Knowledge Representation for Interrogatives in E-HowNet

Knowledge Representation for Interrogatives in E-HowNet Shu-Ling Huang, You-Shan Chung, Yueh-Yin Shih, Keh-Jiann Chen CKIP, Institute of Information Science, Academia Sinica {josieh, yschung, yuehyin, kchen}@iis.sinica.edu.tw Abstract In order to train machines to ‘understand’ natural language, we proposed a universal concept representational mechanism called E-HowNet to encode lexical semantics. In this paper, we take interrogative constructions as examples, i.e. concepts or sentences for asking questions or making inquiries, to demonstrate the mechanisms of semantic representation and composition under the framework of E-HowNet. We classify the interrogative words into five types according to their semantic distinctions, and represent each type with fine-grained features and operators. The process of semantic composition and the difficulties of the representation, such as word sense disambiguation, will be addressed. Finally, we’ll show how machine discriminates two synonymous sentences with different syntactic structures and surface strings to prove that machine understanding is achievable. Keywords: semantic representation, interrogatives, E-HowNet 1. Introduction To understand natural language by machines, lexical semantic representation and composition are the most important techniques. In this paper, we will take the interrogatives as examples to demonstrate the mechanism of lexical semantic representation and composition in E-HowNet (Chen et.al., 2004). E-HowNet uses the word sense definition mechanism of HowNet (Dong, 1988) and the vocabulary of WordNet (Fellbaum,1998) synsets to describe concepts.1 Its goal is to achieve near canonical semantic representation, 1 The advantage of using WordNet synsets is that each synset has unique sense and sense similarity between two synsets can be measured through WordNet ontology. But there is disadvantage of WordNet-like ontologies, for example, each concept class in WordNet has limited linking to other concepts. The major links are hyponymy relations which limit inheritance and inference capability to the classes on the taxonomy. So we adopt similar that is, two sentences with different surface forms or in different languages may achieve similar E-HowNet representations. Take sentences (1) and (2) as examples: (1) 我能否拍照？Is it OK for me to take pictures? (2) 我可不可以照相？Can I take photos? Although the syntactic structure and surface strings of (1), (2) are very different, by using lexical sense definitions in E-HowNet, we hope machine can ‘understand’ that they are synonymous sentences. Analysis of interrogative constructions is of great interest to linguists, as well as to computer scientists, for example, those who engaged in QA techniques. Interrogative constructions have played a central role in the development of modern syntactic theory. Ginzburg & A.Sag (2000) have pointed that interrogative has been at the heart of work in generative grammar, also in government and binding (GB) and head-driven phrase structure (HPSG). Nonetheless, to date most syntactic work has taken place quite separately from semantic and pragmatic work on interrogatives. Taking questions in Mandarin Chinese as example, Shao (1996) has summed up the current study of interrogatives and listed the main research themes as follows: the types of question, interrogative particles, querying focus and its answer, degree of doubt and special interrogative sentences pattern etc.. Most of the above themes are purely grammatical analysis. To build …

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