International Journal of Computer Processing of Languages

The Thai vowel system comprises nine short-long vowel pairs. The nine spreading vowels occur in specific locations covering the whole articulatory area. The location of each vowel can be identified according to its acoustic characteristics. Relationships between acoustic and articulatory characteristics were found in the Thai vowel system where F1 relates to vowel height and F2 relates to vowel advancement like other vowel systems. The bark F1-F0 and F3-F2 were also found to represent vowel height and vowel advancement, respectively. Vowel height includes high, mid and low vowel groups while vowel advancement includes front, central and back vowel groups. The previous binary classification criterion is not sufficient for a spreading vowel system. Hence, three new classification strategies are proposed for the classification of complex vowel systems: (1) classification by vowel height, (2) classification by vowel advancement, and (3) classification by combined vowel height and vowel advancement. Acoustic analysis on the Thai vowel system is conducted and each vowel is acoustically modelled by its acoustic-articulatory features. The F1 and bark F1-F0 are employed in classification by vowel height. The F2 and bark F3-F2 are employed in classification by vowel advancement. Both acoustic features are integrated into a single feature vector in combined vowel height and vowel advancement classification using the Bayesian classifier. From the results, linear frequency scale provides better classification accuracy than bark in every case. The linear F1 is 88.52% correct in vowel height classification while it is 94.09% correct using linear F2 in vowel advancement classification. Both linear F1 and F2 achieve 86.33% correctness in combined classification of all the vowels. These illustrate that three classification strategies efficiently classify complex vowel systems using only static spectral cues. Therefore, the simple linear F1 and F2 values can be used to model vowel articulation and to classify each vowel in languages with a complex vowel system like Thai.

We propose a format of a binary phrase structure grammar with composite labels. The grammar adopts binary rules so that the dependency between two sub-trees can be represented in the label of the tree. The label of a tree is composed of two attributes, each of which is extracted from each sub-tree, so that it can represent the compositional information of the tree. The composite label is generated from part-of-speech tags using an automatic labeling algorithm. Since the proposed rule description scheme is binary and uses only part-of-speech information, it can readily be used in dependency grammar and be applied to other languages as well.

In the best-1 context-free cross validation on 31,080 tree-tagged corpus, the labeled precision is 79.30%, which outperforms phrase structure grammar and dependency grammar by 5% and by 4%, respectively. It shows that the proposed rule description scheme is effective for parsing Korean.

Translation selection is a process to select, from a set of target language words corresponding to a source language word, the most appropriate one that conveys the correct sense of a source word and makes the target language sentence more natural. In this paper, we propose a hybrid method for translation selection that exploits a bilingual dictionary and a target language corpus. Based on the 'word-to-sense and sense-to-word' relationship between a source word and its translations, our method selects translation through two levels: sense disambiguation of a source word and selection of a target word. For translation selection, we introduce three measures: sense preference and sense probability for sense disambiguation, and word probability for word selection. The first one is based on knowledge from a bilingual dictionary, and the others are calculated using statistics from a target language corpus. We evaluated our method and results showed that our method selects more appropriate target words with knowledge extracted from easily obtainable resources.