International Journal of Computer Processing of Languages

When a dependency parser analyzes long sentences with fewer subjects than predicates, it is difficult to recognize which predicate governs which subject. To handle such syntactic ambiguity between subjects and predicates, we define a "S(ubject)-clause" as a group of words containing several predicates and their common subject. This paper proposes a method to segment S-clauses and perform syntactic analysis of long sentences using S-clause segmentation. To segment S-clauses, various machine learning methods were applied. We found that the Logitboost method produced the best performance.

In our experimental evaluation, S-clause information turned out to be effective in determining the governor of a subject and that of a predicate in dependency parsing. Further syntactic analysis using S-clauses achieved an improvement in precision by 5.25 percent.

As of now, Text-to-Speech (TTS) systems are widely used throughout the full spectrum of our activities, and various natural language processing techniques have been utilized to enhance the performance of such TTS systems. As TTS systems begin to play an important role for communication between human and machine, naturalness is considered the most crucial measure of performance for TTS systems, in addition to correctness. General statistical approaches, though widely adopted, are not appropriate for the phenomena as they assign the same intonation to the same sentence. We analyze various kinds of corpus to extract informative features for intonation generation in a Combinatory Categorial Grammar framework, and express intonation-annotated document using Speech Synthesis Markup Language for target system neutral application.

Word-spacing errors, one of the most frequent errors in Korean, produce ambiguities in the lexical interpretation of parts of speech or render sentences including them incomprehensible. Resolving those errors is thus crucial in Korean language processing application domains. In this paper, we propose a stochastic Korean word-spacing system with smoothing using Korean Spelling Checker, which is equally robust for both inner data and external data. In order to cope with various problems of word-spacing, this study (a) presents a simple stochastic word-spacing system with only two parameters using the odds favoring the inner-spacing of a given syllable bigram as well as relative word ftequencies, and (b) endeavors to (i) remove noise from the training data and (ii) diminish training data-dependency by dynamically creating a candidate word with a longest-radix-selecting algorithm. The system thus becomes robust against unseen words and offers a similar performance for both inner data and external data: it obtained a 98.47% and a 97.78% precision in word-unit correction for the inner test data and the balanced external test data, respectively.

Recent MT paradigms, such as example-based MT, pattern-based MT, and hybrid MT contribute to overcoming the shortcoming of rule-based MT by improving the quality of translations. However, a problem with erroneous selection of examples or patterns also exists in these types of MTs. In this paper, we discover a matching abnormality, which we term an Over-Specification Problem that occurs when an input sentence corresponding to the pattern of general sense is actually matched to the patterns of exceptional sense, and thus is incorrectly translated. We formally define the problem and propose a solution based on the similarity of dependency trees and lexical association ratio to detect whether the input tree is dragged into the exceptional range of example trees or not. Finally, promising experimental results are provided. We take a hybrid MT as a testing MT and Chinese-to-Korean as a sample language pair.

This paper proposes a framework for a man-computer cooperative information retrieval system, supplying users of online discussion systems (or group decision support systems) with immediate information from the Internet. This system can sense the discussion topic and sub-topics actively, and sends the sub-topics as search terms to a WWW search engine. A filter and feedback module checks the quality of searching results and interacts with the search engine if needed. The filtered items from the filter and feedback module are sent to online discussion users. At last an information recommendation module records the visited history of all items, and then works out the most favorite ones to recommend them to each expert selectively. Some primary experimental results show that this framework is feasible.

A handwritten Chinese address recognition (HCAR) system is proposed in this paper. Handwritten Chinese addresses are difficult to recognize because handwritten Chinese characters are characterized by a large vocabulary, complicated structure, irregular distortion and touching characters, etc. The proposed approach takes good advantage of Chinese address knowledge, and applies key character extraction and holistic word matching in solving the problem. Different from conventional approaches, the proposed approach can successfully avoid character segmentation errors. Experimental results show the proposed approach is very effective.