Similarity between Words Computed by Spreading Activation on an English Dictionary

This paper proposes a method for measuring semantic similarity between words as a new tool for text analysis. The similarity is measured on a semantic network constructed systematically from a subset of the English dictionary, LDOCE (Long- man Dictionary of Contemporary English). Spreading activation on the network can directly compute the similarity between any two words in the Longman Defining Vocabulary, and indirectly the similarity of all the other words in LDOCE. The similarity represents the strength of lexical cohesion or semantic relation, and also provides valuable information about similarity and coherence of texts. 1 Introduction A text is not just a sequence of words, but it also has coherent structure. The meaning of each word in a text depends on the structure of the text. Recognizing the structure of text is an essential task in text understanding.[Grosz and Sidner, 1986] One of the valuable indicators of the structure of text is lexical cohesion. [ Halliday and Has..

Word Word Sense Disambiguation with a Corpus Corpus-based Corpus ased Semantic Network

Determining the meaning of words in text is an important process in natural language processing. In this paper, we propose a new method for word sense disambiguation that uses a corpus-based semantic network. Creating a semantic network that represents semantic distances among words in general, we resolve the ambiguities activating the network. Theoretically, our method needs no annotation on the corpus from which a CSN is created and also makes the data sparseness problem irrelevant. Practically, it achieved a success rate of 92.1%, which is better than those of other comparable studies

Structural Disambiguation Based on Reliable Estimation of Strength of Association

This paper proposes a new class-based method to estimate the strength of association in word co-occurrence for the purpose of structural disambiguation. To deal with sparseness of data, we use a conceptual dictionary as the source for acquiring upper classes of the words related in the co-occurrence, and then use t-scores to determine a pair of classes to be employed for calculating the strength of association. We have applied our method to determining dependency relations in Japanese and prepositional phrase attachments in English. The experimental resuits show that the method is sound, effective and useful in resolving structural ambiguities