| intelligent mobile agents </term> that mediate | between | <term> users </term> and <term> information sources | #808 We integrate a spoken language understanding system with intelligent mobile agents that mediate between users and information sources. | ||
| annotators </term> can reliably differentiate | between | semantically coherent and incoherent <term> | #2493 We conducted an annotation experiment and showed that human annotators can reliably differentiate between semantically coherent and incoherent speech recognition hypotheses. | ||
| quality </term> shows a close correlation | between | the <term> automatic parse-based evaluation | #2871 An experimental evaluation of summarization quality shows a close correlation between the automatic parse-based evaluation and a manual evaluation of generated strings. | ||
| Those <term> hubs </term> mark the boundary | between | <term> root </term> and <term> suffix </term> , | #3214 Those hubs mark the boundary between root and suffix, achieving similar performance to more complex mixtures of techniques. | ||
| </term> , the <term> accuracy difference </term> | between | the two approaches is only 14.0 % , and | #4884 On a subset of the most difficult SENSEVAL-2 nouns, the accuracy differencebetween 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. | ||
| tool is therefore designed to distinguish | between | the <term> anaphoric occurrences </term> of | #6137 This tool is therefore designed to distinguish between the anaphoric occurrences of il, for which an anaphora resolution system has to look for an antecedent, and the expletive occurrences of this pronoun, for which it does not make sense to look for an antecedent. | ||
| . This study establishes the equivalence | between | the standard use of <term> BLEU </term> in <term> | #6272 This study establishes the equivalence between the standard use of BLEU in word n-grams and its application at the character level. | ||
| research area as well as the relationship | between | research organizations and research areas | #6581 We determined the changes in the number of papers published for each research organization and on each research area as well as the relationship between research organizations and research areas. | ||
| wordnets </term> built upon correspondences | between | different <term> languages </term> can play | #7060 Parallel wordnets built upon correspondences between different languages can play a crucial role in multilingual knowledge processing. | ||
| Since there is no <term> homomorphism </term> | between | pairs of <term> monolingual wordnets </term> | #7078 Since there is no homomorphismbetween pairs of monolingual wordnets, we must rely on lexical semantic relation (LSR) mappings to ensure conceptual cohesion. | ||
| there is not a large performance difference | between | the two <term> models </term> . The results | #7769 Experimental results showed that the proposed method achieves almost 60% accuracy and that there is not a large performance difference between the two models. | ||
| allows us to better examine the interplay | between | <term> smoothing </term> and parsing results | #9010 In addition to the high accuracy of the model, the use of smoothing in an unlexicalized parser allows us to better examine the interplay between smoothing and parsing results. | ||
| Extracting <term> semantic relationships </term> | between | <term> entities </term> is challenging . This | #9268 Extracting semantic relationshipsbetween entities is challenging. | ||
| structure , with strong <term> dependencies </term> | between | <term> arguments </term> . We show how to build | #10085 This stands in stark contrast to the linguistic observation that a core argument frame is a joint structure, with strong dependenciesbetween arguments. | ||
| work well only when there is a good match | between | the <term> training and test data </term> with | #10447 Traditional machine learning techniques have been applied to this problem with reasonable success, but they have been shown to work well only when there is a good match between the training and test data with respect to topic. | ||
| of reference </term> , and flexible links | between | <term> semantics </term> and <term> collaborative | #10658 Our contributions include a concise, modular architecture with reversible processes of understanding and generation, an information-state model of reference, and flexible links between semantics and collaborative problem solving. | ||
| demonstrate an additional increase of correlation | between | <term> automatic evaluation measures </term> | #11391 Results from experiments with word dependent substitution costs will demonstrate an additional increase of correlation between automatic evaluation measures and human judgment. | ||
| research context to uncover relationships | between | <term> language </term> and behavioral patterns | #11851 As evidence of its usefulness and usability, it has been used successfully in a research context to uncover relationships between language and behavioral patterns in two distinct domains: tutorial dialogue (Kumar et al., submitted) and on-line communities (Arguello et al., 2006). | ||
| disambiguate various <term> relations </term> | between | <term> named entities </term> by use of various | #12264 This paper presents an unsupervised learning approach to disambiguate various relationsbetween named entities by use of various lexical and syntactic features from the contexts. | ||
| examines what kind of <term> similarity </term> | between | <term> words </term> can be represented by | #12414 This paper examines what kind of similaritybetween words can be represented by what kind of word vectors in the vector space model. |
