| presuppositions </term> that is presented | in | / Gazdar 1979 / . / Soames 1982 / provides | #15363 /Soames 1979/ provides some counterexamples to the theory of natural language presuppositions that is presented in /Gazdar 1979/. | |
| language presuppositions </term> described | in | / Mercer 1987 , 1988 / gives a simple and | #15413 By reappraising these insightful counterexamples, the inferential theory for natural language presuppositions described in /Mercer 1987, 1988/ gives a simple and straightforward explanation for the presuppositional nature of these sentences. | |
| Mercer 1987 / rejects the solution found | in | / Soames 1982 / leaving these counterexamples | #15389 /Mercer 1987/ rejects the solution found in /Soames 1982/ leaving these counterexamples unexplained. | |
| <term> resource-frugal approach </term> results | in | 87.5 % <term> agreement </term> with a state | #4536 Our resource-frugal approach results in 87.5% agreement with a state of the art, proprietary Arabic stemmer built using rules, affix lists, and human annotated text, in addition to an unsupervised component. | |
| probabilistic context-free grammars </term> working | in | a ' synchronous ' way . Two <term> hardness | #7469 These models can be viewed as pairs of probabilistic context-free grammars working in a 'synchronous' way. | |
| roughly corresponding to <term> arcs </term> | in | a <term> chart </term> . <term> Chart-like parsing | #20912 It is also a drastic generalization of chart Parsing, partial instantiation of clauses in a program roughly corresponding to arcsin a chart. | |
| determines that a <term> homophone </term> is misused | in | a <term> compound noun </term> if one or both | #20493 The method accurately determines that a homophone is misused in a compound noun if one or both of its neighbors is not a member of the semantic set defined by the homophone. | |
| </term> , the <term> theory </term> is expressed | in | a <term> content-independent formalism </term> | #11940 Like logic, the theory is expressed in a content-independent formalism. | |
| </term> , or <term> goals </term> , at each point | in | a <term> conversation </term> , difficulties | #14437 Because a speaker and listener cannot be assured to have the same beliefs, contexts, perceptions, backgrounds, or goals, at each point in a conversation, difficulties and mistakes arise when a listener interprets a speaker's utterance. | |
| control the <term> top-down derivation </term> | in | a declarative way . This <term> generation | #16241 The system utilizes typed feature structures to control the top-down derivationin a declarative way. | |
| <term> natural language understanding </term> | in | a <term> dialogue system </term> . We build | #3491 In this paper, we propose a novel Cooperative Model for natural language understandingin a dialogue system. | |
| the processing of <term> utterances </term> | in | a <term> discourse </term> . <term> Discourse | #14322 This theory provides a framework for describing the processing of utterancesin a discourse. | |
| that , it successfully classifies 73.2 % | in | a <term> German corpus </term> of 2.284 <term> | #2518 An evaluation of our system against the annotated data shows that, it successfully classifies 73.2% in a German corpus of 2.284 SRHs as either coherent or incoherent (given a baseline of 54.55%). | |
| In our approach , <term> sentences </term> | in | a given <term> abstract </term> are analyzed | #11718 In our approach, sentencesin a given abstract are analyzed and labeled with a specific move in light of various rhetorical functions. | |
| <term> comma checker </term> to be integrated | in | a <term> grammar checker </term> for <term> Basque | #11220 In this paper, we describe the research using machine learning techniques to build a comma checker to be integrated in a grammar checker for Basque. | |
| , a <term> hub </term> is a <term> node </term> | in | a <term> graph </term> with <term> in-degree </term> | #3178 For our purposes, a hub is a nodein a graph with in-degree greater than one and out-degree greater than one. | |
| <term> training examples </term> and organized | in | a <term> hierarchy </term> ; this task was | #15853 First, how linguistic concepts are acquired from training examples and organized in a hierarchy; this task was discussed in previous papers [Zernik87]. | |
| wrongly substituted , deleted or inserted | in | a <term> Japanese bunsetsu </term> and an <term> | #20660 In order to judge three types of the errors, which are characters wrongly substituted, deleted or inserted in a Japanese bunsetsu and an English word, and to correct these errors, this paper proposes new methods using m-th order Markov chain model for Japanese kanji-kana characters and English alphabets, assuming that Markov probability of a correct chain of syllables or kanji-kana characters is greater than that of erroneous chains. | |
| </term> on <term> co-occurrence patterns </term> | in | a large <term> corpus </term> . To a large | #16628 This paper presents an automatic scheme for collecting statistics on co-occurrence patternsin a large corpus. | |
| interesting information piece would be found | in | a <term> large database </term> . Traditional | #50 The question is, however, how an interesting information piece would be found in a large database. |
