739 research outputs found
A Comparison of Different Machine Transliteration Models
Machine transliteration is a method for automatically converting words in one
language into phonetically equivalent ones in another language. Machine
transliteration plays an important role in natural language applications such
as information retrieval and machine translation, especially for handling
proper nouns and technical terms. Four machine transliteration models --
grapheme-based transliteration model, phoneme-based transliteration model,
hybrid transliteration model, and correspondence-based transliteration model --
have been proposed by several researchers. To date, however, there has been
little research on a framework in which multiple transliteration models can
operate simultaneously. Furthermore, there has been no comparison of the four
models within the same framework and using the same data. We addressed these
problems by 1) modeling the four models within the same framework, 2) comparing
them under the same conditions, and 3) developing a way to improve machine
transliteration through this comparison. Our comparison showed that the hybrid
and correspondence-based models were the most effective and that the four
models can be used in a complementary manner to improve machine transliteration
performance
Natural language processing
Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems
Bridge Correlational Neural Networks for Multilingual Multimodal Representation Learning
Recently there has been a lot of interest in learning common representations
for multiple views of data. Typically, such common representations are learned
using a parallel corpus between the two views (say, 1M images and their English
captions). In this work, we address a real-world scenario where no direct
parallel data is available between two views of interest (say, and )
but parallel data is available between each of these views and a pivot view
(). We propose a model for learning a common representation for ,
and using only the parallel data available between and
. The proposed model is generic and even works when there are views
of interest and only one pivot view which acts as a bridge between them. There
are two specific downstream applications that we focus on (i) transfer learning
between languages ,,..., using a pivot language and (ii)
cross modal access between images and a language using a pivot language
. Our model achieves state-of-the-art performance in multilingual document
classification on the publicly available multilingual TED corpus and promising
results in multilingual multimodal retrieval on a new dataset created and
released as a part of this work.Comment: Published at NAACL-HLT 201
Character-level and syntax-level models for low-resource and multilingual natural language processing
There are more than 7000 languages in the world, but only a small portion of them benefit from Natural Language Processing resources and models. Although languages generally present different characteristics, “cross-lingual bridges” can be exploited, such as transliteration signals and word alignment links. Such information, together with the availability of multiparallel corpora and the urge to overcome language barriers, motivates us to build models that represent more of the world’s languages.
This thesis investigates cross-lingual links for improving the processing of low-resource languages with language-agnostic models at the character and syntax level. Specifically, we propose to (i) use orthographic similarities and transliteration between Named Entities and rare words in different languages to improve the construction of Bilingual Word Embeddings (BWEs) and named entity resources, and (ii) exploit multiparallel corpora for projecting labels from high- to low-resource languages, thereby gaining access to weakly supervised processing methods for the latter.
In the first publication, we describe our approach for improving the translation of rare words and named entities for the Bilingual Dictionary Induction (BDI) task, using orthography and transliteration information. In our second work, we tackle BDI by enriching BWEs with orthography embeddings and a number of other features, using our classification-based system to overcome script differences among languages. The third publication describes cheap cross-lingual signals that should be considered when building mapping approaches for BWEs since they are simple to extract, effective for bootstrapping the mapping of BWEs, and overcome the failure of unsupervised methods. The fourth paper shows our approach for extracting a named entity resource for 1340 languages, including very low-resource languages from all major areas of linguistic diversity. We exploit parallel corpus statistics and transliteration models and obtain improved performance over prior work. Lastly, the fifth work models annotation projection as a graph-based label propagation problem for the part of speech tagging task. Part of speech models trained on our labeled sets outperform prior work for low-resource languages like Bambara (an African language spoken in Mali), Erzya (a Uralic language spoken in Russia’s Republic of Mordovia), Manx (the Celtic language of the Isle of Man), and Yoruba (a Niger-Congo language spoken in Nigeria and surrounding countries)
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