A Study Of Chinese Textual Entailment

The main task of textual entailment is recognition of textual entailment which has a lot of application in natural language processing. However, there is still a great distance between the effects of existing study of recognition of textual entailment and scale applications. One of the most important reasons is that motivations behind textual entailments need further excavation and methods used in recognition of textual entailment need further improvement, which is the main work of this paper.The contents of existing study of textual entailment motivations mainly concentrated on word relations and syntax transformation. Word relations as textual entailment motivations are to be extended in this paper by utilizing lexical functions from Meaning-Text Theory. And especially, image schemata will be utilized as motivations behind textual entailments to express knowledge needed in recognition of textual entailments.As far as methods of recognition of textual entailment is concerned, existing syntax dependency analysis is not enough to uncover language knowledge needed in textual entailments, which is improved by introducing conceptual dependency analysis in this paper. Nevertheless, it is found that conceptual dependency analysis cannot undertake everything in excavating linguistic motivations behind textual entailment, e.g., correspondences between meta-language functions, abstract concepts and concrete expressions cannot be excavated with conceptual dependency analysis, which can be made up by lexical functions effectively.It is found that the reason for image schemata to become motivations behind textual entailment is that image schemata are idealized, conventionalized, and predictable. Conventionalization of common sense can improve corpus coverage in recognition of textual entailment. Both lexical functions and image schemata own the function of conventionalizing common sense and they are complementary in this function. Recognition of textual entailment, as the main procedure of textual inference and understanding, involves all kinds of cognitive mechanisms, such as conceptual integration, metonymy, and metaphor. All these problems can not be solved or explained by a single method or theory. For this reason, comprehensive methodology and multiple angles of explanation are applied in this paper. E.g., lexical functions are used to fill the gap left by conceptual dependency analysis; besides theory of conceptual integration, other theories, such as Default Theory, Relevance Theory, and Adaptation Theory are also used to explain problems and phenomena in the recognition of Chinese textual entailment.This paper has nine chapters with the following contents or viewpoints:Chapter1is introduction, explaining the concept of textual entailment, the reasons for choosing the topic, research status, and contents, purposes, significance of this study. Theoretical backgrounds, methods, and resources to be used in the study are also introduced.Chapter2classifies and defines textual entailments; and discusses the recognition ways of and motivations behind textual semantic entailment. Frame dependency analysis and lexical functions are the main measures utilized in the recognition of textual semantic entailment. The results of analysis shows that frame dependency analysis is effective in recognizing textual semantic entailment; conceptual dependency analysis and lexical functions are complementary in recognition of textual entailment; and metonymy is not necessarily based on Image schemata.Chapter3studies the recognition of textual semantic presupposition and motivations behind it. It is shown that image schemata play an important role in recognizing textual semantic presupposition, and the fundamental idea or operation of conceptual dependency analysis is schematic mapping from an image schema to a concrete sentence but not the analysis of interior semantic relations of a concrete sentence.Chapter4discusses the recognition of and motivations behind textual conventional conversational implicature; and the problem of conventionalization of common sense in recognition of textual entailment. Analysis shows that:(1) textual conventional conversational implicature can best embody the usefulness of image schemata and conceptual dependency analysis, especially the function of frame dependency analysis;(2) during the recognition processes of those textual entailments with image schemata as their motivations, compressions based on image schemata effectively expand the domain of compression of key relations in theory of conceptual integration;(3) conceptual dependency analysis is closely related to conceptual integration;(4) the recognition of the correspondent entailment between abstract, meta-language concepts and concrete, embodied expressions are complementary with recognition of textual entailment uncovered by conceptual dependency analysis, word relations, and syntax transformation. How to establish the links between meta-language concepts and embodied expressions is one of the important tasks for improving recognition of textual entailment. Chapter5discusses the recognition of and linguistic motivations behind textual resultative entailment. Analysis shows that scripts as motivations play an important role in the recognition of textual resultative entailment, and as syntax and special word relations can also express cause-effect relations, they can be motivations of textual resultative entailment.Based on the above study and carding of the corpus, Chapter6tentatively discusses construction of resources for recognition of textual entailment in natural language processing. While constructing these resources, characteristics of language as a united system should be considered and all the resources share the same purpose: serve recognition of textual entailment in natural language processing. And thus it is required that while constructing these resources, it should be tried to cover all the corpus, avoiding overlapping or conflicts among different resources to ensure discreteness, and if there are conflicts that cannot be avoided, mechanisms should be offered to settle the conflicts. As these resources are natural language processing oriented, all the image schema resources must be machine readable, which means that experts in computing are needed in resource construction.Chapter7offers examples of application of this study. Application of recognition of textual entailment has both simpler and more complex places than the study of recognition of textual entailment above. Most of the examples come from Chinese Proficiency Test, which embodies the application potential of the study above both in recognition of textual entailment in natural language processing and teaching Chinese as a second language.Chapter8explains some problems involved in this study. There are many problems appearing during the process of this study and only some of which the author thought to have certain depth of understanding are chosen to be discussed with certain detail and these problems discussed are:the difficulty and probabilistic of recognition of textual entailment, cancellability and projectivity of textual semantic entailment, and metaphor and metonymy involved in recognition of textual entailment.Chapter9is the conclusion, summarizing this study and pointing out the possible future work.