| tech,25-2-H01-1041,bq | meaning representation </term> called a <term> | semantic | frame </term> . The key features of the <term> | #436 The CCLINC Korean-to-English translation system consists of two core modules, language understanding and generation modules mediated by a language neutral meaning representation called asemantic frame. |
| measure(ment),19-3-H01-1058,bq | performance </term> ( typically , <term> word or | semantic | error rate </term> ) from a list of <term> | #1091 The oracle knows the reference word string and selects the word string with the best performance (typically, word or semantic error rate) from a list of word strings, where each word string has been obtained by using a different LM. |
| other,20-2-N03-1012,bq | hypotheses ( SRH ) </term> in terms of their <term> | semantic | coherence </term> . We conducted an <term> | #2476 We apply our system to the task of scoring alternative speech recognition hypotheses (SRH) in terms of theirsemantic coherence. |
| other,10-1-P03-1009,bq | <term> clustering </term> in inducing <term> | semantic | verb classes </term> from undisambiguated | #3894 Previous research has demonstrated the utility of clustering in inducingsemantic verb classes from undisambiguated corpus data. |
| other,6-2-P03-1068,bq | backbone of the <term> annotation </term> are <term> | semantic | roles </term> in the <term> frame semantics | #4969 The backbone of the annotation aresemantic roles in the frame semantics paradigm. |
| tech,13-4-P03-1068,bq | </term> and <term> ambiguity </term> in <term> | semantic | annotation </term> . We investigate the <term> | #5004 On this basis, we discuss the problems of vagueness and ambiguity insemantic annotation. |
| human agreement </term> . Motivated by this | semantic | criterion we analyze the empirical quality | #5326 Motivated by this semantic criterion we analyze the empirical quality of distributional word feature vectors and its impact on word similarity results, proposing an objective measure for evaluating feature vector quality. | |
| other,8-3-N04-1024,bq | </term> of <term> sentences </term> based on <term> | semantic | similarity measures </term> and <term> discourse | #6698 This system identifies features of sentences based onsemantic similarity measures and discourse structure. |
| tech,16-1-I05-5003,bq | related to the task of <term> sentence-level | semantic | equivalence classification </term> . This | #8333 The task of machine translation (MT) evaluation is closely related to the task of sentence-level semantic equivalence classification. |
| other,25-2-I05-5003,bq | <term> classifiers </term> to predict <term> | semantic | equivalence </term> and <term> entailment </term> | #8362 This paper investigates the utility of applying standard MT evaluation methods (BLEU, NIST, WER and PER) to building classifiers to predictsemantic equivalence and entailment. |
| other,13-1-P06-1052,bq | problem </term> : Given an <term> underspecified | semantic | representation ( USR ) </term> of a <term> | #11132 We present an efficient algorithm for the redundancy elimination problem: Given an underspecified semantic representation (USR) of a scope ambiguity, compute an USR with fewer mutually equivalent readings. |
| other,6-1-T78-1031,bq | performing <term> inference </term> in <term> | semantic | networks </term> are presented and compared | #12056 Two styles of performing inference insemantic networks are presented and compared. |
| other,8-5-T78-1031,bq | rules </term> can be constructed in a <term> | semantic | network </term> using a variant of a <term> | #12137 Node-based inference rules can be constructed in asemantic network using a variant of a predicate calculus notation. |
| other,1-4-P82-1035,bq | being described . These <term> syntactic and | semantic | expectations </term> can be used to figure | #13058 These syntactic and semantic expectations can be used to figure out unknown words from context, constrain the possible word-senses of words with multiple meanings (ambiguity), fill in missing words (elllpsis), and resolve referents (anaphora). |
| other,1-3-P84-1047,bq | </term> are grouped together . Like <term> | semantic | grammar </term> , this allows easy exploitation | #13351 Likesemantic grammar, this allows easy exploitation of limited domain semantics. |
| other,25-3-C88-1007,bq | obviating the need for answers to <term> | semantic | questions </term> that we do not yet have | #15088 The principle advantage of this approach is that knowledge concerning translation equivalence of expressions may be directly exploited, obviating the need for answers tosemantic questions that we do not yet have. |
| other,31-1-C90-3045,bq | </term> , for instance , to the task of <term> | semantic | interpretation </term> or <term> automatic | #16456 The unique properties of tree-adjoining grammars (TAG) present a challenge for the application of TAGs beyond the limited confines of syntax, for instance, to the task ofsemantic interpretation or automatic translation of natural language. |
| other,3-1-C90-3063,bq | defeasibility </term> . Manual acquisition of <term> | semantic | constraints </term> in broad domains is very | #16607 Manual acquisition ofsemantic constraints in broad domains is very expensive. |
| other,8-3-C90-3063,bq | these <term> statistics </term> reflect <term> | semantic | constraints </term> and thus are used to | #16641 To a large extent, these statistics reflectsemantic constraints and thus are used to disambiguate anaphora references and syntactic ambiguities. |
| other,18-6-C90-3063,bq | statistics </term> indeed reflect the <term> | semantic | constraints </term> and thus provide a basis | #16709 The results of the experiment show that in most of the cases the cooccurrence statistics indeed reflect thesemantic constraints and thus provide a basis for a useful disambiguation tool. |
