| other,13-3-H01-1055,ak | has been extensively studied by the <term> | natural | language generation community </term> , though | #981 The issue of system response to users has been extensively studied by thenatural language generation community, though rarely in the context of dialog systems. | |
| tech,6-1-P01-1008,ak | for <term> interpretation and generation of | natural | language </term> , current systems use <term> | #1764 While paraphrasing is critical both for interpretation and generation of natural language, current systems use manual or semi-automatic methods to collect paraphrases. | |
| tech,14-2-P01-1009,ak | warrant serious attention , yet present <term> | natural | language search engines </term> perform poorly | #1861 These words appear frequently enough in dialog to warrant serious attention, yet presentnatural language search engines perform poorly on queries containing them. | |
| tech,12-4-P01-1009,ak | <term> operational semantics </term> of <term> | natural | language applications </term> improve , even | #1916 The value of this approach is that as the operational semantics ofnatural language applications improve, even larger improvements are possible. | |
| tech,7-1-P01-1056,ak | automatically training modules of a <term> | natural | language generator </term> have recently | #2020 Techniques for automatically training modules of anatural 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,14-1-N03-1004,ak | learning </term> and other areas of <term> | natural | language processing </term> , we developed | #2321 Motivated by the success of ensemble methods in machine learning and other areas ofnatural language processing, we developed a multi-strategy and multi-source approach to question answering which is based on combining the results from different answering agents searching for answers in multiple corpora. | |
| tech,11-1-N03-3010,ak | novel <term> Cooperative Model </term> for <term> | natural | language understanding </term> in a <term> | #3489 In this paper, we propose a novel Cooperative Model fornatural language understanding in a dialogue system. | |
| other,13-1-P03-1005,ak | HDAG ) Kernel </term> for <term> structured | natural | language data </term> . The <term> HDAG Kernel | #3804 This paper proposes the Hierarchical Directed Acyclic Graph (HDAG) Kernel for structured natural language data. | |
| tech,1-1-I05-2043,ak | the <term> WSD models </term> . Using <term> | natural | language processing </term> , we carried | #6531 Usingnatural language processing, we carried out a trend survey on Japanese natural language processing studies that have been done over the last ten years. | |
| tech,12-1-I05-2043,ak | carried out a trend survey on <term> Japanese | natural | language processing studies </term> that | #6543 Using natural language processing, we carried out a trend survey on Japanese natural language processing studies that have been done over the last ten years. | |
| tech,14-1-I05-2048,ak | currently one of the hot spots in <term> | natural | language processing </term> . Over the last | #6751 Statistical machine translation (SMT) is currently one of the hot spots innatural language processing. | |
| tech,8-12-J05-1003,ak | experiments in this article are on <term> | natural | language parsing ( NLP ) </term> , the approach | #8309 Although the experiments in this article are onnatural language parsing (NLP), the approach should be applicable to many other NLP problems which are naturally framed as ranking tasks, for example, speech recognition, machine translation, or natural language generation. | |
| tech,43-12-J05-1003,ak | <term> machine translation </term> , or <term> | natural | language generation </term> . We present | #8344 Although the experiments in this article are on natural language parsing (NLP), the approach should be applicable to many other NLP problems which are naturally framed as ranking tasks, for example, speech recognition, machine translation, ornatural language generation. | |
| other,12-1-P80-1004,ak | <term> human understanding </term> of <term> | natural | language </term> . This paper discusses a | #13389 Interpreting metaphors is an integral and inescapable process in human understanding ofnatural language. | |
| tech,1-1-P80-1019,ak | </term> are also discussed . Current <term> | natural | language interfaces </term> have concentrated | #13465 Currentnatural language interfaces have concentrated largely on determining the literal meaning of input from their users. | |
| tech,13-2-P80-1019,ak | underpinning , much recent work suggests that <term> | natural | language interfaces </term> will never appear | #13495 While such decoding is an essential underpinning, much recent work suggests thatnatural language interfaces will never appear cooperative or graceful unless they also incorporate numerous non-literal aspects of communication, such as robust communication procedures. | |
| tech,20-4-P80-1019,ak | valuable methods of more traditional <term> | natural | language interfaces </term> . When people | #13599 The paper proposes interfaces based on a judicious mixture of these techniques and the still valuable methods of more traditionalnatural language interfaces. | |
| other,3-1-P80-1026,ak | interfaces </term> . When people use <term> | natural | language </term> in natural settings , they | #13606 When people usenatural language in natural settings, they often use it ungrammatically, missing out or repeating words, breaking-off and restarting, speaking in fragments, etc.. | |
| people use <term> natural language </term> in | natural | settings , they often use it ungrammatically | #13609 When people use natural language in natural settings, they often use it ungrammatically, missing out or repeating words, breaking-off and restarting, speaking in fragments, etc.. | ||
| other,7-3-P80-1026,ak | computer system </term> wishes to accept <term> | natural | language input </term> from its users on | #13657 If a computer system wishes to acceptnatural language input from its users on a routine basis, it must display a similar indifference. |
