Concordance

Query semantic 27 >
Filter all but first hit in document 20 (932.4 per million)

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.
	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,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,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,11-6-A92-1027,bq	search space </term> is achieved by using <term>	semantic	</term> rather than <term> syntactic categories	#17714 A further reduction in the search space is achieved by usingsemantic rather than syntactic categories on the terminal and non-terminal edges, thereby reducing the amount of ambiguity and thus the number of edges, since only edges with a valid semantic interpretation are ever introduced.
other,2-2-H92-1026,bq	</term> incorporates <term> lexical , syntactic ,	semantic	, and structural information </term> from	#18925 HBG incorporates lexical, syntactic, semantic, and structural information from the parse tree into the disambiguation process in a novel way.
other,39-3-H92-1060,bq	gluing them together to obtain a single <term>	semantic	frame </term> encoding the full <term> meaning	#19425 Robust parsing is applied only after a full analysis has failed, and it involves the two stages of 1) parsing a set of phrases and clauses, and 2) gluing them together to obtain a singlesemantic frame encoding the full meaning of the sentence.
other,17-6-A94-1026,bq	component places some restrictions on the <term>	semantic	categories </term> of the <term> adjoining	#20477 The basic idea of this method is that a compound noun component places some restrictions on thesemantic categories of the adjoining words.
other,6-3-C94-1052,bq	experiments , corresponding to rather <term> closed	semantic	domains </term> , have been developed in	#20796 Several experiments, corresponding to rather closed semantic domains, have been developed in order to generate lexical cross-relations between English and Spanish.


	in Help