Exploiting Lexical Conceptual Structure for paraphrase generation

Abstract. Lexical Conceptual Structure (LCS) represents verbs as semantic structures with a limited number of semantic predicates. This paper attempts to exploit how LCS can be used to explain the regularities underlying lexical and syntactic paraphrases, such as verb alternation, compound word decomposition, and lexical derivation. We propose a paraphrase generation model which transforms LCSs of verbs, and then conduct an empirical experiment taking the paraphrasing of Japanese light-verb constructions as an example. Experimental results justify that syntactic and semantic properties of verbs encoded in LCS are useful to semantically constrain the syntactic transformation in paraphrase generation.

The influence of media violence on youth

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83429/1/2003.Anderson_etal.InfluenceofMediaViolenceonYouth.PsychologicalScienceinthePublicInterest.pd

Handling Translation Divergences: Combining Statistical and Symbolic Techniques in Generation-Heavy Machine Translation

This paper describes a novel approach to handling translation divergences in a Generation-Heavy Hybrid Machine Translation (GHMT) system. The translation divergence problem is usually reserved for Transfer and Interlingual MT because it requires a large combination of complex lexical and structural mappings. A major requirement of these approaches is the accessibility of large amounts of explicit symmetric knowledge for both source and target languages. This limitation renders Transfer and Interlingual approaches ineffective in the face of structurally-divergent language pairs with asymmetric resources. GHMT addresses the more common form of this problem, source-poor/targetrich, by fully exploiting symbolic and statistical target-language resources