Concordance

Query Our 29 (1,352.0 per million)

</term> as an <term> edit operation </term> .	Our	<term> measure </term> can be exactly calculated	#10375 In this paper, we will present a new evaluation measure which explicitly models block reordering as an edit operation. Our measure can be exactly calculated in quadratic time.
of <term> unsupervised WSD systems </term> .	Our	<term> combination methods </term> rely on <term>	#11009 We investigate several voting- and arbiter-based combination strategies over a diverse pool of unsupervised WSD systems. Our combination methods rely on predominant senses which are derived automatically from raw text.
</term> , <term> missing periods </term> , etc .	Our	solution to these problems is to make use	#13026 However, a great deal of natural language texts e.g., memos, rough drafts, conversation transcripts etc., have features that differ significantly from neat texts, posing special problems for readers, such as misspelled words, missing words, poor syntactic construction, missing periods, etc. Our solution to these problems is to make use of expectations, based both on knowledge of surface English and on world knowledge of the situation being described.
break down , <term> communication </term> .	Our	goal is to recognize and isolate such <term>	#14498 Such mistakes can slow, and possibly break down, communication. Our goal is to recognize and isolate such miscommunications and circumvent them.
embedded within <term> disjunctions </term> .	Our	interpretation differs from that of Pereira	#14751 This semantics for feature structures extends the ideas of Pereira and Shieber [11], by providing an interpretation for values which are specified by disjunctions and path values embedded within disjunctions. Our interpretation differs from that of Pereira and Shieber by using a logical model in place of a denotational semantics.
it is often computationally inefficient .	Our	<term> model </term> allows a careful examination	#14805 Unification is attractive, because of its generality, but it is often computationally inefficient. Our model allows a careful examination of the computational complexity of unification.
complex <term> linguistic databases </term> .	Our	most important task in building the <term>	#17293 If we want valuable lexicons and grammars to achieve complex natural language processing, we must provide very powerful tools to help create and ensure the validity of such complex linguistic databases. Our most important task in building the editor was to define a set of coherence rules that could be computationally applied to ensure the validity of lexical entries.
of the total <term> processing time </term> .	Our	study has concluded that we only need to	#20298 In TDMT, example-retrieval (ER), i.e., retrieving examples most similar to an input expression, is the most dominant part of the total processing time. Our study has concluded that we only need to implement the ER for expressions including a frequent word on APs.
</term> with <term> text understanding </term> .	Our	<term> document understanding technology </term>	#21388 Because of the complexity of documents and the variety of applications which must be supported, document understanding requires the integration of image understanding with text understanding. Our document understanding technology is implemented in a system called IDUS (Intelligent Document Understanding System), which creates the data for a text retrieval application and the automatic generation of hypertext links.


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