Транслитерация

Named Entity recognition (NER) is an im- portant part of many natural language pro- cessing tasks. Most current approaches employ machine learning techniques and require supervised data. However, many languages …...

Named Entity recognition (NER) is an important part of many natural language processing tasks. Current approaches of- ten employ machine learning techniques and require supervised data. However, many languages lack …...

Most current machine transliteration sys- tems employ a corpus of known source- target word pairs to train their system, and typically evaluate their systems on a similar corpus. In this …...

Transliteration is the task of converting a word from one alphabetic script to another. We present a novel, substring-based ap- proach to transliteration, inspired by phrase- based models of machine …...

Transliteration is the process of transcribing words from a source script to a target script. These words can be content words or proper nouns. They may be of local or …...

We present a method to transliterate names in the framework of end-to-end statistical machine translation. The system is trained to learn when to transliterate. For Arabic to English MT, we …...

This paper introduces a new method for iden- tifying named-entity (NE) transliterations in bilingual corpora. Recent works have shown the advantage of discriminative approaches to transliteration: given two strings (ws, …...

This paper presents a semi-supervised learn- ing framework for mining Chinese-English lexicons from large amount of Chinese Web pages. The issue is motivated by the ob- servation that many Chinese …...

AbstractWe describe ScriptTranscriber, an open source toolkit for extracting transliterations in comparable corpora from languages written in different scripts. The system includes various methods for extracting potential terms of interest …...

This paper proposes a general learning frame- work for a class of problems that require learn- ing over latent intermediate representations. Many natural language processing (NLP) de- cision problems are …...

AbstractIn this paper, we present a system for transliterating the Arabic-based script of Urdu to a Roman transliteration scheme. The system is integrated into a larger system consisting of a …...

There are some cases where the non- Japanese buyers are unable to find products they want through the Japanese shopping Web sites because they require Japanese queries. We propose to …...

This paper describes QCRI-MES’s sub- mission on the English-Russian dataset to the Eighth Workshop on Statistical Ma- chine Translation. We generate improved word alignment of the training data by incorporating …...

Hindi is the lingua-franca of India. Al- though all non-native speakers can com- municate well in Hindi, there are only a few who can read and write in it. In …...

This paper describes the English-Russian and Russian-English statistical machine translation (SMT) systems developed at Yandex School of Data Analysis for the shared translation task of the ACL 2013 Eighth Workshop …...

This paper describes the string-to-tree sys- tems built at the University of Edin- burgh for the WMT 2014 shared trans- lation task. We developed systems for English-German, Czech-English, French- English, …...

In this study we compare two machine translation devices on twelve machine translation medical- domain specific tasks, and two transliteration tasks, altogether involving twelve language pairs, including English-Chinese and English-Russian, …...

In this paper we improve Urdu→Hindi⇄English machine trans- lation through triangulation and translit- eration. First we built an Urdu→Hindi SMT system by inducing triangulated and transliterated phrase-tables from Urdu–English and …...

Code-mixing is frequently observed in user generated content on social media, especially from multilingual users. The linguistic complexity of such content is compounded by presence of spelling vari- ations, transliteration …...

AbstractWe present a compendium of 110 Statistical Machine Translation systems built from parallel corpora of 11 Indian languages belonging to both Indo-Aryan and Dravidian families. We analyze the relationship between …...

. Bilingual lexicons play a vital role in cross-language information retrieval and machine translation. The manual construction of these lexicons is often costly and time consuming. Word alignment techniques are …...

AbstractThis article describes a methodology of recovering and preservation of old Romanian texts and problems related to their recognition. Our focus is to create a gold corpus for Romanian language …...

Transliteration from low-resource languages is difficult, in large part due to the small amounts of data available for training transliteration systems. In this paper, we evaluate the effectiveness of several …...

Automatic identification of Arabic dialects in a text is a difficult task, especially for Maghreb languages and when they are written in Arabic or Latin characters (Arabizi). These texts are …...

For many of the world’s languages, the Bible is the only significant bilingual, or even monolingual, text, making it a unique training re- source for tasks such as translation, named …...

AbstractIn this article, we discuss which text, speech, and image technologies have been developed, and would be feasible to develop, for the approximately 60 Indigenous languages spoken in Canada. In …...

AbstractTransliteration is generally a phonetically based transcription across different writing systems. It is a crucial task for various downstream natural language processing applications. For the Myanmar (Burmese) language, robust automatic …...

AbstractBilingual dictionary induction (BDI) is the task of accurately translating words to the target language. It is of great importance in many low-resource scenarios where cross-lingual training data is not …...

AbstractOut-of-vocabulary words are still a challenge in cross-lingual Natural Language Processing tasks, for which transliteration from source to target language or script is one of the solutions. In this study, …...

AbstractThis work presents a method of word sense clustering that differentiates homonyms and merge homophones, taking Japanese as an example, where orthographical variation causes problem for language processing. It uses …...

AbstractGesture typing is a method of typing words on a touch-based keyboard by creating a continuous trace passing through the relevant keys. This work is aimed at developing a keyboard …...

AbstractThe transformer has been shown to outperform recurrent neural network-based sequence-to-sequence models in various word-level NLP tasks. Yet for character-level transduction tasks, e.g. morphological inflection generation and historical text normalization, …...

AbstractMany sequence-to-sequence tasks in natural language processing are roughly monotonic in the alignment between source and target sequence, and previous work has facilitated or enforced learning of monotonic attention behavior …...

AbstractTransfer learning based on pretraining language models on a large amount of raw data has become a new norm to reach state-of-the-art performance in NLP. Still, it remains unclear how …...

The problem of transliteration deals with the phonetic transcription of text from a source writing system into a target writing system. With the inception of neural net models like Sequence-to-sequence …...

AbstractIn this paper we present the final result of a project focused on Tunisian Arabic encoded in Arabizi, the Latin-based writing system for digital conversations. The project led to the …...

AbstractMultilingual language models have shown impressive cross-lingual transfer ability across a diverse set of languages and tasks. To improve the cross-lingual ability of these models, some strategies include transliteration and …...