying degrees of lexical coverage.…”

Section: The Theory Of Ontological Semantics and The Ontosem Environmentmentioning

confidence: 99%

An NLP Lexicon as a Largely Language-Independent Resource

McShane

Ниренбург

Beale

2005

Machine Translation

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This paper describes salient aspects of the OntoSem lexicon of English, a lexicon whose semantic descriptions can either be grounded in a language-independent ontology, rely on extra-ontological expressive means, or exploit a combination of the two. The variety of descriptive means, as well as the conceptual complexity of semantic description to begin with, necessitates that OntoSem lexicons be compiled primarily manually. However, once a semantic description is created for a lexeme in one language, it can be reused in others, often with little or no modification. Said differently, the challenge in building a semantic lexicon is describing semantics; once the semantics are described, it is relatively straightforward to connect given meanings to the appropriate head words in other languages. In this paper we provide a brief overview of the OntoSem lexicon and processing environment, orient our approach to lexical semantics among others in the field, and describe in more detail what we mean by the largely language-independent lexicon. Finally, we suggest reasons why our resources might be of interest to the larger community.

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Section: The Theory Of Ontological Semantics and The Ontosem Environmentmentioning

confidence: 99%

An NLP Lexicon as a Largely Language-Independent Resource

McShane

Ниренбург

Beale

2005

Machine Translation

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“…TMRs are produced as a result of semantic analysis which uses knowledge sources such as lexicon, onomasticon and fact repository to resolve ambiguities and time references. TMRs have been used as the substrate for questionanswering [26], machine translation [12] and knowledge extraction. Once the TMRs are generated, OntoSem2OWL converts them to an equivalent OWL representation.…”

Section: Ontosemmentioning

confidence: 99%

Using a Natural Language Understanding System to Generate Semantic Web Content

Java

Ниренбург

McShane

et al. 2007

International Journal on Semantic Web and Information Systems

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We describe our research on automatically generating rich semantic annotations of text and making it available on the Semantic Web. In particular, we discuss the challenges involved in adapting the OntoSem natural language processing system for this purpose. OntoSem, an implementation of the theory of ontological semantics under continuous development for over fifteen years, uses a specially constructed NLP-oriented ontology and an ontologicalsemantic lexicon to translate English text into a custom ontology-motivated knowledge representation language, the language of text meaning representations (TMRs). OntoSem concentrates on a variety of ambiguity resolution tasks as well as processing unexpected input and reference. To adapt OntoSem's representation to the Semantic Web, we developed a translation system, OntoSem2OWL, between the TMR language into the Semantic Web language OWL. We next used OntoSem and OntoSem2OWL to support SemNews, an experimental web service that monitors RSS news sources, processes the summaries of the news stories and publishes a structured representation of the meaning of the text in the news story. 2 The alternative on the supply side is, then, automatic annotation. Within the current state of the art, automatically produced annotations are roughly at the level attainable by the latest information extraction techniques -a reasonably good level of capturing named entities with a somewhat less successful categorization of such entities (e.g., decidingwhether Jordan is used as the first name of an individual or a reference to the Hashemite kingdom). Extracting more advanced types of semantic information, for example, types of events (to say nothing about determining semantic arguments, "case roles" in AI terminology), is not quite within the current information extraction capabilities, though work in this direction is ongoing. Indeed, semantic annotation is at the moment an active subfield of computational linguistics, where annotated corpora are intended for use by machine learning approaches to building natural language processing capabilities.On the demand side of the Semantic Web, a core capability is improving the precision of the Web search which will be facilitated by detailed semantic annotations that are unambiguous and sufficiently detailed to support the search engine in making fine-grained distinctions in calculating scores of documents. Another core capability is to transcend the level of document retrieval and instead return as answers to user queries specially generated pragmatically and stylistically appropriate responses. To attain this capability, intelligent agents must rely on very detailed semantic annotations of texts. We believe that such annotations will be, for all intents and purposes, complete text meaning representations, not just sets of semantic or pragmatic markers (and certainly not templates filled with uninterpreted snippets of the input text that are generated by the current information extraction methods).To attain such goals, Semantic Web agents must be ...

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“…TMRs have been used as the substrate for question-answering (e.g., [12]), machine translation (e.g., [13]) and knowledge extraction, and were also used as the basis for reasoning in the questionanswering system AQUA, where they supplied knowledge to showcase temporal reasoning capabilities of the JTP object-oriented reasoning system [14]. Text analysis relies on the following static knowledge resources:…”

Section: Ontosemmentioning

confidence: 99%