Pbsmt presenation waleed_oransa_29_april2010
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The document provides an overview of statistical machine translation, including its background, approaches, evaluation, and online services. It discusses how statistical machine translation works by training language and translation models on parallel corpora to generate translations that are both fluent and faithful. Evaluation can be done through human assessment or automatic metrics like BLEU that compare machine translations to human references. Popular online machine translation services are also reviewed.
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![Statistical Machine Translation Waleed Oransa, M.Sc. College of Computing and Information Technology Arab Academy for Science and Technology Cairo, Egypt [email_address]](https://image.slidesharecdn.com/pbsmtpresenationwaleedoransa29april2010-100429093124-phpapp01/85/Pbsmt-presenation-waleed_oransa_29_april2010-1-320.jpg)
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Junki Matsuo - 2015 - Source Phrase Segmentation and Translation for Japanese...Association for Computational Linguistics
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Behzad Ghorbani presented research on unsupervised cross-lingual speaker adaptation for text-to-speech synthesis. The goal was to personalize speech-to-speech translation by adapting synthesized speech output to the user's voice using speech recognition. Three studies on unsupervised and cross-lingual adaptation approaches were discussed: 1) Finnish-English using decision tree construction, 2) Chinese-English comparing supervised and unsupervised schemes, and 3) English-Japanese using unsupervised adaptation and evaluation of synthetic speech quality.
Hps a hierarchical persian stemming method
In this paper, a novel hierarchical Persian Stemming approach based on the Part-Of-Speech (POS) of the
word in a sentence is presented. The implemented stemmer includes hash tables and several deterministic
finite automata (DFA) in its different levels of hierarchy for removing the prefixes and suffixes of the
words. We had two intentions in using hash tables in our method. The first one is that the DFA don’t
support some special words, so hash table can partly solve the addressed problem. And the second goal is
to speed up the implemented stemmer with omitting the time that DFA need. Because of the hierarchical
organization, this method is fast and flexible enough. Our experiments on test sets from Hamshahri
Collection and Security News from ICTna.ir Site show that our method has the average accuracy of
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S URVEY O N M ACHINE T RANSLITERATION A ND M ACHINE L EARNING M ODELS
Globalization and growth of Internet users truly demands for almost all internet based applications to
support
l
oca
l l
anguages. Support
of
l
oca
l
l
anguages can be
given in all internet based applications by
means of Machine Transliteration
and
Machine Translation
.
This paper provides the thorough survey on
machine transliteration models and machine learning
approaches
used for machine transliteration
over the
period
of more than two decades
for internationally used languages as well as Indian languages.
Survey
shows that linguistic approach provides better results for the closely related languages and probability
based statistical approaches are good when one of the
languages is phonetic and other is non
-
phonetic.
B
etter accuracy can be achieved only by using Hybrid and Combined models.
"Machine Translation 101" and the Challenge of Patents
Delivered at the European Patent Office's annual Patent Information Conference (EPOPIC 2014)
November 5th 2014
Warsaw, Poland.
In this talk, we give an introduction as to how machine translation works and what makes certain content types and languages more difficult than others.
Machine Translation Approaches and Design Aspects
This document discusses machine translation approaches and design aspects. It describes example-based machine translation (EBMT) which translates sentences from English to Hindi. EBMT relies on a database of translated examples for translation. The document outlines the typical process of machine translation, including text input, analysis, transfer, generation and morphological/syntactic analysis. It also describes different machine translation approaches like knowledge-based MT, statistical MT, and example-based MT. Example-based MT derives translations from aligned corpora of existing translated examples by matching input sentences, retrieving translations, and recombining adapted segments.
NLP
Natural language processing (NLP) involves analyzing and understanding human language to allow interaction between computers and humans. The document outlines key steps in NLP including morphological analysis, syntactic analysis, semantic analysis, and pragmatic analysis to convert text into structured representations. It also discusses statistical NLP and real-world applications such as machine translation, question answering, and speech recognition.
NLP
Natural language processing (NLP) is introduced, including its definition, common steps like morphological analysis and syntactic analysis, and applications like information extraction and machine translation. Statistical NLP aims to perform statistical inference for NLP tasks. Real-world applications of NLP are discussed, such as automatic summarization, information retrieval, question answering and speech recognition. A demo of a free NLP application is presented at the end.
D2 anandkumar
This document describes a factored statistical machine translation system from English to Tamil that incorporates Tamil morphology. The system first reorders and factors the English text, then uses morphological analysis and generation tools for Tamil to further factorize the text. This addresses challenges of translating between languages with different morphological structures and word orders. The system was shown to improve over a baseline SMT system for English to Tamil translation by integrating linguistic information like lemmas and morphological features.
**JUNK** (no subject)
The document discusses natural language processing (NLP) for Tamil to Hindi conversion. It introduces the Universal Networking Language (UNL) as an intermediate representation to express information across languages. UNL allows text to be converted to different languages like converting a webpage to various natural languages. The document then discusses the advantages of developing machine translation between Tamil and other languages, particularly English and Hindi. It outlines the components needed for a Tamil-Hindi machine translation system, including morphological analyzers for Tamil and Hindi, a word mapping unit, and generators.
Machine translation
This slides covers introduction about machine translation, some technique using in MT such as example based MT and statistical MT, main challenge facing us in machine translation, and some examples of application using in MT
Effectof morphologicalsegmentation&de segmentationonmachinetranslation
In this presentation, I tried to do study on effect of Morphological Segmentation and De-segmentation on the effect and quality of Machine translation with respect to English-Konkani Translation.
Svetlin Nakov - Improved Word Alignments Using the Web as a Corpus
Nakov P., Nakov S., Paskaleva E., Improved Word Alignments Using the Web as a Corpus, Proceedings of the International Conference RANLP 2007 (Recent Advances in Natural Language Processing), pp. 400-405, ISBN 978-954-91743-7-3, Borovets, Bulgaria, 27-29 September 2007
English to Bangla Translation
The document discusses machine translation and text translation from English to Bangla. It provides an overview of different machine translation approaches, including direct, indirect, example-based, and statistical methods. Previous work on English to Bangla translation is summarized, including approaches using chunking, parsing, and converting parse trees. The document also discusses the speaker's qualifications and proposes investigating the role of machine learning in a framework for detecting external plagiarism from monolingual documents.
Machine translation ppt by shantanu arora
This document discusses machine translation and its techniques. It begins with an introduction to machine translation, defining it as using computer software to translate between natural languages. It then covers various machine translation techniques including example-based, dictionary-based, rule-based, hybrid, neural, statistical, interlingual, and transfer-based. It also discusses challenges in machine translation such as ambiguity, different word orders between languages, compound words, and missing names. It concludes by mentioning some applications of machine translation like Google Translator, Bing Translator, and SYSTRAN.
Natural Language Processing
This document discusses natural language processing (NLP) from a developer's perspective. It provides an overview of common NLP tasks like spam detection, machine translation, question answering, and summarization. It then discusses challenges in NLP like ambiguity and new forms of written language. The document goes on to explain how probabilistic models are used in NLP to infer language properties and complete tasks like sentence completion and phrase rearrangement using concepts like language models. It also covers text processing techniques like tokenization and regular expressions. Finally, it discusses spelling correction in detail using techniques like noisy channel modeling and confusion matrices.
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core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
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structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
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Pbsmt presenation waleed_oransa_29_april2010
- 1. Statistical Machine Translation Waleed Oransa, M.Sc. College of Computing and Information Technology Arab Academy for Science and Technology Cairo, Egypt [email_address]
- 6. Interlingua Semantic Syntactic Structure Word s Semantic Syntactic Structure Word s Direct Syntactic Transfer Semantic Transfer Source Language Text Target Language Text Conceptual Generation Semantic Generation Syntactic Generation Morphological Generation Conceptual Analysis Semantic Analysis Parsing Morphological Analysis Better Quality & More difficulty
- 12. SMT Training Phase English Sentences PBSMT System Training Language Model (Arabic) Input: Training Corpus Arabic/English Bi-Text Output: Language Model and Translation Model Arabic Sentences Translation Model (English/Arabic) Language Modeling Training (Tool: SRILM toolkit) Translation Modeling Training (Tool: Giza++ & Moses toolkit) What is the Language Model?
- 14. LM Role in SMT مشرف يجتمعوا مشرف تجتمع مشرف يجتمع مشرف يتقابل مشرف يقابل مشرف يجتمعا مشرف تجتمعا مشرف يجتمعن مشرف يجتمعون مشرف يتقابلان Language Model مشرف xxxx مع كبار المسؤولين المدنيين والعسكريين Language Model يجتمع يجتمعوا تجتمع يجتمع يتقابل يقابل يجتمعا تجتمعا يجتمعن يجتمعون يتقابلان
- 16. SMT Training Phase English Sentences PBSMT System Training Language Model (Arabic) Input: Training Corpus Arabic/English Bi-Text Output: Language Model and Translation Model Arabic Sentences Translation Model (English/Arabic) Language Modeling Training (Tool: SRILM toolkit) Translation Modeling Training (Tool: Giza++ & Moses toolkit) What is the Translation Model?
- 21. How TM & LM work together? Musharraf Meets with Senior Civilian and Military Officials مشرف ***** مع كبار المسؤولين المدنيين والعسكريين يجتمع Language Model Translation Model يجتمعوا تجتمع يجتمع يتقابل يقابل يجتمعا تجتمعا يجتمعن يجتمعون يتقابلان
- 24. PBSMT Training Phase English Sentences PBSMT System Training Language Model (Arabic) Input: Training Corpus Arabic/English Bi-Text Output: Language Model and Translation Model PBSMT Normal Training Phase Arabic Sentences Translation Model Phrase Table (English/Arabic) Language Modeling Training (Tool: SRILM toolkit) Translation Modeling Training (Tool: Giza++ & Moses toolkit)
- 25. Phrase based alignment (The prophet, الرسول ) (The prophet Mohamed, الرسول محمد ) (great man, رجل عظيم ) (Mohamed is a great man, محمد رجل عظيم ) (The prophet Mohamed is a great man, الرسول محمد رجل عظيم ) etc. Extract all phrase: English to Arabic word alignment Arabic to English word alignment Intersection of both alignments
- 29. Human Evaluation 5 Adequacy (1-5) 4 Fluency (1-5) مشرف يتقابل مع كبار المسؤولين المدنيين والعسكريين بخصوص ملف الحد من الانتشار النووي MT Musharraf Meets with Senior Civilian and Military Officials to Examine Nuclear Anti-Proliferation Dossier Source
- 32. Online MT Services Review البنتان قالتا أنّنا جيّدين SK 8 قال الاثنان بنات " نحن جيّد " SY 7 ان فتاتين " نحن جيدة " MS 6 الفتاتين وقال " نحن جاهزون " GO 5 قالت الفتاتان " نحن جيدات " The two girls said "we are good" B خمسة عشر بنتًا Sakhr Trjem (SK) 4 خمسة عشر بنات Systran translator (SY) 3 خمسة عشر الفتيات MS-Bing Translator (MS) 2 خمسة عشر فتيات Google Translate (GO) 1 خمس عشرة فتاة Fifteen girls A Arabic Translation Sentence/Translation Service
- 33. Thank you شكراً