| will demonstrate an application of this | approach | called <term> LCS-Marine </term> . Using <term> | #824 We have built and will demonstrate an application of this approach called LCS-Marine. | ||
| confidence </term> . We describe a three-tiered | approach | for <term> evaluation </term> of <term> spoken | #1199 We describe a three-tiered approach for evaluation of spoken dialogue systems. | ||
| performance </term> . We describe our use of this | approach | in numerous fielded user studies conducted | #1229 We describe our use of this approach in numerous fielded user studies conducted with the U.S. military. | ||
| military . This paper proposes a practical | approach | employing <term> n-gram models </term> and <term> | #1246 This paper proposes a practical approach employing n-gram models and error-correction rules for Thai key prediction and Thai-English language identification. | ||
| of the same <term> source text </term> . Our | approach | yields <term> phrasal and single word lexical | #1802 Our approach yields phrasal and single word lexical paraphrases as well as syntactic paraphrases. | ||
| operational semantics </term> . The value of this | approach | is that as the <term> operational semantics | #1908 The value of this approach is that as the operational semantics of natural language applications improve, even larger improvements are possible. | ||
| tech,32-1-P01-1056,ak | hand-crafted template-based or rule-based | approaches | </term> . In this paper We experimentally | #2049 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. | |
| developed a multi-strategy and multi-source | approach | to <term> question answering </term> which | #2331 Motivated by the success of ensemble methods in machine learning and other areas of natural 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,2-1-N03-2025,ak | quality </term> . A novel <term> bootstrapping | approach | </term> to <term> Named Entity ( NE ) tagging | #3289 A novel bootstrapping approach to Named Entity (NE) tagging using concept-based seeds and successive learners is presented. | |
| successive learners </term> is presented . This | approach | only requires a few common <term> noun or | #3307 This approach only requires a few common noun or pronoun seeds that correspond to the concept for the targeted NE, e.g. he/she/man/woman for PERSON NE. | ||
| </term> . The resulting <term> NE system </term> | approaches | <term> supervised NE performance </term> for | #3381 The resulting NE systemapproaches supervised NE performance for some NE types. | ||
| tech,0-4-N03-3010,ak | robustness and flexibility . <term> Statistical | approach | </term> is much more robust but less accurate | #3536 Statistical approach is much more robust but less accurate. | |
| <term> corpus data </term> . We describe a new | approach | which involves clustering <term> subcategorization | #3907 We describe a new approach which involves clustering subcategorization frame (SCF) distributions using the Information Bottleneck and nearest neighbour methods. | ||
| tech,3-1-P03-1022,ak | </term> . We apply a <term> decision tree based | approach | </term> to <term> pronoun resolution </term> | #3980 We apply a decision tree based approach to pronoun resolution in spoken dialogue. | |
| tech,4-1-P03-1050,ak | paper presents an <term> unsupervised learning | approach | </term> to building a <term> non-English ( | #4438 This paper presents an unsupervised learning approach to building a non-English (Arabic) stemmer. | |
| be given for <term> Arabic </term> , but the | approach | is applicable to any <term> language </term> | #4523 Examples and results will be given for Arabic, but the approach is applicable to any language that needs affix removal. | ||
| tech,1-6-P03-1050,ak | removal </term> . Our <term> resource-frugal | approach | </term> results in 87.5 % <term> agreement </term> | #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. | |
| learning </term> . In this paper , we evaluate an | approach | to automatically acquire <term> sense-tagged | #4830 In this paper, we evaluate an approach to automatically acquire sense-tagged training data from English-Chinese parallel corpora, which are then used for disambiguating the nouns in the SENSEVAL-2 English lexical sample task. | ||
| accuracy difference </term> between the two | approaches | is only 14.0 % , and the difference could | #4887 On a subset of the most difficult SENSEVAL-2 nouns, the accuracy difference between the two approaches is only 14.0%, and the difference could narrow further to 6.5% if we disregard the advantage that manually sense-tagged data have in their sense coverage. | ||
| tech,5-3-H05-1032,ak | </term> . It is found that the <term> Bayesian | approach | </term> generally leverages performance of | #5411 It is found that the Bayesian approach generally leverages performance of a summarizer, at times giving it a significant lead over non-Bayesian models. |
