Algorithms for the alignment of words in translated texts are well established. However, only recently new approaches have been proposed to identify word translations from non-parallel or even unrelated texts. This task is

this paper, we present an initial algorithm for translating technical terms using a pair of non-parallel corpora. Evalution results show translation precisions at around 30% when only the top candidate is considered. While this precision is lower than that achieved with parallel corpora, we show that top 20 candidate output from our algorithm allows translators to increase their accuracy by 50.9%. In the following sections, we first describe a pair of non-parallel corpora we use for experiments, and then we introduce the Word Relation Matrix (WoRM), a statistical word feature representation for technical term translation from non-parallel corpora. We evaluate the effectiveness of this feature with two sets of experiments, using English/English, and English/Japanese non-parallel corpora. 2. BACKGROUND

This paper presents work on the task of constructing a word-level translation lexicon purely from unrelated monolingual corpora. We combine various clues such as cognates, similar context, preservation of word similarity, and word frequency. Experimental results for the construction of a German-English noun lexicon are reported.

We present a method for learning bilingual translation lexicons from monolingual corpora. Word types in each language are characterized by purely monolingual features, such as context counts and orthographic substrings. Translations are induced using a generative model based on canonical correlation analysis, which explains the monolingual lexicons in terms of latent matchings. We show that high-precision lexicons can be learned in a variety of language pairs and from a range of corpus types. 1

In this paper, we present a model of statistical word-level mapping for comparable corpora. The approach is based on the assumption that if two terms have close distributional profiles, their corresponding translations' distributional profiles should be close in a comparable corpus. The proposed model is described. A preliminary investigation on intralanguage comparable corpora is laid out. The preliminary results are >92% accurate, suggesting the feasibility of the model. The model needs to undergo some improvements and should be tested cross linguistically before assessing its significance.

We present various methods to train word-level translation models for statistical machine translation systems that use widely different knowledge sources ranging from parallel corpora and a bilingual lexicon to only monolingual corpora in two languages. Some novel methods are presented and previously published methods are reviewed. Also, a common evaluation metric enables the first quantitative comparison of these approaches.

Bilingual term lists are extensively used as a resource for dictionary-based Cross-Language Information Retrieval (CLIR), in which the goal is to find documents written in one natural language based on queries that are expressed in another. This paper identifies eight types of terms that affect retrieval effectiveness in CLIR applications through their coverage by general-purpose bilingual term lists, and reports results from an experimental evaluation of the coverage of 35 bilingual term lists in news retrieval application. Retrieval effectiveness was found to be strongly influenced by term list size for lists that contain between 3,000 and 30,000 unique terms per language. Supplemental techniques for named entity translation were found to be useful with even the largest lexicons. The contribution of named entity translation was evaluated in a cross-language experiment involving English and Chinese. Smaller effects were observed from deficiencies in the coverage of domainspecific terminology when searching news stories.

We present a geometric view on bilingual lexicon extraction from comparable corpora, which allows to re-interpret the methods proposed so far and identify unresolved problems. This motivates three new methods that aim at solving these problems. Empirical evaluation shows the strengths and weaknesses of these methods, as well as a significant gain in the accuracy of extracted lexicons. 1

In this paper we investigate unsupervised name transliteration using comparable corpora, corpora where texts in the two languages deal in some of the same topics — and therefore share references to named entities — but are not translations of each other. We present two distinct methods for transliteration, one approach using an unsupervised phonetic transliteration method, and the other using the temporal distribution of candidate pairs. Each of these approaches works quite well, but by combining the approaches one can achieve even better results. We believe that the novelty of our approach lies in the phonetic-based scoring method, which is based on a combination of carefully crafted phonetic features, and empirical results from the pronunciation errors of second-language learners of English. Unlike previous approaches to transliteration, this method can in principle work with any pair of languages in the absence of a training dictionary, provided one has an estimate of the pronunciation of words in text. 1

An unsupervised method for word sense disambiguation using a bilingual comparable corpus was developed. First, it extracts statistically significant pairs of related words from the corpus of each language. Then, aligning pairs of related words translingually, it calculates the correlation between the senses of a first-language polysemous word and the words related to the polysemous word, which can be regarded as clues for determining the most suitable sense. Finally, for each instance of the polysemous word, it selects the sense that maximizes the score, i.e., the sum of the correlations between each sense and the clues appearing in the context of the instance. To overcome both the problem of ambiguity in the translingual alignment of pairs of related words and that of disparity of topical coverage between corpora of different languages, an algorithm for calculating the correlation between senses and clues iteratively was devised. An experiment using Wall Street Journal and Nihon Keizai Shimbun corpora showed that the new method has promising performance; namely, the applicability and precision of its sense selection are 88.5 % and 77.7%, respectively, averaged over 60 test polysemous words. 1