| 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 <term> user studies </term> | #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 | #1801 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 | #1907 The value of this approach is that as the operational semantics of natural language applications improve, even larger improvements are possible. | |
| tech,21-1-N03-1004,bq | developed a <term> multi-strategy and multi-source | approach | to question answering </term> which is based | #2330 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,bq | quality </term> . A novel <term> bootstrapping | approach | </term> to <term> Named Entity ( NE ) tagging | #3288 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 <term> common noun </term> | #3306 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. | |
| tech,0-4-N03-3010,bq | robustness and flexibility . <term> Statistical | approach | </term> is much more robust but less accurate | #3535 Statistical approach is much more robust but less accurate. |
| <term> corpus data </term> . We describe a new | approach | which involves clustering <term> subcategorization | #3906 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,bq | </term> . We apply a <term> decision tree based | approach | </term> to <term> pronoun resolution </term> | #3979 We apply a decision tree based approach to pronoun resolution in spoken dialogue. |
| tech,4-1-P03-1050,bq | paper presents an <term> unsupervised learning | approach | </term> to building a <term> non-English ( | #4436 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> | #4521 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,bq | removal </term> . Our <term> resource-frugal | approach | </term> results in 87.5 % <term> agreement </term> | #4534 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 | #4828 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. | |
| presents a <term> machine learning </term> | approach | to bare <term> sluice disambiguation </term> | #5155 This paper presents a machine learningapproach to bare sluice disambiguation in dialogue. | |
| between <term> objects </term> . However , such an | approach | does not work well when there is no distinctive | #5635 However, such an approach does not work well when there is no distinctive attribute among objects. | |
| tech,12-2-C04-1112,bq | </term> , we introduce a <term> lemma-based | approach | </term> . The advantage of this novel method | #6022 Instead of building individual classifiers per ambiguous wordform, we introduce a lemma-based approach. |
| text as a coherent <term> corpus </term> . Our | approach | is based on the idea that one person tends | #6140 Our approach is based on the idea that one person tends to use one expression for one meaning. |
