Natural language processing technology has developed remarkably, but it is still difficult for computers to understand contextual meanings as humans do. The purpose of our work has been to construct an associative concept dictionary for Japanese verbs and make computers understand contextual meanings with a high degree of accuracy. We constructed an automatic system that can be used to estimate elliptical words. We present the result of comparing words that were estimated both by our proposed system (VNACD) and three baseline systems (VACD, NACD, and CF). We then calculated the mean reciprocal rank (MRR), top N accuracy (top 1, top 5, and top 10), and the mean average precision (MAP). Finally, we showed the effectiveness of our method for which both an associative concept dictionary for verbs (Verb-ACD) and one for nouns (Noun-ACD) were used. From the results, we conclude that both the Verb-ACD and the Noun-ACD play a key role in estimating elliptical words.

We propose a Word Sense Disambiguation (WSD) method that accurately classifies ambiguous words to concepts in the Associative Concept Dictionary (ACD) even when the test corpus and the training corpus for WSD are acquired from different domains. Many WSD studies determine the context of the target ambiguous word by analyzing sentences containing the target word. However, they offer poor performance when they are applied to a corpus that differs from the training corpus. One solution is to use associated words that are domain-independently assigned to the concept in ACD; i.e. many users commonly imagine those words against a given concept. Furthermore, by using the associated words of a concept as search queries for a training corpus, our method extracts relevant words, those that are computationally judged to be related to that concept. By checking the frequency of associated words and relevant words that appear near to the target word in a sentence in the test corpus, our method classifies the target word to the concept in ACD. Our evaluation using two different types of corpus demonstrates its good accuracy.

Conventional methods for disambiguation problems have been using statistical methods with co-occurrence of words in their contexts. It seems that human-beings assign appropriate word senses to the given ambiguous word in the sentence depending on the words which followed the ambiguous word when they could not disambiguate by using the previous contextual information. In this research, Contextual Dynamic Network Model is developed using the Associative Concept Dictionary which includes semantic relations among concepts/words and the relations can be represented with quantitative distances among them. In this model, an interactive activation method is used to identify a word’s meaning on the Contextual Semantic Network where the activation values on the network are calculated using the distances. The proposed method constructs dynamically the Contextual Semantic Network according to the input words sequentially that appear in the sentence including an ambiguous word. Therefore, in this research, after the model calculates the activation values, if there is little difference between the activation values, it reconstructs the network depending on the next words in input sentence. The evaluation of proposed method showed that the accuracy rates are high when Contextual Semantic Network has high density whose node are extended using around the ambiguous word.

In this study, we extracted brain activities related to semantic relations and distances to improve the precision of distance calculation among concepts in the Associated Concept Dictionary (ACD). For the experiments, we used a multi-channel Near-infrared Spectroscopy (NIRS) device to measure the response properties of the changes in hemoglobin concentration during word-concept association tasks. The experiments’ stimuli were selected from pairs of stimulus words and associated words in the ACD and presented in the form of a visual stimulation to the subjects. In our experiments, we obtained subject response data and brain activation data in Broca's area ―a human brain region that is active in linguistic/word-concept decision tasks― and these data imply relations with the length of associative distance. This study showed that it was possible to connect brain activities to the semantic relation among concepts, and that it would improve the method for concept distance calculation in order to build a more human-like ontology model.

Many of the Japanese ideographs (Chinese characters) have a few meanings. Such ambiguities should be identified by using their contextual information. For example, we have an ideograph which has two pronunciations, /hitai/ and /gaku/, the former means a forehead of the human body and the latter has two meanings, an amount of money and a picture frame. Conventional methods for such a disambiguation problem have been using statistical methods with co-occurrence of words in their context. In this research, Contextual Dynamic Network Model is developed using the Associative Concept Dictionary which includes semantic relations among concepts/words and the relations can be represented with quantitative distances. In this model, an interactive activation method is used to identify a word’s meaning on the Contextual Semantic Network where the activation on the network is calculated using the distances. The proposed method constructs dynamically the Contextual Semantic Network according to the input words sequentially that appear in the sentence including an ambiguous word.