| lr-prod,12-6-H92-1060,bq | system </term> on the <term> February '92 test | sentences | </term> , and discuss some issues with regard | #19513 We also report here on the performance of the system on the February '92 test sentences, and discuss some issues with regard to the evaluation methodology. |
| lr,3-3-H92-1026,bq | way . We use a <term> corpus of bracketed | sentences | </term> , called a <term> Treebank </term> , | #18949 We use a corpus of bracketed sentences, called a Treebank, in combination with decision tree building to tease out the relevant aspects of a parse tree that will determine the correct parse of a sentence. |
| other,12-2-J05-4003,bq | classifier </term> that , given a pair of <term> | sentences | </term> , can reliably determine whether | #9010 We train a maximum entropy classifier that, given a pair ofsentences, can reliably determine whether or not they are translations of each other. |
| other,25-2-I05-5008,bq | grammaticality </term> : at least 99 % correct <term> | sentences | </term> ; ( ii ) their <term> equivalence in | #8495 We measured the quality of the paraphrases produced in an experiment, i.e., (i) their grammaticality: at least 99% correctsentences; (ii) their equivalence in meaning: at least 96% correct paraphrases either by meaning equivalence or entailment; and, (iii) the amount of internal lexical and syntactical variation in a set of paraphrases: slightly superior to that of hand-produced sets. |
| other,9-2-C04-1106,bq | quite dubious about <term> analogies between | sentences | </term> : they would not be enough numerous | #5895 But computational linguists seem to be quite dubious about analogies between sentences: they would not be enough numerous to be of any use. |
| other,40-1-N01-1003,bq | how to combine them into one or more <term> | sentences | </term> . In this paper , we present <term> | #1333 Sentence planning is a set of inter-related but distinct tasks, one of which is sentence scoping, i.e. the choice of syntactic structure for elementary speech acts and the decision of how to combine them into one or moresentences. |
| other,13-2-E06-1031,bq | result in correct or almost correct <term> | sentences | </term> . In this paper , we will present | #10352 In many cases though such movements still result in correct or almost correctsentences. |
| other,34-3-I05-2021,bq | <term> words </term> in <term> source language | sentences | </term> . Surprisingly however , the <term> | #7892 At the same time, the recent improvements in the BLEU scores of statistical machine translation (SMT) suggests that SMT models are good at predicting the right translation of the words in source language sentences. |
| other,18-1-C90-2032,bq | <term> dependency structure </term> of <term> | sentences | </term> . The <term> DoPS system </term> extracts | #16302 This paper proposes document oriented preference sets(DoPS) for the disambiguation of the dependency structure ofsentences. |
| other,13-5-P06-2059,bq | corpus </term> consisting of 126,610 <term> | sentences | </term> . This paper examines what kind of | #11468 In our experiment, the method could construct a corpus consisting of 126,610sentences. |
| other,14-4-C90-2032,bq | structures </term> of <term> Japanese patent claim | sentences | </term> . This paper describes the framework | #16353 Implementation and empirical results are described for the the analysis of dependency structures of Japanese patent claim sentences. |
| other,33-4-C88-2086,bq | presuppositional nature </term> of these <term> | sentences | </term> . We have developed a <term> computational | #15432 By reappraising these insightful counterexamples, the inferential theory for natural language presuppositions described in /Mercer 1987, 1988/ gives a simple and straightforward explanation for the presuppositional nature of thesesentences. |
| other,31-3-N04-1022,bq | </term> of <term> source and target language | sentences | </term> . We report the performance of the | #6610 We describe a hierarchy of loss functions that incorporate different levels of linguistic information from word strings, word-to-word alignments from an MT system, and syntactic structure from parse-trees of source and target language sentences. |
| <term> English stemmer </term> and a small ( 10K | sentences | ) <term> parallel corpus </term> as its sole | #4466 The stemming model is based on statistical machine translation and it uses an English stemmer and a small (10K sentences) parallel corpus as its sole training resources. | |
| other,5-5-C88-2160,bq | <term> paraphrasing </term> ambiguous <term> | sentences | </term> are presented . Computer programs | #15733 Some examples of paraphrasing ambiguoussentences are presented. |
| other,5-3-N04-1024,bq | identifies <term> features </term> of <term> | sentences | </term> based on <term> semantic similarity | #6695 This system identifies features ofsentences based on semantic similarity measures and discourse structure. |
| other,4-2-P06-4011,bq | articles </term> . In our approach , <term> | sentences | </term> in a given <term> abstract </term> are | #11717 In our approach,sentences in a given abstract are analyzed and labeled with a specific move in light of various rhetorical functions. |
| tech,6-1-J05-4003,bq | method </term> for <term> discovering parallel | sentences | </term> in <term> comparable , non-parallel | #8991 We present a novel method for discovering parallel sentences in comparable, non-parallel corpora. |
| other,10-1-N04-1024,bq | </term> includes a capability that labels <term> | sentences | </term> in student <term> writing </term> with | #6655 CriterionSM Online Essay Evaluation Service includes a capability that labelssentences in student writing with essay-based discourse elements (e.g., thesis statements). |
| other,8-2-A88-1001,bq | and heuristically-produced complete <term> | sentences | </term> in <term> text </term> or <term> text-to-speech | #14890 Multimedia answers include videodisc images and heuristically-produced completesentences in text or text-to-speech form. |
