Word Sense Disambiguation using Cross-Lingual approach has been used successfully for languages like Farsi and Hindi. However, a comparable corpus in the form of Wikipedia articles available in English and Hindi has been used for such a task. This motivated us to further the approach and test the results when a parallel corpus is used. In this project, we specifically wanted to observe if the a...

Cross-Lingual Lexical Substitution (CLLS) is the task that aims at providing for a target word in context, several alternative substitute words in another language. The proposed sets of translations may come from external resources or be extracted from textual data. In this paper, we apply for the first time an unsupervised cross-lingual WSD method to this task. The method exploits the results ...

In this work, we will explore different approaches used in Cross-Lingual Information Retrieval (CLIR) systems. Mainly, CLIR systems which use statistical machine translation (SMT) systems to translate queries into collection language. This will include using SMT systems as a black box or as a white box, also the SMT systems that are tuned towards better CLIR performance. After that, we will pre...

This paper proposes a new bootstrapping framework using cross-lingual information projection. We demonstrate that this framework is particularly effective for a challenging NLP task which is situated at the end of a pipeline and thus suffers from the errors propagated from upstream processing and has low-performance baseline. Using Chinese event extraction as a case study and bitexts as a new s...

Probabilistic topic models are unsupervised generative models which model document content as a two-step generation process, that is, documents are observed as mixtures of latent concepts or topics, while topics are probability distributions over vocabulary words. Recently, a significant research effort has been invested into transferring the probabilistic topic modeling concept from monolingua...

Cross-lingual information extraction is the task of distilling facts from foreign language (e.g. Chinese text) into representations in another language that is preferred by the user (e.g. English tuples). Conventional pipeline solutions decompose the task as machine translation followed by information extraction (or vice versa). We propose a joint solution with a neural sequence model, and show...

We evaluate the feasibility of applying currently available research tools to the problem of cross lingual QA. We establish a task baseline by combining a cross lingual IR system with a monolingual QA system in a very short amount of time. A higher precision strategy involves applying the monolingual QA system to an automatically translated question, assumed to be correct. A higher coverage str...

We describe the LIMSI system for the SemEval-2013 Cross-lingual Word Sense Disambiguation (CLWSD) task. Word senses are represented by means of translation clusters in different languages built by a cross-lingual Word Sense Induction (WSI) method. Our CLWSD classifier exploits the WSI output for selecting appropriate translations for target words in context. We present the design of the system ...

In cross-lingual dependency annotation projection, information is often lost during transfer because of early decoding. We present an end-to-end graph-based neural network dependency parser that can be trained to reproduce matrices of edge scores, which can be directly projected across word alignments. We show that our approach to cross-lingual dependency parsing is not only simpler, but also a...