The PASCAL Visual Object Classes Challenge

In this paper we describe a novel technique for detecting salient regions in an image. The detector is a generalization to affine invariance of the method introduced by Kadir and Brady [10]. The detector deems a region salient if it exhibits unpredictability in both its attributes and its spatial scale.

An affine invariant salient region detector

Considerable progress has been made in recent years in the development of dialogue systems that support robust and efficient human–machine interaction using spoken language. Spoken dialogue technology allows various interactive applications to be built and used for practical purposes, and research focuses on issues that aim to increase the system’s communicative competence by including aspects of error correction, cooperation, multimodality, and adaptation in context. This book gives a comprehensive view of state-of-the-art techniques that are used to build spoken dialogue systems. It provides an overview of the basic issues such as system architectures, various dialogue management methods, system evaluation, and also surveys advanced topics concerning extensions of the basic model to more conversational setups. The goal of the book is to provide an introduction to the methods, problems, and solutions that are used in dialogue system development and evaluation. It presents dialogue modelling and system development issues relevant in both academic and industrial environments and also discusses requirements and challenges for advanced interaction management and future research. vi KEywoRDS Spoken dialogue systems, multimodality, evaluation, error-handling, dialogue management, statistical method v MC_Jok nen_FM. ndd Achorn Internat onal 10/10/2009 04:18AM

https://www.morganclaypool.com/doi/suppl/10.2200/S00509ED1V01Y201305HLT023/suppl_file/Dagan_Ch1.pdf

Synthesis Lectures on Human Language Technologies

Hope is considered significant for the well-being, recuperation and restoration of human life by health professionals. Hope speech reflects the belief that one can discover pathways to their desired objectives and become roused to utilise those pathways. To encourage research in natural language processing towards positive reinforcement approach, we created a hope speech detection dataset. This paper reports on the shared task of hope speech detection for Tamil, English, and Malayalam languages. The shared task was conducted as a part of the EACL 2021 workshop on Language Technology for Equality, Diversity, and Inclusion (LT-EDI-2021). We summarize here the datasets for this challenge which are openly available at https://competitions.codalab.org/competitions/27653, and present an overview of the methods and the results of the competing systems. To the best of our knowledge, this is the first shared task to conduct hope speech detection.

Findings of the Shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion

Sentiment Analysis is the process of identifying and categorising the sentiments expressed in a text into positive or negative. The words which carry the sentiments are the keys in sentiment prediction. The SentiWordNet is the sentiment lexicon used to determine the sentiment of texts. There are huge number of sentiment terms that are not in the SentiWordNet limit the performance of Sentiment Analysis. Gathering and grouping such sentiment words manually is a tedious task. In this paper we propose a sentiment lexicon expansion method using Word2vec and fastText word embeddings along with rule-based Sentiment Analysis method. We expand the sentiment lexicon from the initial seed list of 2951 positive and 5598 negative words in two steps: (i) Gathering related words using Word2vec word embedding and (ii) Gathering lexically similar words using fastText word embedding. Our final lexicons UJ_Lex_Pos and UJ_Lex_Neg ended up with 10537 positive and 12664 negative words respectively which are labelled using Word2vec word embedding. Furthermore the rule-based Sentiment Analysis method uses expanded lexicons (UJ_Lex_Pos and UJ_Lex_Neg), lists of conjunctions and negational words to predict the sentiments expressed in Tamil texts. The method is evaluated on UJ_MovieReviews and an accuracy of 88 0.14% is obtained.

Sentiment Lexicon Expansion using Word2vec and fastText for Sentiment Prediction in Tamil texts

Sentiment Analysis (SA) is an application of Natural Language Processing (NLP) to extract the sentiments expressed in the text. In this paper, we experimented five approaches to perform SA, namely, Lexicon based approach, Supervised Machine learning based approach, Hybrid approach, K-means with Bag of Word (BoW) approach and K-modes with BoW approach. We have experimented these approaches using five corpora with different feature representation techniques to predict the best approach to perform SA in Tamil texts. In this research we used Basic features such as word count and punctuation count in addition to traditional features such as Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) included to check their influence in the prediction. We have compared these approaches, features and the corpora. From the evaluation the highest accuracy of 79% is obtained for UJ_Corpus_Opinions_Nouns corpus with fastText for supervised Machine learning based approach.

Sentiment Analysis in Tamil Texts: A Study on Machine Learning Techniques and Feature Representation

This paper proposes a word embedding-based Part of Speech (POS) tagger for Tamil language The experiments are conducted with different word embeddings BoW, TF-IDF, Word2vec, fastText and GloVe that are created using UJ-Tamil corpus Different combinations of eight features with three classifiers linear SVM, Extreme Gradient Boosting and k-Nearest Neighbor are used to build the POS tagger The results are compared against Viterbi algorithm-based POS tagger The results show that word embedding can be used for POS tagging with good performance BoW, TF-IDF and fastText give an impressive performance compared with Word2vec and GloVe The accuracy of 99% is obtained with word embedding of BoW and TF-IDF with unigrams as well as bigrams and with linear SVM classifier POS tag of a given word can be identified with 99% of accuracy using word embeddings based POS tagger in Tamil

Word embedding-based Part of Speech tagging in Tamil texts

A spell checker is a tool to check the spelling of words in a document, validate them and in case of finding error in spelling, list out the possible words as suggestions. There are several researches available on spell checkers for European languages and Indian languages. But few for Tamil language, maybe, because of its nature as it is a highly inflectional and complex language. An efficient error detection and correction approach for spell checking in Tamil language has been proposed in this paper. A novel hybrid approach is adopted using tree-based algorithm with stemming and n-gram techniques. Testing results show that our system detects perfectively the error in spelling and provides most suitable suggestions for correcting the misspelt words with minimum of 91% accuracy.

A novel hybrid approach to detect and correct spelling in Tamil text

Spell checker is a tool that finds and corrects misspelt words in a text document. Spelling error detection and correction techniques are widely used by text editing systems, machine translation systems, optical character recognition systems, search engines and speech recognition systems. Though spell checkers for European languages and Indian languages are well developed, few for Tamil language, perhaps, because the fact that Tamil language is morphologically rich and agglutinative makes it a challenging task. An efficient approach to generating suggestions for misspelt words in Tamil language has been proposed in this paper. The proposed novel approach uses n-gram technique on stemmed form of the words with two different hash-tables and find the better one to generate most suitable alternatives to misspelt words by speeding up the lookup. The use of length of words in hash-table speed up finding appropriate suggestions while reducing the number of inappropriate suggestions. Test results show that the suggestions generated by the system are with 95% accuracy as approved by a Scholar in Tamil.

Sinnathamby Mahesan

Papers

Sentiment Analysis in Tamil Texts: A Study on Machine Learning Techniques and Feature Representation

Sentiment Lexicon Expansion using Word2vec and fastText for Sentiment Prediction in Tamil texts

Word embedding-based Part of Speech tagging in Tamil texts

A novel hybrid approach to detect and correct spelling in Tamil text

Use of a novel hash-table for speeding-up suggestions for misspelt Tamil words