Abstract not found

Abstract not found

This paper describes a method for linear text seg- mc. ntation which is twice as accurate and over seven times as fast as the state-of-the-art (Reynar, 1998). Inter-sentence similarity is replaced by rank in the local context. Boundary locations are discovered by divisive clustering.

We propose a semi-automatic tool, termight, that helps professional translators and terminologists identify technical terms and their translations. The tool makes use of part-of-speech tagging and word-alignment programs to extract candidate terms and their translations. Although the extraction programs are far from perfect, it isn't too hard for the user to filter out the wheat from the chaff. The extraction algorithms emphasize completeness. Alter-native proposals are likely to miss important but infrequent terms/translations. To reduce the burden on the user during the filtering phase, candidates are presented in a convenient order, along with some useful concordance evidence, in an interface that is designed to minimize keystrokes. Termight is currently being used by the trans-

This article advances the state of the art ofbitext mapping by formulating the problem in terms of pattern recognition. From this point of view, the success of a bitext mapping algorithm hinges on how well it performs three tasks: signal generation, noise filtering, and search. The Smooth Injective Map Recognizer (SIMR) algorithm presented here integrates innovative approaches to each of these tasks. Objective evaluation has shown that SIMR's accuracy is consistently high for language pairs as diverse as French/English and Korean/English. If necessary, S IMR's bitext maps can be efficiently converted into segment alignments using the Geometric Segment Alignment (GSA) algorithm, which is also presented here. SIMR has produced bitext maps for over 200 megabytes of French-English bitexts. GSA has converted these maps into alignments. Both the maps and the alignments are available from the Linguistic Data Consortium) 1.

We have developed a new program called word_align for aligning parallel text, text such as the Canadian Hansards that are available in two or more languages. The program takes the output of char_align (Church, 1993), a robust alternative to sentence-based alignment pro- grams, and applies word-level constraints us- ing a version of Brown et al.'s Model 2 (Brown et al., 1993), modified and extended to deal with robustness issues. Word_align was tested on a subset of Canadian Itansards supplied by Simard (Simard et al., 1992). The combination of word_align plus char_align reduces the variance (average square error) by a factor of 5 over char_align alone. More importantly, because word_align and char_align were designed to work robustly on texts that are smaller and more noisy than the 1tansards, it has been pos- sible to successfully deploy the programs at AT&T Language Line Services, a commercial translation service, to help them with difficult terminology.

We describe our experience with automatic alignment of sentences in parallel English-Chinese texts. Our report concerns three related topics: (1) progress on the HKUST English-Chinese Parallel Bilingual Corpus; (2) experiments addressing the applicability of Gale & Church's (1991) lengthbased statistical method to the task of alignment involving a non-Indo-European language; and (3) an improved statistical method that also incorporates domain-specific lexical cues.

We present a pattern matching method for compiling a bilingual lexicon of nouns and proper nouns from unaligned, noisy parallel texts of Asian/IndcEuropean language pairs. Tagging information of one guage is used. Word frequency and position information for high and low frequency words are represented in two different vector forms for pattern matching. New anchor point finding and noise elimination techniques are introduced. We obtained a 73.1% precision. We also show how the results can be used in the compilation of domain-specific noun phrases.

Abstract not found

NLP work is to construct a detailed map of the correspondence between a text and its translation. Several auto- matic methods for this task have been proposed in recent years. Yet even the best of these methods can err by several typeset pages. The Smooth Injective Map Recognizer (SIMR) is a new bitext mapping algorithm. SIMR's errors are smaller than those of the previous front-runner by more than a factor of 4. Its robustness has en- abled new commercial-quality applications. The greedy nature of the algorithm makes it independent of memory resources. Unlike other bitext mapping algorithms, SIMR allows crossing correspondences to account for word order differences. Its output can be converted quickly and easily into a sen- tence alignment. SIMR's output has been used to align more than 200 megabytes of the Canadian Hansards for publication by the Linguistic Data Consortium.