Cross-Lingual Learning provides a mechanism to adapt NLP tools available for label rich languages to achieve similar tasks for label-scarce languages. An efficient cross-lingual tool significantly reduces the cost and effort required to manually annotate data. In this paper, we use the Recursive Autoencoder architecture to develop a Cross Lingual Sentiment Analysis (CLSA) tool using sentence al...

This paper proposes to learn languageindependent word representations to address cross-lingual dependency parsing, which aims to predict the dependency parsing trees for sentences in the target language by training a dependency parser with labeled sentences from a source language. We first combine all sentences from both languages to induce real-valued distributed representation of words under ...

The NTU-MC compilation taps on the linguistic diversity of multilingual texts available within Singapore. The current version of NTU-MC contains 595,000 words (26,000 sentences) in 7 languages (Arabic, Chinese, English, Indonesian, Japanese, Korean and Vietnamese) from 7 language families (Afro-Asiatic, Sino-Tibetan, Indo-European, Austronesian, Japonic, Korean as a language isolate and Austro-...

It has been established that incorporating word cluster features derived from large unlabeled corpora can significantly improve prediction of linguistic structure. While previous work has focused primarily on English, we extend these results to other languages along two dimensions. First, we show that these results hold true for a number of languages across families. Second, and more interestin...

We present ATTRACT-REPEL, an algorithm for improving the semantic quality of word vectors by injecting constraints extracted from lexical resources. ATTRACT-REPEL facilitates the use of constraints from monoand crosslingual resources, yielding semantically specialised cross-lingual vector spaces. Our evaluation shows that the method can make use of existing cross-lingual lexicons to construct h...

This paper describes the development of a question answering system for the RomanianEnglish cross-lingual track organized within the Cross Lingual Evaluation Forum (CLEF) 2006 campaign. The development stages of our cross-lingual Question Answering (QA) system are described incrementally throughout the paper, at the same time pinpointing the problems that occurred and the way they were addresse...

The present paper describes the development of a cross lingual query dependent snippet generation module. It is a language independent module, so it also performs as a multilingual snippet generation module. It is a module of the Cross Lingual Information Access (CLIA) system. This module takes the query and content of each retrieved document and generates a query dependent snippet for each ret...

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 5W 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. We analyze systems...

Cross-lingual information retrieval (CLIR) is a challenging task that requires overcoming linguistic barriers to match user queries with relevant documents in different languages. One of the major challenges CLIR lack parallel corpora, which hinders development effective translation models. This challenge can be addressed using snippets as dataset train Snippets automatically extracted from var...