| tech,25-2-H01-1041,bq | The <term> CCLINC Korean-to-English translation system </term> consists of two <term> core modules </term> , <term> language understanding and generation modules </term> mediated by a <term> language neutral meaning representation </term> called a <term> semantic frame </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 | The <term> oracle </term> knows the <term> reference word string </term> and selects the <term> word string </term> with the best <term> performance </term> ( typically , <term> word or semantic error rate </term> ) from a list of <term> word strings </term> , where each <term> word string </term> has been obtained by using a different <term> LM </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 | We apply our <term> system </term> to the task of <term> scoring </term> alternative <term> speech recognition hypotheses ( SRH ) </term> in terms of their <term> semantic coherence </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 | Previous research has demonstrated the utility of <term> clustering </term> in inducing <term> semantic verb classes </term> from undisambiguated <term> corpus data </term> . | #3894 Previous research has demonstrated the utility of clustering in inducingsemantic verb classes from undisambiguated corpus data. |
| other,6-2-P03-1068,bq | The backbone of the <term> annotation </term> are <term> semantic roles </term> in the <term> frame semantics paradigm </term> . | #4969 The backbone of the annotation aresemantic roles in the frame semantics paradigm. |
| tech,13-4-P03-1068,bq | On this basis , we discuss the problems of <term> vagueness </term> and <term> ambiguity </term> in <term> semantic annotation </term> . | #5004 On this basis, we discuss the problems of vagueness and ambiguity insemantic annotation. |
| Motivated by this semantic criterion we analyze the empirical quality of <term> distributional word feature vectors </term> and its impact on <term> word similarity </term> results , proposing an objective <term> measure </term> for evaluating <term> feature vector </term> 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 | This system identifies <term> features </term> of <term> sentences </term> based on <term> semantic similarity measures </term> and <term> discourse structure </term> . | #6698 This system identifies features of sentences based onsemantic similarity measures and discourse structure. |
| tech,16-1-I05-5003,bq | The task of <term> machine translation ( MT ) evaluation </term> is closely related to the task of <term> sentence-level semantic equivalence classification </term> . | #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 | This paper investigates the utility of applying standard <term> MT evaluation methods ( BLEU , NIST , WER and PER ) </term> to building <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 | We present an efficient algorithm for the <term> redundancy elimination problem </term> : Given an <term> underspecified semantic representation ( USR ) </term> of a <term> scope ambiguity </term> , compute an <term> USR </term> with fewer mutually <term> equivalent readings </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 | Two styles of 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 | <term> Node-based inference rules </term> can be constructed in a <term> semantic network </term> using a variant of a <term> predicate calculus notation </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 | These <term> syntactic and semantic expectations </term> can be used to figure out <term> unknown words </term> from <term> context </term> , constrain the possible <term> word-senses </term> of <term> words with multiple meanings </term> ( <term> ambiguity </term> ) , fill in <term> missing words </term> ( <term> elllpsis </term> ) , and resolve <term> referents </term> ( <term> anaphora </term> ) . | #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 | Like <term> semantic grammar </term> , this allows easy exploitation of <term> limited domain semantics </term> . | #13351 Likesemantic grammar, this allows easy exploitation of limited domain semantics. |
| other,25-3-C88-1007,bq | The principle advantage of this approach is that knowledge concerning translation equivalence of expressions may be directly exploited , 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 | The unique properties of <term> tree-adjoining grammars ( TAG ) </term> present a challenge for the application of <term> TAGs </term> beyond the limited confines of <term> syntax </term> , for instance , to the task of <term> semantic interpretation </term> or <term> automatic translation of natural language </term> . | #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 | Manual acquisition of <term> semantic constraints </term> in broad domains is very expensive . | #16607 Manual acquisition ofsemantic constraints in broad domains is very expensive. |
| other,8-3-C90-3063,bq | To a large extent , these <term> statistics </term> reflect <term> semantic constraints </term> and thus are used to disambiguate <term> anaphora references </term> and <term> syntactic ambiguities </term> . | #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 | The results of the experiment show that in most of the cases the <term> cooccurrence statistics </term> indeed reflect the <term> semantic constraints </term> and thus provide a basis for a useful <term> disambiguation tool </term> . | #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. |
