Transformation-Based Learning (TBL) is a relatively new machine learning method that has achieved notable success on language problems. This paper presents a variant of TBL, called Randomized TBL, that overcomes the training time problems of standard TBL without sacrificing accuracy. It includes a set of experiments on part-of-speech tagging in which the size of the corpus and template set are varied. The results show that Randomized TBL can address problems that are intractable in terms of training time for standard TBL. In addition, for language problems such as dialogue act tagging where the most effective features have not been identified through linguistic studies, Randomized TBL allows the researcher to experiment with a large set of templates capturing many potentially useful features and feature interactions.

ANVIL is an information retrieval system using natural language processing techniques, intended for retrieval of captioned images. It extracts dependency structures from the image captions and user queries, and then applies a high accuracy matching algorithm which recursively explores the dependency structures to determine their similarity. A further algorithm allows additional contextual information to be extracted following a successful match, with the intention of helping users understand and organise the retrieval results. ANVIL was developed to high engineering standards, and as well as looking at the research aspects of the system, we also look at some of the design and development issues. English and Japanese versions of the system have been developed.

Automatic Accent Insertion (AAI) is the problem of re-inserting accents (diacritics) into a text where they are missing. Unaccented French texts are still quite common in electronic media, as a result of a long history of character encoding problems and the lack of well-established conventions for typing accented characters on computer keyboards. An AAI method for French is presented, based on a statistical language model. Next, it is shown how this AAI method can be used to do real-time accent insertions within a word processing environment, making it possible to type in French without having to type accents. Various mechanisms are proposed to improve the performance of real-time AAI, by exploiting online corrections made by the user. Experiments show that, on average, such a system produces less than one accentuation error for every 200 words typed.

Compound noun segmentation is one of the crucial problems in Korean language processing because a series of nouns in Korean may appear without space in real text, which makes it difficult to identify its morphological constituents. This paper presents an effective method of Korean compound noun segmentation based on lexical data extracted from a corpus. The segmentation consists of two tasks: First, it uses a Hand-Build Segmentation Dictionary (HBSD) to segment compound nouns which frequently occur or need an exceptional process. Second, a segmentation algorithm using data from a corpus is proposed, where simple nouns and their frequencies are stored in a Simple Noun Dictionary (SND) for segmentation. The analysis is executed based on modified tabular parsing using min-max operation. Our experiments have shown a very effective accuracy rate of about 97.29%, which turns out to be very effective.

The automated analysis of natural language data has become a central issue in the design of Intelligent Information Systems. The term natural language is intended to cover all the possible modalities of human communication and it is not restricted to written or spoken language. Processing unrestricted natural language is still considered as an AI-hard task. However various analysis techniques have been proposed in order to address specific aspects of natural language. In particular, recent interest has been on providing approximate analysis techniques, assuming that perfect analysis is not possible, but that partial results are still very useful.