International Symposium on Chinese Spoken Language Processing (ISCSLP 2002)

Taipei, Taiwan
August 23-24, 2002

Challenges and Advances in Semantic Representation and Interpretation

Jhing-Fa Wang

Department of Electrical Engineering, National Cheng Kung University

One of the greatest challenges in developing natural language understanding lies in semantic representation and interpretation (SRI). Semantic representation concerns how elements of sentences represent semantic constituents and what kind of relationships semantic constituents are. Semantic interpretation concerns how to interpret these constituents and relationships semantically. The field of semantic representation and interpretation has witnessed a number of significant advances in the past years. The traditional methods include first-order predicate calculus (FOPC), case frames and grammars, and logical form (LF). Recently, unified natural language (UNL), flat and deep ABox, ontologies, and latent semantic acquisition (LSA) have been developed for the shared formal conceptualizations of particular domains. These methods, as specifications of the concepts in a given field, and of the relationships among those concepts, provide insight into the nature of information produced by that field and are an essential ingredient for any attempts to arrive at a shared understanding of concepts in a field.

However, some design issues must be considered to achieve a good SRI. For this reason, this lecture introduces four evaluation criteria scope portability, accuracy, facility, and efficiency (SAFE). After comparing previous SRI techniques under SAFE, we find several methods are deficient in scope portability and efficiency, while others are lack of facility or accuracy. And these drawbacks confuse new researchers and various computer systems when dealing with natural language understanding. For these drawbacks, we have proposed a devised Acting Role Table (ART) and an efficient automatic ART construction algorithm for language understanding. The semantics of each sentence is represented in the devised Acting Role Table. The ART consists of acting roles of the sentences, i.e., action, agent, instrument, theme, location, and time, together with their associated modifiers. In this step, we use verb-driven syntax analysis to determine the acting roles in the sentences; and use the semantic analysis to constrain the acting roles based on the features defined in well-known knowledge database e.g. WordNet and HowNet. In addition, we applied ART in question answering for primaryschool textbook, spoken dialogue system for mobile information retrieval, template expansion for example-based machine translation and language translation for travel planning and illustrate a good direction for language understanding.


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  4. Jhing-Fa Wang and Hsien-Chang Wang, J-Y Huwang(2000) Domain-unconstrained language understanding Based on CKIP-AutoTag, How-net, and ART. 6th International Conference of Spoken Language Processing.
  5. Jhing-Fa Wang and Hsien-Chang Wang and Chin-Nan Lee (2000) Domain Unconstrained Language Understanding Based on How-net. PACLIC 14, Japan.
  6. Jhing-Fa Wang and Shun-Chieh Lin (2002) Bilingual Corpus Evaluation and Discriminative Sentence Vector Expansion for Machine Translation. ICAIET 2002, Malaysia.

Bibliographic reference.  Wang, Jhing-Fa (2002): "Challenges and advances in semantic representation and interpretation", In ISCSLP 2002, paper INV4 (abstract).