Our feature space consists of both these standard monolingual features and cross-lingual similarities among documents.

The cross-lingual similarities are valuated using different translation mechanisms, e.g., dictionary-based translation or machine translation, or even without any translation at all.

4.2 Cross-lingual Document Similarities

Our problem setting differs from cross-lingual web search, where the goal is to return machine-translated results from one language in response to a query from another (Lavrenko et al., 2002).

To allow for cross-lingual information, we extend the order of individual documents into that of bilingual document pairs: given two bilingual document pairs, we will write (6(1),C§-1)) > (eg2),c§2)) to indi-

The advantages are twofold: (l) we can treat multiple cross-lingual document similarities the same way as the commonly used query-document features in a uniform manner of learning; (2) with the similarities, the relevance estimation on bilingual document pairs can be enhanced, and this in return can improve the ranking of documents.

,n. Based on that, we produce a set of bilingual ranking instances 8 = {<I>ij, zij}, where each <I>ij = {xi;yj;sij} is the feature vector of (6,, cj) consisting of three components: xi = f (qe, 61-) is the vector of monolingual relevancy features of 6,, yi = f (qc, cj) is the vector of monolingual relevancy features of 03-, and sij = sim(ei, 03-) is the vector of cross-lingual similarities between ei and 03-, and zij 2 (C(61), C(03)) is the corresponding click counts.

Cross-lingual tasks are especially difficult due to the compounding effect of errors in language processing and errors in machine translation (MT).

In this paper, we present an error analysis of a new cross-lingual task: the SW task, a sentence-level understanding task which seeks to return the English 5W's (Who, What, When, Where and Why) corresponding to a Chinese sentence.

The best cross-lingual 5W system was still 19% worse than the best monolingual 5W system, which shows that MT significantly degrades sentence-level understanding.

Cross-lingual applications address this need by presenting information in the speaker’s language even when it originally appeared in some other language, using machine

In this paper, we present an evaluation and error analysis of a cross-lingual application that we developed for a government-sponsored evaluation, the 5 W task.

In this paper, we address the cross-lingual 5 W task: given a source-language sentence, return the 5W’s translated (comprehensibly) into the target language.

The cross-lingual 5W task is closely related to cross-lingual information retrieval and cross-lingual question answering (Wang and Card 2006; Mitamura et al.

In cross-lingual information extraction (Sudo et al.

This paper focuses on the problem of cross-lingual sentiment classification, which leverages an available English corpus for Chinese sentiment classification by using the English corpus as training data.

In this paper, we propose to use the co-training approach to address the problem of cross-lingual sentiment classification.

In this study, we focus on the problem of cross-lingual sentiment classification, which leverages only English training data for supervised sentiment classification of Chinese product reviews, without using any Chinese resources.

In this study, we focus on improving the corpus-based method for cross-lingual sentiment classification of Chinese product reviews by developing novel approaches.

Cross-domain text classification can be considered as a more general task than cross-lingual sentiment classification.

In particular, several previous studies focus on the problem of cross-lingual text classification, which can be considered as a special case of general cross-domain text classification.

In the context of cross-lingual sentiment classification, each labeled English review or unlabeled Chinese review has two views of features: English features and Chinese features.

We believe that this property would be useful in transliteration extraction, cross-lingual information retrieval applications.

Denoting the number of cross-lingual mappings that are common in both A and Q as CA0, the number of cross-lingual mappings in A as CA and the number of cross-lingual mappings in Q as Cg, precision Pr is given as CAglCA, recall Be as GAO/CG and F-score as 2P7“ - Rc/(Pr + Re).

We expect a good alignment to have a sharp cross-lingual mapping with low alignment entropy.

One of the main challenges of unsupervised multilingual learning is to exploit cross-lingual patterns discovered in data, while still allowing a wide range of language-specific idiosyncrasies.

For each pair of coupled bilingual constituents, a pair of part-of-speech sequences are drawn jointly from a cross-lingual distribution.

Research in this direction was pioneered by (Wu, 1997), who developed Inversion Transduction Grammars to capture cross-lingual grammar variations such as phrase re-orderings.