| tech,25-2-H01-1041,ak | 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,ak | 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,ak | hypotheses ( SRH ) </term> in terms of their <term> | semantic | coherence </term> . We conducted an <term> | #2477 We apply our system to the task of scoring alternative speech recognition hypotheses (SRH) in terms of theirsemantic coherence. | |
| other,10-1-P03-1009,ak | <term> clustering </term> in inducing <term> | semantic | verb classes </term> from undisambiguated | #3895 Previous research has demonstrated the utility of clustering in inducingsemantic verb classes from undisambiguated corpus data. | |
| other,6-2-P03-1068,ak | backbone of the <term> annotation </term> are <term> | semantic | roles </term> in the <term> frame semantics | #4971 The backbone of the annotation aresemantic roles in the frame semantics paradigm. | |
| tech,13-4-P03-1068,ak | </term> and <term> ambiguity </term> in <term> | semantic | annotation </term> . We investigate the <term> | #5006 On this basis, we discuss the problems of vagueness and ambiguity insemantic annotation. | |
| model,15-2-I05-4007,ak | wordnets </term> , we must rely on <term> lexical | semantic | relation ( LSR ) mappings </term> to ensure | #7089 Since there is no homomorphism between pairs of monolingual wordnets, we must rely on lexical semantic relation (LSR) mappings to ensure conceptual cohesion. | |
| model,23-3-I05-4007,ak | </term> and a set of <term> cross-lingual lexical | semantic | relations </term> . In particular , we propose | #7125 In this paper, we propose and implement a model for bootstrapping parallel wordnets based on one monolingual wordnet and a set of cross-lingual lexical semantic relations. | |
| tech,16-1-I05-5003,ak | related to the task of <term> sentence-level | semantic | equivalence classification </term> . This | #7383 The task of machine translation (MT) evaluation is closely related to the task of sentence-level semantic equivalence classification. | |
| other,25-2-I05-5003,ak | <term> classifiers </term> to predict <term> | semantic | equivalence </term> and <term> entailment </term> | #7412 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,1-1-P05-1053,ak | MT architectures </term> . Extracting <term> | semantic | relationships </term> between <term> entities | #9266 Extractingsemantic relationships between entities is challenging. | |
| other,7-2-P05-1053,ak | of diverse <term> lexical , syntactic and | semantic | knowledge </term> in <term> feature-based relation | #9284 This paper investigates the incorporation of diverse lexical, syntactic and semantic knowledge in feature-based relation extraction using SVM. | |
| other,4-5-P05-1053,ak | chunking </term> . We also demonstrate how <term> | semantic | information </term> such as <term> WordNet </term> | #9357 We also demonstrate howsemantic information such as WordNet and Name List, can be used in feature-based relation extraction to further improve the performance. | |
| tech,6-1-P05-1073,ak | Despite much recent progress on accurate <term> | semantic | role labeling </term> , previous work has | #10040 Despite much recent progress on accuratesemantic role labeling, previous work has largely used independent classifiers, possibly combined with separate label sequence models via Viterbi decoding. | |
| model,13-1-P06-1052,ak | problem </term> : Given an <term> underspecified | semantic | representation ( USR ) </term> of a <term> | #12069 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. | |
| model,6-1-T78-1031,ak | performing <term> inference </term> in <term> | semantic | networks </term> are presented and compared | #12993 Two styles of performing inference insemantic networks are presented and compared. | |
| model,8-5-T78-1031,ak | rules </term> can be constructed in a <term> | semantic | network </term> using a variant of a <term> | #13074 Node-based inference rules can be constructed in asemantic network using a variant of a predicate calculus notation. | |
| model,6-1-P81-1032,ak | interpretation </term> requires strong <term> | semantic | domain models </term> , <term> fail-soft recovery | #13732 Robust natural language interpretation requires strongsemantic domain models, fail-soft recovery heuristics, and very flexible control structures. | |
| other,1-4-P82-1035,ak | being described . These <term> syntactic and | semantic | expectations </term> can be used to figure | #14347 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 (ellipsis), and resolve referents (anaphora). | |
| tech,23-2-E83-1029,ak | Procedural Systemic Grammar </term> , the <term> | semantic | analyzer </term> relying on the <term> Conceptual | #14602 Specifically, the following components of the system are described: the syntactic analyzer, based on a Procedural Systemic Grammar, thesemantic analyzer relying on the Conceptual Dependency Theory, and the dictionary. |
