| tech,6-1-P01-1008,bq | interpretation and generation of natural | language | </term> , current systems use manual or semi-automatic | #1764 While paraphrasing is critical both for interpretation and generation of natural language, current systems use manual or semi-automatic methods to collect paraphrases. |
| other,10-1-C94-1030,bq | speech recognition </term> of a <term> natural | language | </term> , it has been difficult to detect | #20624 In optical character recognition and continuous speech recognition of a natural language, it has been difficult to detect error characters which are wrongly deleted and inserted. |
| model,3-1-H92-1026,bq | generative probabilistic model of natural | language | </term> , which we call <term> HBG </term> , | #18901 We describe a generative probabilistic model of natural language, which we call HBG, that takes advantage of detailed linguistic information to resolve ambiguity. |
| other,9-1-P80-1004,bq | process in <term> human understanding of natural | language | </term> . This paper discusses a <term> method | #12453 Interpreting metaphors is an integral and inescapable process in human understanding of natural language. |
| other,34-1-C90-3045,bq | or <term> automatic translation of natural | language | </term> . We present a variant of <term> TAGs | #16463 The unique properties of tree-adjoining grammars (TAG) present a challenge for the application of TAGs beyond the limited confines of syntax, for instance, to the task of semantic interpretation or automatic translation of natural language. |
| tech,9-1-C88-2162,bq | far have not fared well in <term> modeling | language | acquisition </term> . For one thing , <term> | #15747 Computer programs so far have not fared well in modeling language acquisition. |
| other,21-3-N06-4001,bq | context to uncover relationships between <term> | language | </term> and <term> behavioral patterns </term> | #10915 As evidence of its usefulness and usability, it has been used successfully in a research context to uncover relationships betweenlanguage and behavioral patterns in two distinct domains: tutorial dialogue (Kumar et al., submitted) and on-line communities (Arguello et al., 2006). |
| other,8-1-C04-1103,bq | role in many <term> multilingual speech and | language | applications </term> . In this paper , a | #5740 Machine transliteration/back-transliteration plays an important role in many multilingual speech and language applications. |
| other,15-3-P05-1074,bq | how <term> paraphrases </term> in one <term> | language | </term> can be identified using a <term> phrase | #9702 Using alignment techniques from phrase-based statistical machine translation, we show how paraphrases in onelanguage can be identified using a phrase in another language as a pivot. |
| lr,6-1-H92-1003,bq | describes a recently collected <term> spoken | language | corpus </term> for the <term> ATIS ( Air Travel | #18531 This paper describes a recently collected spoken language corpus for the ATIS (Air Travel Information System) domain. |
| other,13-1-P03-1005,bq | Kernel </term> for <term> structured natural | language | data </term> . The <term> HDAG Kernel </term> | #3804 This paper proposes the Hierarchical Directed Acyclic Graph (HDAG) Kernel for structured natural language data. |
| other,8-5-P84-1047,bq | advantages from the point of view of <term> | language | definition </term> are also noted . Representative | #13398 Several advantages from the point of view oflanguage definition are also noted. |
| other,13-1-J86-4002,bq | human-machine interactions </term> in a <term> natural | language | environment </term> . Because a <term> speaker | #14407 The goal of this work is the enrichment of human-machine interactions in a natural language environment. |
| other,15-6-C94-1026,bq | which are selected from different <term> | language | families </term> . In <term> optical character | #20610 Most importantly, the experimental objects are Chinese-English texts, which are selected from differentlanguage families. |
| other,3-2-N06-2009,bq | need </term> . Finding the preferred <term> | language | </term> for such a <term> need </term> is a valuable | #10752 Finding the preferredlanguage for such a need is a valuable task. |
| other,22-1-P90-1014,bq | </term> in the <term> principle-and-parameters | language | framework </term> . First , by investigating | #17358 The principle known as free indexation plays an important role in the determination of the referential properties of noun phrases in the principle-and-parameters language framework. |
| other,13-3-H01-1055,bq | extensively studied by the <term> natural | language | generation community </term> , though rarely | #982 The issue of system response to users has been extensively studied by the natural language generation community, though rarely in the context of dialog systems. |
| tech,21-3-C88-1044,bq | will be incorporated into a <term> natural | language | generation system </term> . This paper summarizes | #15236 This research is part of a larger study of anaphoric expressions, the results of which will be incorporated into a natural language generation system. |
| tech,7-1-P01-1056,bq | training </term> modules of a <term> natural | language | generator </term> have recently been proposed | #2020 Techniques for automatically training modules of a natural language generator have recently been proposed, but a fundamental concern is whether the quality of utterances produced with trainable components can compete with hand-crafted template-based or rule-based approaches. |
| tech,17-1-H01-1070,bq | key prediction </term> and <term> Thai-English | language | identification </term> . The paper also proposes | #1259 This paper proposes a practical approach employing n-gram models and error-correction rules for Thai key prediction and Thai-English language identification. |
