| 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> | #2492 We conducted an annotation experiment and showed that human annotators can reliably differentiate between semantically coherent and incoherent speech recognition hypotheses. | |
| </term> quality shows a close correlation | between | the <term> automatic parse-based evaluation | #2870 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> , | #3213 Those hubs mark the boundary between root and suffix, achieving similar performance to more complex mixtures of techniques. | |
| </term> , the <term> accuracy </term> difference | between | the two approaches is only 14.0 % , and | #4882 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. | |
| objects </term> and <term> binary relations </term> | between | <term> objects </term> . However , such an | #5628 Past work of generating referring expressions mainly utilized attributes of objects and binary relationsbetween objects. | |
| direct orthographical mapping ( DOM ) </term> | between | two different <term> languages </term> is presented | #5766 In this paper, a novel framework for machine transliteration/back transliteration that allows us to carry out direct orthographical mapping (DOM)between two different languages is presented. | |
| other,3-1-C04-1106,bq | significantly . The reality of <term> analogies | between | words </term> is refuted by noone ( e.g. | #5846 The reality of analogies between words is refuted by noone (e.g., I walked is to to walk as I laughed is to to laugh, noted I walked : to walk :: I laughed : to laugh). |
| other,9-2-C04-1106,bq | to be quite dubious about <term> analogies | between | sentences </term> : they would not be enough | #5894 But computational linguists seem to be quite dubious about analogies between sentences: they would not be enough numerous to be of any use. |
| the <term> co-occurrence distribution </term> | between | pairs of <term> terms </term> , an <term> independence | #6335 The framework is composed of a novel algorithm to efficiently compute the co-occurrence distributionbetween pairs of terms, an independence model, and a parametric affinity model. | |
| </term> to compute <term> similarity </term> | between | <term> words </term> or use <term> lexical affinity | #6364 In comparison with previous models, which either use arbitrary windows to compute similaritybetween words or use lexical affinity to create sequential models, in this paper we focus on models intended to capture the co-occurrence patterns of any pair of words or phrases at any distance in the corpus. | |
| <term> essay question </term> and relatedness | between | <term> discourse elements </term> . <term> Intra-sentential | #6726 A support vector machine uses these features to capture breakdowns in coherence due to relatedness to the essay question and relatedness between discourse elements. | |
| . This study establishes the equivalence | between | the standard use of <term> BLEU </term> in <term> | #7729 This study establishes the equivalence between the standard use of BLEU in word n-grams and its application at the character level. | |
| demonstrate an additional increase of correlation | between | <term> automatic evaluation measures </term> | #10454 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 <term> behavioral | #10914 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). | |
| </term> to disambiguate various relations | between | <term> name entities </term> by use of various | #11327 This paper presents an unsupervised learning approach to disambiguate various relations between name 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 | #11477 This paper examines what kind of similaritybetween words can be represented by what kind of word vectors in the vector space model. | |
| <term> arc </term> or a <term> path of arcs </term> | between | two given <term> nodes </term> to be inferred | #12073 Path-based inference allows an arc or a path of arcsbetween two given nodes to be inferred from the existence of another specified path between the same two nodes. | |
| existence of another specified <term> path </term> | between | the same two <term> nodes </term> . <term> Path-based | #12087 Path-based inference allows an arc or a path of arcs between two given nodes to be inferred from the existence of another specified pathbetween the same two nodes. | |
| roles . Some examples of the difference | between | <term> Japanese sentence structure </term> | #13298 Some examples of the difference between Japanese sentence structure and English sentence structure, which is vital to machine translation are also discussed together with various interesting ambiguities. |
