Climbing the Tower of Babel: Unsupervised Multilingual Learning

UMCC_DLSI: Reinforcing a Ranking Algorithm with Sense Frequencies and Multidimensional Semantic Resources to solve Multilingual Word Sense Disambiguation

This work introduces a new unsupervised approach to multilingual word sense disambiguation. Its main purpose is to automatically choose the intended sense (meaning) of a word in a particular context for different languages. It does so by selecting the correct Babel synset for the word and the various Wiki Page titles that mention the word. BabelNet contains all the output information that our s...

Chapter 4 Character encoding in corpus construction

Corpus linguistics has developed, over the past three decades, into a rich paradigm that addresses a great variety of linguistic issues ranging from monolingual research of one language to contrastive and translation studies involving many different languages. Today, while the construction and exploitation of English language corpora still dominate the field of corpus linguistics, corpora of ot...

Building the Tower of Babel : Converting

Are We Moving Toward an Information SuperHighway or a Tower of Babel? The Challenge of Large-Scale Semantic Heterogeneity

2016 BUT Babel System: Multilingual BLSTM Acoustic Model with i-Vector Based Adaptation

The paper provides an analysis of BUT automatic speech recognition systems (ASR) built for the 2016 IARPA Babel evaluation. The IARPA Babel program concentrates on building ASR system for many low resource languages, where only a limited amount of transcribed speech is available for each language. In such scenario, we found essential to train the ASR systems in a multilingual fashion. In this w...