Several approaches have been studied to cope with the exceptional features of non-segmented languages. When there is no explicit information about the boundary of a word, segmenting an input text is a formidable task in language processing. Not only the contemporary word list, but also usages of the words have to be maintained to cover the use in the current texts. The accuracy and efficiency in higher processing do heavily rely on this word boundary identification task. In this paper, we introduce some statistical based approaches to tackle the problem due to the ambiguity in word segmentation. The word boundary identification problem is then defined as a part of others for performing the unified language processing in total. To exhibit the ability in conducting the unified language processing, we selectively study the tasks of language identification, word extraction, and dictionary-less search engine.

In this paper, we present a new algorithm for statistical static timing analysis of a CMOS combinatorial circuit, which takes correlations into account to improve accuracy of the distribution of the maximum delay of the circuit. The correlations treated in the algorithm are not only the one between distributions of arrival times of input signals to a logic gate but also correlation between switching delays of a logic gate and correlation between interconnect delays of a net. We model each delay by a normal distribution, and use a normal distribution of two stochastic variables with a coefficient of correlation for computing the maximum of two delays. Since the algorithm takes the correlation into account, the time complexity is O(m2) in the worst-case, where m is the number of edges of the graph representing a given circuit. But, for real combinatorial circuits, the complexity is expected to be less than this.

Handprinted Chinese character recognition (HCCR) can be classified into two major approaches: statistical and structural. While neither of these two approaches can lead to a total and practical solution for HCCR, integrating them to take advantages of both seems to be a promising and obviously feasible approach. But, how to integrate them would be a big issue. In this paper, we propose an integrated HCCR system. The system starts from a statistical phase. This phase uses line-density-distribution-based features extracted after nonlinear normalization to guarantee that different writing variations of the same character have similar feature vectors. It removes accurately and efficiently the impossible candidates and results in a final candidate set. Then follows the structural phase, which inherits the line segments used in the statistical phase and extracts a set of stroke substructures as features. These features are used to discriminate the similar characters in the final candidate set and hence improve the recognition rate. Tested by using a large set of characters in a handprinted Chinese character database, the proposed HCCR system is robust and can achieve 96 percent accuracy for characters in the first 100 variations of the database.

Automatic alignment of bilingual texts is useful to example-based machine translation by facilitating the creation of example pairs of translation for the machine. Two main approaches to automatic alignment have been reported in the literature. They are lexical approach and statistical approach. The former looks for relationships between lexical contents of the bilingual texts in order to find alignment pairs, while the latter uses statistical correlation between sentence lengths of the bilingual texts as the basis of matching. This paper describes a combination of the two approaches in aligning Japanese-Cinese bilingual texts by allowing kanji contents and sentence lengths in the texts to work together in achieving an alignment process. Because of the sentential structure differences between Japanese and Chinese, matching at the sentence level may result in frequent matching between a number of sentences en masses. In view of this, the current work also attempts to create shorter alignment pairs by permitting sentences to be matched with clauses or phrases of the other text if possible. While such matching is more difficult and error-prone, the reliance on kanji contents has proven to be very useful in minimizing the errors. The current research has thus found solutions to problems that are unique to the present work.