Conceptual metaphor and scripts in Recognizing Textual Entailment

被引:0
作者
Murray, William R. [1 ]
机构
[1] Boeing Phamton Works, Seattle, WA 98124 USA
来源
NATURAL LANGUAGE PROCESSING AND COGNITIVE SCIENCE, PROCEEDINGS | 2008年
关键词
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The power and pervasiveness of conceptual metaphor can be harnessed to expand the class of textual entailments that can be performed in the Recognizing Textual Entailment (RTE) task and thus improve our ability to understand human language and make the kind of textual inferences that people do. RTE is a key component for question understanding and discourse understanding. Although extensive lexicons, such as WordNet, can capture some word senses of conventionalized metaphors, a more general capability is needed to handle the considerable richness of lexical meaning based on metaphoric extensions that is found in common news articles, where writers routinely employ and extend conventional metaphors. We propose adding to RTE systems an ability to recognize a library of common conceptual metaphors, along with scripts. The role of the scripts is to allow entailments from the source to the target domain in the metaphor by describing activities in the source domain that map onto elements of the target domain. An example is the progress of an activity, such as a career or relationship, as measured by the successful or unsuccessful activities in a journey towards its destination. In particular we look at two conceptual metaphors: IDEAS AS PHYSICAL OBJECTS, which is part of the Conduit Metaphor of Communication, and ABSTRACT ACTIVITIES AS JOURNEYS. The first allows inferences that apply to physical objects to (partially) apply to ideas and communication acts (e.g., "he lobbed jibes to the comedian"). The second allows the progress of an abstract activity to be assessed by comparing it to a journey (e.g., "his career was derailed"). We provide a proof of concept where axioms for actions on physical objects, and axioms for how physical objects behave compared to communication objects, are combined to make correct RTE inferences in Prover9 for example text-hypothesis pairs. Similarly, axioms describing different states in a journey are used to infer the current progress of an activity, such as whether it is succeeding (e.g., "steaming ahead"), in trouble (e.g., "off course"), recovering (e.g., "back on track"), or irrevocably failed (e.g., "hijacked").
引用
收藏
页码:127 / 136
页数:10
相关论文
共 50 条
[31]   Using Recognizing Textual Entailment as a Core Engine for Answer Validation [J].
Wang, Rui ;
Neumann, Guenter .
ADVANCES IN MULTILINGUAL AND MULTIMODAL INFORMATION RETRIEVAL, 2008, 5152 :387-+
[32]   Feature-Rich Classifiers for Recognizing Textual Entailment in Indonesian [J].
Hidayat, Rani Aulia ;
Khasanah, Isnaini Nurul ;
Putri, Wava Carissa ;
Mahendra, Rahmad .
AI IN COMPUTATIONAL LINGUISTICS, 2021, 189 :148-155
[33]   Refining the Judgment Threshold to Improve Recognizing Textual Entailment Using Similarity [J].
Quang-Thuy Ha ;
Thi-Oanh Ha ;
Thi-Dung Nguyen ;
Thuy-Linh Nguyen Thi .
COMPUTATIONAL COLLECTIVE INTELLIGENCE - TECHNOLOGIES AND APPLICATIONS, PT II, 2012, 7654 :335-344
[34]   Combining Lexical Resources with Fuzzy Set Theory for Recognizing Textual Entailment [J].
Feng, Jin ;
Zhou, Yiming ;
Martin, Trevor .
ISBIM: 2008 INTERNATIONAL SEMINAR ON BUSINESS AND INFORMATION MANAGEMENT, VOL 2, 2009, :54-+
[35]   Combining lexical resources with tree edit distance for recognizing textual entailment [J].
Kouylekov, Milen ;
Magnini, Bernardo .
MACHINE LEARNING CHALLENGES: EVALUATING PREDICTIVE UNCERTAINTY VISUAL OBJECT CLASSIFICATION AND RECOGNIZING TEXTUAL ENTAILMENT, 2006, 3944 :217-230
[36]   Using Sentence Semantic Similarity Based on WordNet in Recognizing Textual Entailment [J].
Castillo, Julio J. ;
Cardenas, Marina E. .
ADVANCES IN ARTIFICIAL INTELLIGENCE - IBERAMIA 2010, 2010, 6433 :366-375
[37]   An approach to Recognizing Textual Entailment and TE Search Task using SVM [J].
Javier Castillo, Julio .
PROCESAMIENTO DEL LENGUAJE NATURAL, 2010, (44) :139-145
[38]   A Database of Relations between Predicate Argument Structures for Recognizing Textual Entailment and Contradiction [J].
Matsuyoshi, Suguru ;
Murakami, Koji ;
Matsumoto, Yuji ;
Inui, Kentaro .
PROCEEDINGS OF THE SECOND INTERNATIONAL SYMPOSIUM ON UNIVERSAL COMMUNICATION, 2008, :366-373
[39]   INESC-ID@ASSIN: Measuring Semantic Similarity and Recognizing Textual Entailment [J].
Fialho, Pedro ;
Marques, Ricardo ;
Martins, Bruno ;
Coheur, Luisa ;
Quaresma, Paulo .
LINGUAMATICA, 2016, 8 (02) :33-42
[40]   CoRTE: A Corpus of Recognizing Textual Entailment Data Annotated for Coreference and Bridging Relations [J].
Waseem, Afifah .
TEXT, SPEECH, AND DIALOGUE (TSD 2018), 2018, 11107 :115-125