This paper presents HITS’ system for monolingual and cross-lingual entity linking at TAC 2012. We propose a joint system for entity disambiguation, recognition of NILs and clustering using Markov Logic. The proposed model (1) is global, i.e. a group of mentions in a text is disambiguated in one single step combining various global and local features, and (2) performs disambiguation, unknown ent...

How do we parse the languages for which no treebanks are available? This contribution addresses the cross-lingual viewpoint on statistical dependency parsing, in which we attempt to make use of resource-rich source language treebanks to build and adapt models for the under-resourced target languages. We outline the benefits, and indicate the drawbacks of the current major approaches. We emphasi...

Cross-lingual representation learning is an important step in making NLP scale to all the world’s languages. Recent work on bilingual lexicon induction suggests that it is possible to learn cross-lingual representations of words based on similarities between images associated with these words. However, that work focused on the translation of selected nouns only. In our work, we investigate whet...

Creating knowledge bases based on the crowd-sourced wikis, like Wikipedia, has attracted significant research interest in the field of intelligent Web. However, the derived taxonomies usually contain many mistakenly imported taxonomic relations due to the difference between the user-generated subsumption relations and the semantic taxonomic relations. Current approaches to solving the problem s...

Some languages lack large knowledge bases and good discriminative features for Name Entity Recognition (NER) that can generalize to previously unseen named entities. One such language is Arabic, which: a) lacks a capitalization feature; and b) has relatively small knowledge bases, such as Wikipedia. In this work we address both problems by incorporating cross-lingual features and knowledge base...

A notable challenge in cross-lingual wikification is the problem of retrieving English Wikipedia title candidates given a non-English mention, a step that requires translating names written in a foreign language into English. Creating training data for name translation requires significant amount of human efforts. In order to cover as many languages as possible, we propose a probabilistic model...

With the advent of Big Data concept, a lot of attention has been paid to structuring and giving semantic to this data. Knowledge bases like DBPedia play an important role to achieve this goal. Question answering systems are common approach to address expressivity and usability of information extraction from knowledge bases. Recent researches focused only on monolingual QA systems while cross-li...

This paper describes the Cross-Lingual Word Sense Disambiguation system UvTWSD1, developed at Tilburg University, for participation in two SemEval-2 tasks: the Cross-Lingual Word Sense Disambiguation task and the Cross-Lingual Lexical Substitution task. The UvT-WSD1 system makes use of k-nearest neighbour classifiers, in the form of single-word experts for each target word to be disambiguated. ...

This paper proposes cross-lingual language modeling for transcribing source resourcepoor languages and translating them into target resource-rich languages if necessary. Our focus is to improve the speech recognition performance of low-resource languages by leveraging the language model statistics from resource-rich languages. The most challenging work of cross-lingual language modeling is to s...

Word semantic similarity measuring is a fundamental issue to many NLP applications and the globalization has made an urgent request for cross-lingual word similarity measure. This paper proposed a word semantic similarity measure which is able to work in cross-lingual scenarios. Basically, a concept can be defined by a set of attributes. The basic idea of this work is to compute the similarity ...