Query Word Labeling and Back Transliteration for Indian Languages
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The document describes a system for query word labeling and back transliteration for Indian languages. It uses character n-gram features and context switch probability to label words in code-mixed languages. For back transliteration, it uses a hash-based mapping approach between languages. The system achieved high accuracy scores for language labeling and moderate scores for transliteration on Hindi, Gujarati, and Bangla test sets. Areas for future work include handling spelling variations and working on more under-resourced languages.
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RULE BASED TRANSLITERATION SCHEME FOR ENGLISH TO PUNJABI
Machine Transliteration has come out to be an emerging and a very important research area in the field of
machine translation. Transliteration basically aims to preserve the phonological structure of words. Proper
transliteration of name entities plays a very significant role in improving the quality of machine translation.
In this paper we are doing machine transliteration for English-Punjabi language pair using rule based
approach. We have constructed some rules for syllabification. Syllabification is the process to extract or
separate the syllable from the words. In this we are calculating the probabilities for name entities (Proper
names and location). For those words which do not come under the category of name entities, separate
probabilities are being calculated by using relative frequency through a statistical machine translation
toolkit known as MOSES. Using these probabilities we are transliterating our input text from English to
Punjabi.
Brill's Rule-based Part of Speech Tagger for Kadazan
This paper presents the Part of Speech Tagger (POS) for Kadazan language by
implementing Brill's approach which is also known as a Transformation-Based Error
Driven Learning approach. Kadazan language is chosen because there is not even one POS
tagger has been developed for this language yet. Hence, this study has been carried out in
order to develop a POS tagger especially for Kadazan language that can tag Kadazan
corpus systematically, help to reduce the ambiguity problem and at the same time can be
used as a learning language tool. Therefore, the main objective of this study is to automate
the tagging process for Kadazan language. Brill' approach is an enhance version of the
original Rule-Based approach which it transforms the tags based on a set of predefined
rules. Brill’s approach uses rules to transform wrong tags into correct tags in the corpus. In
order to achieve the main goal, several objectives have been set which are to create the
specific lexical and contextual rules for Kadazan language, by applying Brill’s approach
based on rules and to evaluate the effectiveness of Kadazan Part of Speech using Brill’s
approach. The tagging process is divided into four main phases. In first phase, Brill’s
approach process begins by inputting a new untagged text into the system. In second phase,
the input text will go through the initial state annotater to tag all the words inside the corpus
to its most likely tags and produce a temporary corpus. In third phase, the temporary
corpus is then compared to the goal corpus to detect if there is any errors occurred. In last
phase, the rules will be applied to reduce any errors occurred and fix the temporary corpus.
The tagging approach has been trained using two Kadazan children’s story books which
contain 2069 words. Evaluation process is done by comparing the tagging results of Brill’s
approach with the manual tagging. Kadazan Part of Speech Tagger has achieved around 93
% of accuracy. This study has shown how Brill’s tagging approach can be used to identify
tags for Kadazan language.
Aruna Kumar_Resume
This document contains a resume for Aruna Kumar K R, a linguist professional with over 7 years of experience in Kannada translation, proofreading, and editing. He has a master's degree in English literature and postgraduate diploma in translation. He has worked as a lead external linguist for Kannada on various projects for Google, providing translation from English to Kannada and vice versa, linguistic review, and language quality assurance. His skills include translation tools like Idiom Desktop Workbench and technical understanding of translation workflows.
ON THE UTILITY OF A SYLLABLE-LIKE SEGMENTATION FOR LEARNING A TRANSLITERATION...
Source and target word segmentation and alignment is a primary step in the statistical learning of a Transliteration. Here, we analyze the benefit of a syllable-like segmentation approach for learning a transliteration from English to an Indic language, which aligns the training set word pairs in terms of sub-syllable-like units instead of individual character units. While this has been found useful in the case of dealing with Out-of-vocabulary words in English-Chinese in the presence of multiple target dialects, we asked if this would be true for Indic languages which are simpler in their phonetic representation and pronunciation. We expected this syllable-like method to perform marginally better, but we found instead that even though our proposed approach improved the Top-1 accuracy, the individual-character-unit alignment model
somewhat outperformed our approach when the Top-10 results of the system were re-ranked using language modeling approaches. Our experiments were conducted for English to Telugu transliteration (our method will apply equally well to most written Indic languages); our training consisted of a syllable-like segmentation and alignment of a large training set, on which we built a statistical model by modifying a previous character-level maximum entropy based Transliteration learning system due to Kumaran and Kellner; our testing consisted of using the same segmentation of a test English word, followed by applying the model, and reranking the resulting top 10 Telugu words. We also report the dataset creation and selection since standard datasets are not available.
DISCRIMINATION OF ENGLISH TO OTHER INDIAN LANGUAGES (KANNADA AND HINDI) FOR O...
India is a multilingual multi-script country. In every state of India there are two languages one is state local language and the other is English. For example in Andhra Pradesh, a state in India, the document may contain text words in English and Telugu script. For Optical Character Recognition (OCR) of such a bilingual document, it is necessary to identify the script before feeding the text words to the OCRs of individual scripts. In this paper, we are introducing a simple and efficient technique of script identification for Kannada, English and Hindi text words of a printed document. The proposed approach is based on the horizontal and vertical projection profile for the discrimination of the three scripts. The feature extraction is done based on the horizontal projection profile of each text words. We analysed 700 different words of Kannada, English and Hindi in order to extract the discrimination features and for the development of knowledge base. We use the horizontal projection profile of each text word and based on the horizontal projection profile we extract the appropriate features. The proposed system is tested on 100 differentdocument images containing more than 1000 text words of each script and a classification rate of 98.25%, 99.25% and 98.87% is achieved for Kannada, English and Hindi respectively.
Comparison Analysis of Post- Processing Method for Punjabi Font
The document describes a study that compares different post-processing methods for recognizing handwritten Punjabi (Gurmukhi) text from optical character recognition (OCR) systems. The study proposes using four phases: 1) an AVL tree implementation method to find suggestions, 2) a symmetric delete spelling correction algorithm, 3) taking the union of the results from the first two methods, and 4) ranking the suggestions based on a soundex approach. The goal is to correct errors and improve recognition rates for OCR of handwritten Gurmukhi text by using these post-processing techniques. The methods are tested on a database of over 115,000 Gurmukhi words and the results aim to reduce recognition errors and
Punjabi Text Classification using Naïve Bayes, Centroid and Hybrid Approach
Punjabi Text Classification is the process of assigning predefined classes to the unlabelled text
documents. Because of dramatic increase in the amount of content available in digital form, text
classification becomes an urgent need to manage the digital data efficiently and accurately. Till
now no Punjabi Text Classifier is available for Punjabi Text Documents. Therefore, in this
paper, existing classification algorithm such as Naïve Bayes, Centroid Based techniques are
used for Punjabi Text Classification. And one new approach is proposed for the Punjabi Text
Documents which is the combination Naïve Bayes (to extract the relevant features so as to
reduce the dimensionality) and Ontology Based Classification (that act as text classifier that
used extracted features). These algorithms are performed over 184 Punjabi News Articles on
Sports that classify the documents into 7 classes such as ਿਕਕਟ (krikaṭ), ਹਾਕੀ (hākī), ਕਬੱ ਡੀ
(kabḍḍī), ਫੁਟਬਾਲ (phuṭbāl), ਟੈਿਨਸ (ṭainis), ਬੈਡਿਮੰ ਟਨ (baiḍmiṇṭan), ਓਲੰ ਿਪਕ (ōlmpik).
A Review on a web based Punjabi t o English Machine Transliteration System
This document summarizes a research paper on developing a Punjabi to English machine transliteration system using statistical machine translation. It discusses how existing transliteration systems between other languages use rule-based or hybrid approaches and have accuracies ranging from 73% to 95%. The proposed system aims to increase accuracy by using statistical machine translation techniques to learn from existing transliterated data and select the most probable transliteration when multiple options exist. It will help translate documents in the Punjabi language, which is official in Punjab, into English for international understanding.
Recommended
RULE BASED TRANSLITERATION SCHEME FOR ENGLISH TO PUNJABI
Machine Transliteration has come out to be an emerging and a very important research area in the field of
machine translation. Transliteration basically aims to preserve the phonological structure of words. Proper
transliteration of name entities plays a very significant role in improving the quality of machine translation.
In this paper we are doing machine transliteration for English-Punjabi language pair using rule based
approach. We have constructed some rules for syllabification. Syllabification is the process to extract or
separate the syllable from the words. In this we are calculating the probabilities for name entities (Proper
names and location). For those words which do not come under the category of name entities, separate
probabilities are being calculated by using relative frequency through a statistical machine translation
toolkit known as MOSES. Using these probabilities we are transliterating our input text from English to
Punjabi.
Brill's Rule-based Part of Speech Tagger for Kadazan
This paper presents the Part of Speech Tagger (POS) for Kadazan language by
implementing Brill's approach which is also known as a Transformation-Based Error
Driven Learning approach. Kadazan language is chosen because there is not even one POS
tagger has been developed for this language yet. Hence, this study has been carried out in
order to develop a POS tagger especially for Kadazan language that can tag Kadazan
corpus systematically, help to reduce the ambiguity problem and at the same time can be
used as a learning language tool. Therefore, the main objective of this study is to automate
the tagging process for Kadazan language. Brill' approach is an enhance version of the
original Rule-Based approach which it transforms the tags based on a set of predefined
rules. Brill’s approach uses rules to transform wrong tags into correct tags in the corpus. In
order to achieve the main goal, several objectives have been set which are to create the
specific lexical and contextual rules for Kadazan language, by applying Brill’s approach
based on rules and to evaluate the effectiveness of Kadazan Part of Speech using Brill’s
approach. The tagging process is divided into four main phases. In first phase, Brill’s
approach process begins by inputting a new untagged text into the system. In second phase,
the input text will go through the initial state annotater to tag all the words inside the corpus
to its most likely tags and produce a temporary corpus. In third phase, the temporary
corpus is then compared to the goal corpus to detect if there is any errors occurred. In last
phase, the rules will be applied to reduce any errors occurred and fix the temporary corpus.
The tagging approach has been trained using two Kadazan children’s story books which
contain 2069 words. Evaluation process is done by comparing the tagging results of Brill’s
approach with the manual tagging. Kadazan Part of Speech Tagger has achieved around 93
% of accuracy. This study has shown how Brill’s tagging approach can be used to identify
tags for Kadazan language.
Aruna Kumar_Resume
This document contains a resume for Aruna Kumar K R, a linguist professional with over 7 years of experience in Kannada translation, proofreading, and editing. He has a master's degree in English literature and postgraduate diploma in translation. He has worked as a lead external linguist for Kannada on various projects for Google, providing translation from English to Kannada and vice versa, linguistic review, and language quality assurance. His skills include translation tools like Idiom Desktop Workbench and technical understanding of translation workflows.
ON THE UTILITY OF A SYLLABLE-LIKE SEGMENTATION FOR LEARNING A TRANSLITERATION...
Source and target word segmentation and alignment is a primary step in the statistical learning of a Transliteration. Here, we analyze the benefit of a syllable-like segmentation approach for learning a transliteration from English to an Indic language, which aligns the training set word pairs in terms of sub-syllable-like units instead of individual character units. While this has been found useful in the case of dealing with Out-of-vocabulary words in English-Chinese in the presence of multiple target dialects, we asked if this would be true for Indic languages which are simpler in their phonetic representation and pronunciation. We expected this syllable-like method to perform marginally better, but we found instead that even though our proposed approach improved the Top-1 accuracy, the individual-character-unit alignment model
somewhat outperformed our approach when the Top-10 results of the system were re-ranked using language modeling approaches. Our experiments were conducted for English to Telugu transliteration (our method will apply equally well to most written Indic languages); our training consisted of a syllable-like segmentation and alignment of a large training set, on which we built a statistical model by modifying a previous character-level maximum entropy based Transliteration learning system due to Kumaran and Kellner; our testing consisted of using the same segmentation of a test English word, followed by applying the model, and reranking the resulting top 10 Telugu words. We also report the dataset creation and selection since standard datasets are not available.
DISCRIMINATION OF ENGLISH TO OTHER INDIAN LANGUAGES (KANNADA AND HINDI) FOR O...
India is a multilingual multi-script country. In every state of India there are two languages one is state local language and the other is English. For example in Andhra Pradesh, a state in India, the document may contain text words in English and Telugu script. For Optical Character Recognition (OCR) of such a bilingual document, it is necessary to identify the script before feeding the text words to the OCRs of individual scripts. In this paper, we are introducing a simple and efficient technique of script identification for Kannada, English and Hindi text words of a printed document. The proposed approach is based on the horizontal and vertical projection profile for the discrimination of the three scripts. The feature extraction is done based on the horizontal projection profile of each text words. We analysed 700 different words of Kannada, English and Hindi in order to extract the discrimination features and for the development of knowledge base. We use the horizontal projection profile of each text word and based on the horizontal projection profile we extract the appropriate features. The proposed system is tested on 100 differentdocument images containing more than 1000 text words of each script and a classification rate of 98.25%, 99.25% and 98.87% is achieved for Kannada, English and Hindi respectively.
Comparison Analysis of Post- Processing Method for Punjabi Font
The document describes a study that compares different post-processing methods for recognizing handwritten Punjabi (Gurmukhi) text from optical character recognition (OCR) systems. The study proposes using four phases: 1) an AVL tree implementation method to find suggestions, 2) a symmetric delete spelling correction algorithm, 3) taking the union of the results from the first two methods, and 4) ranking the suggestions based on a soundex approach. The goal is to correct errors and improve recognition rates for OCR of handwritten Gurmukhi text by using these post-processing techniques. The methods are tested on a database of over 115,000 Gurmukhi words and the results aim to reduce recognition errors and
Punjabi Text Classification using Naïve Bayes, Centroid and Hybrid Approach
Punjabi Text Classification is the process of assigning predefined classes to the unlabelled text
documents. Because of dramatic increase in the amount of content available in digital form, text
classification becomes an urgent need to manage the digital data efficiently and accurately. Till
now no Punjabi Text Classifier is available for Punjabi Text Documents. Therefore, in this
paper, existing classification algorithm such as Naïve Bayes, Centroid Based techniques are
used for Punjabi Text Classification. And one new approach is proposed for the Punjabi Text
Documents which is the combination Naïve Bayes (to extract the relevant features so as to
reduce the dimensionality) and Ontology Based Classification (that act as text classifier that
used extracted features). These algorithms are performed over 184 Punjabi News Articles on
Sports that classify the documents into 7 classes such as ਿਕਕਟ (krikaṭ), ਹਾਕੀ (hākī), ਕਬੱ ਡੀ
(kabḍḍī), ਫੁਟਬਾਲ (phuṭbāl), ਟੈਿਨਸ (ṭainis), ਬੈਡਿਮੰ ਟਨ (baiḍmiṇṭan), ਓਲੰ ਿਪਕ (ōlmpik).
A Review on a web based Punjabi t o English Machine Transliteration System
This document summarizes a research paper on developing a Punjabi to English machine transliteration system using statistical machine translation. It discusses how existing transliteration systems between other languages use rule-based or hybrid approaches and have accuracies ranging from 73% to 95%. The proposed system aims to increase accuracy by using statistical machine translation techniques to learn from existing transliterated data and select the most probable transliteration when multiple options exist. It will help translate documents in the Punjabi language, which is official in Punjab, into English for international understanding.
Dynamic Construction of Telugu Speech Corpus for Voice Enabled Text Editor
In recent decades speech interactive systems have gained increasing importance. Performance of an ASR system mainly depends on the availability of large corpus of speech. The conventional method of building a large vocabulary speech recognizer for any language uses a top-down approach to speech. This approach requires large speech corpus with sentence or phoneme level transcription of the speech utterances. The transcriptions must also include different speech order so that the recognizer can build models for all the sounds present. But, for Telugu language, because of its complex nature, a very large, well annotated speech database is very difficult to build. It is very difficult, if not impossible, to cover all the words of any Indian language, where each word may have thousands and millions of word forms. A significant part of grammar that is handled by syntax in English (and other similar languages) is handled within morphology in Telugu. Phrases including several words (that is, tokens) in English would be mapped on to a single word in Telugu.Telugu language is phonetic in nature in addition to rich in morphology. That is why the speech technology developed for English cannot be applied to Telugu language. This paper highlights the work carried out in an attempt to build a voice enabled text editor with capability of automatic term suggestion. Main claim of the paper is the recognition enhancement process developed by us for suitability of highly inflecting, rich morphological languages. This method results in increased speech recognition accuracy with very much reduction in corpus size. It also adapts Telugu words to the database dynamically, resulting in growth of the corpus.
IRJET -Survey on Named Entity Recognition using Syntactic Parsing for Hindi L...
This document summarizes research on named entity recognition for the Hindi language. It discusses various techniques that have been used for NER in Hindi, including rule-based approaches, machine learning approaches like hidden Markov models and conditional random fields, and hybrid approaches. The document also reviews several papers on Hindi NER systems that have used techniques like rule-based methods, list lookup, hidden Markov models, joint parsing with POS tagging, and distant supervision. Most studies found that machine learning approaches like hidden Markov models and conditional random fields produced relatively high accuracy, though performance could likely be improved further with larger annotated corpora.
Aruna kumar K R resume
The document provides a resume for Aruna Kumar K R, who has over 5 years of experience in Kannada translation, proofreading, editing, and writing. He has a master's degree in English literature and postgraduate diploma in translation. He has experience working as a freelance translator for companies like Xiaomi and Google, providing translation from English to Kannada and vice versa, linguistic reviews, and language quality assurance. He is proficient with translation tools like Idiom Desktop Workbench and has strong time management, problem solving, and communication skills.
Ijetcas14 575
This document describes a study that developed a generic transliteration tool to transliterate words from English to Hindi for cross-lingual information access applications. It discusses the challenges of transliteration from English to Hindi due to differences in phonetic properties between the two languages. The study then evaluates the performance of four existing transliteration editor tools - Xlit, GIST, Google Transliteration, and Microsoft Hindi writing tool - on test data including English words, human names, and other terms. Test results show variations in the transliterated output for the same input across the different tools.
Implementation of Enhanced Parts-of-Speech Based Rules for English to Telugu ...
Words of a sentence will not follow same ordering in different languages. This paper proposes certain Parts-of-Speech (POS) based rules for reordering the given English sentence to get translation in Telugu. The added rules for adverbs, exceptional conjunctions in addition to improved handling of inflections enable the system to achieve more accurate translation. The proposed rules along with existing system gave a score of 0.6190 with BLEU evaluation metric while translating sentences from English to Telugu. This paper deals with simple form of sentences in a better way.
Ijnlc020306NAMED ENTITY RECOGNITION IN NATURAL LANGUAGES USING TRANSLITERATION
Transliteration may be defined as the process of mapping sounds in a text written in one language to
another language. Current paper discusses about transliteration and its use in Named Entity Recognition.
We have designed a code that executes Transliteration and assist in the process of Named Entity
Recognition. We have presented some of the results of Named Entity Recognition (NER) using
Transliteration
Development of morphological analyzer for hindi
This document presents the development of a morphological analyzer for the Hindi language. It discusses the importance of morphological analysis in natural language processing. Hindi is described as a morphologically rich, inflected language capable of generating hundreds of words from root words. The proposed analyzer will take in single Hindi words or sentences and return the root word along with features like part of speech, gender, number, and person. It will use both a rule-based approach and corpus-based approach, as previous analyzers have not worked for both inflectional and derivational morphemes. The document outlines the structure of Hindi words and sentences to provide context for developing the analyzer.
Ijartes v1-i1-002
Abstract
Part of speech tagging plays an important role in developing natural language processing software. Part of speech tagging means assigning part of speech tag to each word of the sentence. The part of speech tagger takes a sentence as input and it assigns respective/appropriate part of speech tag to each word of that sentence. In this article I surveys the different work have done about odia POS tagging.
________________________________________________
NERHMM: A TOOL FOR NAMED ENTITY RECOGNITION BASED ON HIDDEN MARKOV MODEL
This document describes a tool called NERHMM for performing named entity recognition using hidden Markov models. The tool allows users to annotate raw text to create tagged corpora, train hidden Markov models on annotated data to calculate parameters, and test new text data to produce named entity tags. The tool works for multiple languages and can handle diverse tag sets. It provides a simple interface for tasks involved in named entity recognition like corpus development and parameter estimation for hidden Markov models. Evaluation on data shows the tool's performance increases with more training data.
Language Identification from a Tri-lingual Printed Document: A Simple Approach
In a multi-script, multilingual country like India, a document may contain text lines in more than one script/language forms. For such a multi-script environment, multilingual Optical Character Recognition (OCR) system is needed to read the multi-script documents. To make a multilingual OCR system successful, it is necessary to identify different script regions of the document before feeding the document to the OCRs of individual language. With this context, this paper proposes to work on the prioritized requirements of a particular region- Andra Pradesh, a state in India, where any document including official ones, would contain the text in three languages-Telugu, Hindi and English. So, the objective of this paper is to develop a system that should aim to accurately identify and separate Telugu, Hindi and English text lines from a printed multilingual document and also to group the portion of the document in other than these three languages into a separate category OTHERS. The proposed method is developed by thoroughly understanding the nature of top and bottom profiles of the printed text lines. Experimentation conducted involved 900 text lines for learning and 900 text lines for testing. The performance has turned out to be 95.67%.
HMM BASED POS TAGGER FOR HINDI
Part of Speech tagging in Indian Languages is still an open problem. We still lack a clear approach in implementing a POS tagger for Indian Languages. In this paper we describe our efforts to build a Hidden Markov Model based Part of Speech Tagger. We have used IL POS tag set for the development of this tagger. We have achieved the accuracy of 92%
HANDLING UNKNOWN WORDS IN NAMED ENTITY RECOGNITION USING TRANSLITERATION
The document discusses handling unknown words in named entity recognition using transliteration. It proposes an approach where named entities in training data are transliterated into other languages and stored in transliteration files. During testing, if an unknown entity is encountered, it is checked against the transliteration files and assigned the corresponding tag if found. The approach is shown to achieve 95.8% recall, 96.3% precision and 96.04% F-measure on a multilingual named entity recognition task handling words from English, Hindi, Marathi, Punjabi and Urdu. Performance metrics for named entity recognition systems such as precision, recall and F-measure are also discussed.
A Tool to Search and Convert Reduplicate Words from Hindi to Punjabi
This document discusses reduplication in Hindi and proposes developing a tool to identify and translate reduplicated words from Hindi to Punjabi. It begins by defining reduplication and describing different types, including morphological, lexical, echo formation, compounds, word and discontinuous reduplication. It then states the problem of automatically identifying echo words in Hindi conversations to translate to Punjabi. Next, it discusses using rule-based and statistical approaches and choosing rule-based. Finally, it outlines the work to be done, including studying reduplication, past research, translating echo words, and rule-based approaches.
An Improved Approach for Word Ambiguity Removal
Word ambiguity removal is a task of removing ambiguity from a word, i.e. correct sense of word is identified from ambiguous sentences. This paper describes a model that uses Part of Speech tagger and three categories for word sense disambiguation (WSD). Human Computer Interaction is very needful to improve interactions between users and computers. For this, the Supervised and Unsupervised methods are combined. The WSD algorithm is used to find the efficient and accurate sense of a word based on domain information. The accuracy of this work is evaluated with the aim of finding best suitable domain of word. Keywords: Human Computer Interaction, Supervised Training, Unsupervised Learning, Word Ambiguity, Word sense disambiguation
Anaphora resolution in hindi language using gazetteer method
Anaphora resolution is one of the active research areas within the realm of natural language processing.
Resolution of anaphoric reference is one of the most challenging and complex task to be handled. This
paper completely emphasis on pronominal anaphora resolution for Hindi Language. There are various
methodologies for resolving anaphora. This paper presents a computational model for anaphora resolution
in Hindi that is based on Gazetteer method. Gazetteer method is a creation of lists and then applies
operations to classify elements present in the list. There are many salient factors for resolving anaphora.
The proposed model resolves anaphora by using two factors that is Animistic and Recency. Animistic factor
always represent living things and non living things whereas Recency describes that the referents
mentioned in current sentence tends to have higher weights than those in previous sentence. This paper
demonstrate the experiments conducted on short Hindi stories ,news articles and biography content from
Wikipedia, its result & future directions to improve accuracy.
IRJET- Tamil Speech to Indian Sign Language using CMUSphinx Language Models
The document describes a proposed system to translate Tamil speech to Indian Sign Language (ISL) using speech recognition and natural language processing algorithms. It aims to help hearing-impaired people communicate independently. The system would use the CMU Sphinx speech recognition tool to convert spoken Tamil to text, then apply grammar rules and machine learning to translate the text to ISL displayed through video or animated avatars. The document reviews similar existing systems and research on speech recognition and sign language translation to inform the design and implementation of the proposed Tamil-ISL system.
Pronominal anaphora resolution in
Anaphora Resolution is a process of finding referents in discourse. In computational linguistic, Anaphora
resolution is complex and challenging task. This paper focuses on pronominal anaphora resolution. It is a
subpart of anaphora resolution where pronouns are referred to noun referents. Including anaphora
resolution into many applications like automatic summarization, opinion mining, machine translation,
question answering systems etc. increase their accuracy by 10%. Related work in this field has been done
in many languages. This paper focuses on resolving anaphora for Punjabi language. A model is proposed
for resolving anaphora and an experiment is conducted to measure the accuracy of the system. The model
uses two factors: Recency and Animistic knowledge. Recency factor works on the concept of Lappin Leass
approach and for introducing animistic knowledge gazetteer method is used. The experiment is conducted
on a Punjabi story containing more than 1000 words and result is drawn with the future directions.
Spell checker for Kannada OCR
A spell checker is an application program to
process the natural languages in machine readable format
effectively. Spelling checking and correction is a basic
necessity and a tedious work in any language, so we require
spell checker software to do this, which is the fundamental
necessity for any work. Spell checker is a set of program
which analyzes the wrongly used word and corrects it by the
most possible correct word. The challenging task here is the
work done for a Kannada language. In a software system
many Kannada words are typed in several formats since
Kannada has many fonts to write the grammar properly.
In this paper, we describe some techniques used in
Kannada language by a spell checker. We use NLP, which is
a field of computer science having relationship between
human (i.e., natural languages) and computers. Usually, we
have some modern NLP algorithms based on machine
learning to carry out the work.
MORPHOLOGICAL ANALYZER USING THE BILSTM MODEL ONLY FOR JAPANESE HIRAGANA SENT...
This study proposes a method to develop neural models of the morphological analyzer for Japanese Hiragana sentences using the Bi-LSTM CRF model. Morphological analysis is a technique that divides text data into words and assigns information such as parts of speech. In Japanese natural language processing systems, this technique plays an essential role in downstream applications because the Japanese language does not have word delimiters between words. Hiragana is a type of Japanese phonogramic characters, which is used for texts for children or people who cannot read Chinese characters. Morphological analysis of Hiragana sentences is more difficult than that of ordinary Japanese sentences because there is less information for dividing. For morphological analysis of Hiragana sentences, we demonstrated the effectiveness of fine-tuning using a model based on ordinary Japanese text and examined the influence of training data on texts of various genres.
EXTRACTING LINGUISTIC SPEECH PATTERNS OF JAPANESE FICTIONAL CHARACTERS USING ...
This study extracted and analyzed the linguistic speech patterns that characterize Japanese anime or game characters. Conventional morphological analyzers, such as MeCab, segment words with high performance, but they are unable to segment broken expressions or utterance endings that are not listed in the dictionary, which often appears in lines of anime or game characters. To overcome this challenge, we propose segmenting lines of Japanese anime or game characters using subword units that were proposed mainly for deep learning, and extracting frequently occurring strings to obtain expressions that characterize their utterances. We analyzed the subword units weighted by TF/IDF according to gender, age, and each anime character and show that they are linguistic speech patterns that are specific for each feature. Additionally, a classification experiment shows that the model with subword units outperformed that with the conventional method.
Rule Based Transliteration Scheme for English to Punjabi
Machine Transliteration has come out to be an emerging and a very important research area in the field of
machine translation. Transliteration basically aims to preserve the phonological structure of words. Proper
transliteration of name entities plays a very significant role in improving the quality of machine translation.
In this paper we are doing machine transliteration for English-Punjabi language pair using rule based
approach. We have constructed some rules for syllabification. Syllabification is the process to extract or
separate the syllable from the words. In this we are calculating the probabilities for name entities (Proper
names and location). For those words which do not come under the category of name entities, separate
probabilities are being calculated by using relative frequency through a statistical machine translation
toolkit known as MOSES. Using these probabilities we are transliterating our input text from English to
Punjabi.
Towards Building Semantic Role Labeler for Indian Languages
Author Credits - Maaz Anwar Nomani
Semantic Role Labeler (SRL) is a semantic parser which can automatically identify and then classify arguments of a verb in a natural language sentence for Hindi and Urdu. For e.g. in the natural language sentence “Sara won the competition because of her hard work.”, ‘won’ is the main verb and there are 3 arguments for this verb; ‘Sara’ (Agent), ‘hard work’ (Reason) and ‘competition’ (Theme). The problem statement of a SRL revolves around the fact that how will you make a machine identify and then classify the arguments of a verb in a natural language sentence.
Since there are 2 sub problem statements here (Identification and Classification), our SRL has a pipeline architecture in which a binary classifier (Logistic Regression) is first trained to identify whether a word is an argument to a verb in a sentence or not (Yes or No) and subsequently a multi-class classifier (SVM with Linear kernel) is trained to classify the identified arguments by above binary classifier into one of the 20 classes. These 20 classes are the various notions present in a natural language sentence (for e.g. Agent, Theme, Location, Time, Purpose, Reason, Cause etc.). These ‘notions’ are called Propbank labels or semantic labels present in a Proposition Bank which is a collection of hand-annotated sentences.
In essence, SRL felicitates Semantic Parsing which essentially is the research investigation of identifying WHO did WHAT to WHOM, WHERE, HOW, WHY and WHEN etc. in a natural language sentence.
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Ijartes v1-i1-002
Abstract
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Spell checker for Kannada OCR
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Rule Based Transliteration Scheme for English to Punjabi
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Towards Building Semantic Role Labeler for Indian Languages
Author Credits - Maaz Anwar Nomani
Semantic Role Labeler (SRL) is a semantic parser which can automatically identify and then classify arguments of a verb in a natural language sentence for Hindi and Urdu. For e.g. in the natural language sentence “Sara won the competition because of her hard work.”, ‘won’ is the main verb and there are 3 arguments for this verb; ‘Sara’ (Agent), ‘hard work’ (Reason) and ‘competition’ (Theme). The problem statement of a SRL revolves around the fact that how will you make a machine identify and then classify the arguments of a verb in a natural language sentence.
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In essence, SRL felicitates Semantic Parsing which essentially is the research investigation of identifying WHO did WHAT to WHOM, WHERE, HOW, WHY and WHEN etc. in a natural language sentence.
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The process of identification of Named Entities (NEs) in a given document and then there classification into different categories of NEs is referred to as Named Entity Recognition (NER). We need to do a great effort in order to perform NER in Indian languages and achieve the same or higher accuracy as that obtained by English and the European languages. In this paper, we have presented the results that we have achieved by performing NER in Hindi, Bengali and Telugu using Hidden Markov Model (HMM) and Performance Metrics.
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The document describes a machine learning approach for language identification, named entity recognition, and transliteration on query words. It discusses:
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2) Applying an IOB tagging scheme and features like character n-grams, context words, and typographic properties for named entity recognition and classification.
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Named Entity (NE) recognition is a task in which proper nouns and numerical information are extracted from documents and are classified into predefined categories such as Person names, Organization names , Location names, miscellaneous(Date and others). It is a key technology of Information Extraction, Question Answering system, Machine Translations, Information Retrial etc. This paper reports about the development of a NER system for Telugu using Conditional Random field (CRF). Though this state of the art machine learning technique has been widely applied to NER in several well-studied languages, the use of this technique to Telugu languages is very new. The system makes use of the different contextual information of the words along with the variety of features that are helpful in predicting the four different named entities (NE) classes, such as Person name, Location name, Organization name, miscellaneous (Date and others). Keywords: Named entity, Conditional Random field, NE, CRF, NER, named entity recognition
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3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
一比一原版（CSULB毕业证）加州州立大学长滩分校毕业证如何办理
原件一模一样【微信：176555708】【加州州立大学长滩分校毕业证（CSULB学位证）成绩单】【微信：176555708】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信：176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信：176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份【微信：176555708】
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
办理加州州立大学长滩分校毕业证（CSULB学位证）学位证【微信：176555708 】外观非常精致，由特殊纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理加州州立大学长滩分校毕业证（CSULB学位证）学位证【微信：176555708 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理加州州立大学长滩分校毕业证（CSULB学位证）学位证【微信：176555708 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理加州州立大学长滩分校毕业证（CSULB学位证）学位证【微信：176555708 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
一比一原版（UCSD毕业证）加利福尼亚大学圣迭戈分校毕业证如何办理
原件一模一样【微信：176555708】【加利福尼亚大学圣迭戈分校毕业证（UCSD学位证）成绩单】【微信：176555708】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信：176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信：176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份【微信：176555708】
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
办理加利福尼亚大学圣迭戈分校毕业证（UCSD学位证）学位证【微信：176555708 】外观非常精致，由特殊纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理加利福尼亚大学圣迭戈分校毕业证（UCSD学位证）学位证【微信：176555708 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理加利福尼亚大学圣迭戈分校毕业证（UCSD学位证）学位证【微信：176555708 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理加利福尼亚大学圣迭戈分校毕业证（UCSD学位证）学位证【微信：176555708 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
On Storytelling & Magic Realism in Rushdie’s Midnight’s Children, Shame, and ...
ABSTRACT: Salman Rushdie’s novels are humorous books about serious times. His cosmopolitanism and
hybrid identity allowed him access to multiple cultures, religions, languages, dialects, and various modes of
writing. His style is often classified as magic realism, blending the imaginary with the real. He draws
inspiration from both English literature and Indian classical sources. Throughout his works, there is a lineage of
‘bastards of history’, a carnival of shameful characters scrolling all along his works. Rushdie intertwines fiction
with reality, incorporating intertextual references to Western literature in his texts, and frequently employing
mythology to explore history. This paper focuses on Rushdie’s three novels: Midnight’s Children, Shame, and
Haroun and the Sea of Stories, analyzing his postmodern storytelling techniques that aim to explore human
vices and follies while offering socio-political criticism.
KEYWORDS : Magic Realism, Rushdie, Satire, Storytelling, Transfictional Identities
一比一原版(AU毕业证)英国阿伯丁大学毕业证如何办理
AU毕业证文凭证书【微信95270640】《英国阿伯丁大学毕业证书原版制作AU成绩单》【Q微信95270640】《仿制AU毕业证成绩单英国阿伯丁大学学位证书pdf电子图》，A.为什么留学生需要操作留信认证?
留信认证全称全国留学生信息服务网认证,隶属于北京中科院。①留信认证门槛条件更低,费用更美丽,并且包过,完单周期短,效率高②留信认证虽然不能去国企,但是一般的公司都没有问题,因为国内很多公司连基本的留学生学历认证都不了解。这对于留学生来说,这就比自己光拿一个证书更有说服力,因为留学学历可以在留信网站上进行查询!
B.为什么我们提供的毕业证成绩单具有使用价值？
查询留服认证是国内鉴别留学生海外学历的唯一途径，但认证只是个体行为，不是所有留学生都操作，所以没有办理认证的留学生的学历在国内也是查询不到的，他们也仅仅只有一张文凭。所以这时候我们提供的和学校颁发的一模一样的毕业证成绩单，就有了使用价值。
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文凭办理流程：
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4毕业证成绩单电子版您确认信息无误之后安排制作成品；
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6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
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五：与学校颁发的相关证件1:1纸质尺寸制定（定期向各大院校毕业生购买最新版本毕,业证成绩单保证您拿到的是鲁昂大学内部最新版本毕业证成绩单微信95270640）
A.为什么留学生需要操作留信认证?
留信认证全称全国留学生信息服务网认证,隶属于北京中科院。①留信认证门槛条件更低,费用更美丽,并且包过,完单周期短,效率高②留信认证虽然不能去国企,但是一般的公司都没有问题,因为国内很多公司连基本的留学生学历认证都不了解。这对于留学生来说,这就比自己光拿一个证书更有说服力,因为留学学历可以在留信网站上进行查询!
B.为什么我们提供的毕业证成绩单具有使用价值？
查询留服认证是国内鉴别留学生海外学历的唯一途径但认证只是个体行为不是所有留学生都操作所以没有办理认证的留学生的学历在国内也是查询不到的他们也仅仅只有一张文凭。所以这时候我们提供的和学校颁发的一模一样的毕业证成绩单就有了使用价值。却也灵机一动立马仰起头双手拢在嘴边朝楼上大喊：“爸爸爸——有人找——那人一听朝山娃尴尬地笑笑悻悻地走了山娃立马“嘭的一声将铁门锁死心却咚咚地乱跳当山娃跟父亲说起这事时父亲很吃惊抚摸着山娃的头说还好醒得及时要不家早被人掏空了到时连电视也没得看啰不过父亲还是夸山娃能临危不乱随机应变有胆有谋山娃笑笑说那都是书上学的看童话和小说时学的不管如何父亲再也不许山娃敞开门坐到门口了父亲递给山娃钥匙说一出门就将门山
Using Playlists to Increase YouTube Watch Time
Discover how to use playlists to keep viewers engaged and increase your watch time.
https://www.sociocosmos.com/product-category/youtube/youtube-comments/
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Query Word Labeling and Back Transliteration for Indian Languages
- 1. Query Word Labeling and Back Transliteration for Indian Languages: MSRI Shared task system description Spandana Gella1,2 , Jatin Sharma1 , Kalika Bali1 1 Microsoft Research, India 2 University of Melbourne, Australia December 9, 2013 Authors: Spandana Gella, Jatin Sharma, Kalika Bali Query Word Labeling and Back Transliteration for Indian Languages
- 2. SubTask1: Query Word Labeling Many Indian languages esp. in social media is written using romanized script Table: Shared Task description in two separate steps of query labeling and back transliteration Authors: Spandana Gella, Jatin Sharma, Kalika Bali Query Word Labeling and Back Transliteration for Indian Languages
- 3. Our Methodology Word level language identification based on character n-gram features learned from wordlists extracted from monolingual corpus (King and "Abney, 2013) Adding context switch probability to indirectly learn the language sequence patterns Frequency based filtering Back-Transliteration Hash based mapping between source and target languages (Kumar and Udupa, 2011) Use indic character mapping to create training data in poor-resource languages Authors: Spandana Gella, Jatin Sharma, Kalika Bali Query Word Labeling and Back Transliteration for Indian Languages
- 4. Terminology, Datasets and Tools Character n-gram features: hello :’h’,’e’,..,’o’,’he’,el’..,’hel’..,’hell’,’ello’,’hello’ Training resources: Word lists (from Leipzig Corpus, Anandbazar Patrika), word frequencies and transliterated pairs given as part of shared task Training size from 100 - 5000 words (Always <=546 for Gujarati) (McCallum, 2002) for learning classifiers, MSRI Name Search Tool for Transliteration Authors: Spandana Gella, Jatin Sharma, Kalika Bali Query Word Labeling and Back Transliteration for Indian Languages
- 5. 100 200 500 1000 2000 3000 5000 1.00 accuracy % 0.80 0.85 0.90 0.95 1.00 0.95 0.90 accuracy % NaiveBayes Max−Ent DTree 0.75 0.75 0.80 0.85 0.90 0.85 0.75 0.80 accuracy % 0.95 1.00 Word label prediction based on n-gram features 100 (a) Hindi−English sampled size (a) Hindi 200 500 1000 2000 3000 (b) Gujarati−English sampled size (b) Gujarati 5000 100 200 500 1000 2000 3000 5000 (c) Bangla−English sampled size (c) Bangla Figure: Learning curves for maximum entropy, naive Bayes and decision tree on word labeling for Hindi, Gujarati and Bangla language on development data Authors: Spandana Gella, Jatin Sharma, Kalika Bali Query Word Labeling and Back Transliteration for Indian Languages
- 6. 100 200 500 1000 2000 3000 (a) Hindi - Maxent 5000 s, − < # * + s , < − − # * + # + * 100 200 500 1000 2000 3000 (b) Gujarati - Maxent 5000 , s , s < 0.87 < − s, # * + 0.86 , − < s # + * 0.85 s s , < , − < # s + * accuracy % 0.95 0.90 s * + < − # , s # − , < s + * 0.84 , s + − < # *, s accuracy % < + # < − *, s 0.85 0.92 0.90 0.88 + − # * < , + − * # − < # + *, s 0.86 accuracy % 0.94 # < − + *, 0.88 Adding context-switch probability s < , − s , < − , s * < # − , s , < − s + * # − < # + * < + # * − + # * + # * − # * + + 100 200 + 0.6 * 0.65 # 0.7 − 0.75 < 0.8 , 0.85 s 0.9 None 500 1000 2000 3000 5000 (c) Bangla - Naive Figure: Learning curves with varying context switch probabilities Authors: Spandana Gella, Jatin Sharma, Kalika Bali Query Word Labeling and Back Transliteration for Indian Languages
- 7. Language Identification Errors Type Romanized Predicted Reference Short Words Ambiguous Words Erroneous Words Mixed Numerals Words i; ve the; ate emosal zara2; duwan2 H; E E; E H E; E E; H H; H E H; H Table: Annotation Errors Authors: Spandana Gella, Jatin Sharma, Kalika Bali Query Word Labeling and Back Transliteration for Indian Languages
- 8. Back Transliteration MSRI Name Search Tool, built based on n-gram based feature hashing Used indic character mapping between Hindi-Bangla and Hindi-Gujarati All 3 systems for Gujarati and Bangla uses indic character mapping Authors: Spandana Gella, Jatin Sharma, Kalika Bali Query Word Labeling and Back Transliteration for Indian Languages
- 9. Test set Results System MSRI-1 MSRI-2 MSRI-3 Maximum Median LA 0.9823 0.9848 0.9826 0.9848 0.9540 Hindi TF 0.8127 0.8130 0.8101 0.8130 0.4160 TQM 0.1940 0.1980 0.1860 0.1980 0.0290 LA 0.9614 0.9755 0.9661 0.9755 0.9661 Gujarati TF TQM 0.4711 0.0800 0.4803 0.0733 0.4748 0.0667 0.4803 0.0800 0.4748 0.0733 LA 0.9259 0.9499 0.9459 0.9499 0.9359 Bangla TF 0.4914 0.5033 0.5137 0.5137 0.4973 TQM 0.0100 0.0100 0.0100 0.0100 0.0100 Table: Language labeling analysis on submitted runs in all three languages, along with maximum and median scores. Our runs which had maximum scores are presented in bold. LA - Labeling Accuracy, TF- Transliteration F-score, TQM - % of queries that had exact labeling and transliteration Authors: Spandana Gella, Jatin Sharma, Kalika Bali Query Word Labeling and Back Transliteration for Indian Languages
- 10. Transliteration Error Analysis Table: Transliteration Errors Authors: Spandana Gella, Jatin Sharma, Kalika Bali Query Word Labeling and Back Transliteration for Indian Languages
- 11. Summary Contributions: Using context switch probability increases the performance of language labeling in code-mixed language. Cross-language character mapping to increase transliteration accuracy promising direction for resource-poor languages Future Work: Extending it to text with spelling variations (covering text normalization) Working on multiple languages esp. poor resource languages by exploiting resources from related languages Authors: Spandana Gella, Jatin Sharma, Kalika Bali Query Word Labeling and Back Transliteration for Indian Languages
- 12. Questions? Authors: Spandana Gella, Jatin Sharma, Kalika Bali Query Word Labeling and Back Transliteration for Indian Languages
- 13. Bibliography I King, B. and "Abney, S. (2013). Labeling the languages of words in mixed-language documents using weakly supervised methods. In Proceedings of NAACL-HLT, pages 1110–1119. Kumar, S. and Udupa, R. (2011). Learning hash functions for cross-view similarity search. In Proceedings of the Twenty-Second international joint conference on Artificial Intelligence-Volume Volume Two, pages 1360–1365. AAAI Press. McCallum, A. K. (2002). Mallet: A machine learning for language toolkit. Authors: Spandana Gella, Jatin Sharma, Kalika Bali Query Word Labeling and Back Transliteration for Indian Languages