A survey on sentiment analysis in Urdu: A resource-poor language

© 2020 Background/introduction: The dawn of the internet opened the doors to the easy and widespread sharing of information on subject matters such as products, services, events and political opinions. While the volume of studies conducted on sentiment analysis is rapidly expanding, these studies mostly address English language concerns. The primary goal of this study is to present state-of-art survey for identifying the progress and shortcomings saddling Urdu sentiment analysis and propose rectifications. Methods: We described the advancements made thus far in this area by categorising the studies along three dimensions, namely: text pre-processing lexical resources and sentiment classification. These pre-processing operations include word segmentation, text cleaning, spell checking and part-of-speech tagging. An evaluation of sophisticated lexical resources including corpuses and lexicons was carried out, and investigations were conducted on sentiment analysis constructs such as opinion words, modifiers, negations. Results and conclusions: Performance is reported for each of the reviewed study. Based on experimental results and proposals forwarded through this paper provides the groundwork for further studies on Urdu sentiment analysis

Knowledge Expansion of a Statistical Machine Translation System using Morphological Resources

Translation capability of a Phrase-Based Statistical Machine Translation (PBSMT) system mostly depends on parallel data and phrases that are not present in the training data are not correctly translated. This paper describes a method that efficiently expands the existing knowledge of a PBSMT system without adding more parallel data but using external morphological resources. A set of new phrase associations is added to translation and reordering models; each of them corresponds to a morphological variation of the source/target/both phrases of an existing association. New associations are generated using a string similarity score based on morphosyntactic information. We tested our approach on En-Fr and Fr-En translations and results showed improvements of the performance in terms of automatic scores (BLEU and Meteor) and reduction of out-of-vocabulary (OOV) words. We believe that our knowledge expansion framework is generic and could be used to add different types of information to the model.JRC.G.2-Global security and crisis managemen

Participative Urban Health and Healthy Aging in the Age of AI

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2022, held in Paris, France, in June 2022. The 15 full papers and 10 short papers presented in this volume were carefully reviewed and selected from 33 submissions. They cover topics such as design, development, deployment, and evaluation of AI for health, smart urban environments, assistive technologies, chronic disease management, and coaching and health telematics systems

Workshop Proceedings of the 12th edition of the KONVENS conference

The 2014 issue of KONVENS is even more a forum for exchange: its main topic is the interaction between Computational Linguistics and Information Science, and the synergies such interaction, cooperation and integrated views can produce. This topic at the crossroads of different research traditions which deal with natural language as a container of knowledge, and with methods to extract and manage knowledge that is linguistically represented is close to the heart of many researchers at the Institut für Informationswissenschaft und Sprachtechnologie of Universität Hildesheim: it has long been one of the institute’s research topics, and it has received even more attention over the last few years

Multilingual fine-grained sentiment text classification of web content for advertising technology applications

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Proceedings of the Conference on Natural Language Processing 2010

This book contains state-of-the-art contributions to the 10th conference on Natural Language Processing, KONVENS 2010 (Konferenz zur Verarbeitung natürlicher Sprache), with a focus on semantic processing. The KONVENS in general aims at offering a broad perspective on current research and developments within the interdisciplinary field of natural language processing. The central theme draws specific attention towards addressing linguistic aspects ofmeaning, covering deep as well as shallow approaches to semantic processing. The contributions address both knowledgebased and data-driven methods for modelling and acquiring semantic information, and discuss the role of semantic information in applications of language technology. The articles demonstrate the importance of semantic processing, and present novel and creative approaches to natural language processing in general. Some contributions put their focus on developing and improving NLP systems for tasks like Named Entity Recognition or Word Sense Disambiguation, or focus on semantic knowledge acquisition and exploitation with respect to collaboratively built ressources, or harvesting semantic information in virtual games. Others are set within the context of real-world applications, such as Authoring Aids, Text Summarisation and Information Retrieval. The collection highlights the importance of semantic processing for different areas and applications in Natural Language Processing, and provides the reader with an overview of current research in this field

Cross-Lingual and Low-Resource Sentiment Analysis

Identifying sentiment in a low-resource language is essential for understanding opinions internationally and for responding to the urgent needs of locals affected by disaster incidents in different world regions. While tools and resources for recognizing sentiment in high-resource languages are plentiful, determining the most effective methods for achieving this task in a low-resource language which lacks annotated data is still an open research question. Most existing approaches for cross-lingual sentiment analysis to date have relied on high-resource machine translation systems, large amounts of parallel data, or resources only available for Indo-European languages. This work presents methods, resources, and strategies for identifying sentiment cross-lingually in a low-resource language. We introduce a cross-lingual sentiment model which can be trained on a high-resource language and applied directly to a low-resource language. The model offers the feature of lexicalizing the training data using a bilingual dictionary, but can perform well without any translation into the target language. Through an extensive experimental analysis, evaluated on 17 target languages, we show that the model performs well with bilingual word vectors pre-trained on an appropriate translation corpus. We compare in-genre and in-domain parallel corpora, out-of-domain parallel corpora, in-domain comparable corpora, and monolingual corpora, and show that a relatively small, in-domain parallel corpus works best as a transfer medium if it is available. We describe the conditions under which other resources and embedding generation methods are successful, and these include our strategies for leveraging in-domain comparable corpora for cross-lingual sentiment analysis. To enhance the ability of the cross-lingual model to identify sentiment in the target language, we present new feature representations for sentiment analysis that are incorporated in the cross-lingual model: bilingual sentiment embeddings that are used to create bilingual sentiment scores, and a method for updating the sentiment embeddings during training by lexicalization of the target language. This feature configuration works best for the largest number of target languages in both untargeted and targeted cross-lingual sentiment experiments. The cross-lingual model is studied further by evaluating the role of the source language, which has traditionally been assumed to be English. We build cross-lingual models using 15 source languages, including two non-European and non-Indo-European source languages: Arabic and Chinese. We show that language families play an important role in the performance of the model, as does the morphological complexity of the source language. In the last part of the work, we focus on sentiment analysis towards targets. We study Arabic as a representative morphologically complex language and develop models and morphological representation features for identifying entity targets and sentiment expressed towards them in Arabic open-domain text. Finally, we adapt our cross-lingual sentiment models for the detection of sentiment towards targets. Through cross-lingual experiments on Arabic and English, we demonstrate that our findings regarding resources, features, and language also hold true for the transfer of targeted sentiment

Exploring variabilities through factor analysis in automatic acoustic language recognition

La problématique traitée par la Reconnaissance de la Langue (LR) porte sur la définition découverte de la langue contenue dans un segment de parole. Cette thèse se base sur des paramètres acoustiques de courte durée, utilisés dans une approche d adaptation de mélanges de Gaussiennes (GMM-UBM). Le problème majeur de nombreuses applications du vaste domaine de la re- problème connaissance de formes consiste en la variabilité des données observées. Dans le contexte de la Reconnaissance de la Langue (LR), cette variabilité nuisible est due à des causes diverses, notamment les caractéristiques du locuteur, l évolution de la parole et de la voix, ainsi que les canaux d acquisition et de transmission. Dans le contexte de la reconnaissance du locuteur, l impact de la variabilité solution peut sensiblement être réduit par la technique d Analyse Factorielle (Joint Factor Analysis, JFA). Dans ce travail, nous introduisons ce paradigme à la Reconnaissance de la Langue. Le succès de la JFA repose sur plusieurs hypothèses. La première est que l information observée est décomposable en une partie universelle, une partie dépendante de la langue et une partie de variabilité, qui elle est indépendante de la langue. La deuxième hypothèse, plus technique, est que la variabilité nuisible se situe dans un sous-espace de faible dimension, qui est défini de manière globale.Dans ce travail, nous analysons le comportement de la JFA dans le contexte d un dispositif de LR du type GMM-UBM. Nous introduisons et analysons également sa combinaison avec des Machines à Vecteurs Support (SVM). Les premières publications sur la JFA regroupaient toute information qui est amélioration nuisible à la tâche (donc ladite variabilité) dans un seul composant. Celui-ci est supposé suivre une distribution Gaussienne. Cette approche permet de traiter les différentes sortes de variabilités d une manière unique. En pratique, nous observons que cette hypothèse n est pas toujours vérifiée. Nous avons, par exemple, le cas où les données peuvent être groupées de manière logique en deux sous-parties clairement distinctes, notamment en données de sources téléphoniques et d émissions radio. Dans ce cas-ci, nos recherches détaillées montrent un certain avantage à traiter les deux types de données par deux systèmes spécifiques et d élire comme score de sortie celui du système qui correspond à la catégorie source du segment testé. Afin de sélectionner le score de l un des systèmes, nous avons besoin d un analyses détecteur de canal source. Nous proposons ici différents nouveaux designs pour engendrées de tels détecteurs automatiques. Dans ce cadre, nous montrons que les facteurs de variabilité (du sous-espace) de la JFA peuvent être utilisés avec succès pour la détection de la source. Ceci ouvre la perspective intéressante de subdiviser les5données en catégories de canal source qui sont établies de manière automatique. En plus de pouvoir s adapter à des nouvelles conditions de source, cette propriété permettrait de pouvoir travailler avec des données d entraînement qui ne sont pas accompagnées d étiquettes sur le canal de source. L approche JFA permet une réduction de la mesure de coûts allant jusqu à généraux 72% relatives, comparé au système GMM-UBM de base. En utilisant des systèmes spécifiques à la source, suivis d un sélecteur de scores, nous obtenons une amélioration relative de 81%.Language Recognition is the problem of discovering the language of a spoken definitionutterance. This thesis achieves this goal by using short term acoustic information within a GMM-UBM approach.The main problem of many pattern recognition applications is the variability of problemthe observed data. In the context of Language Recognition (LR), this troublesomevariability is due to the speaker characteristics, speech evolution, acquisition and transmission channels.In the context of Speaker Recognition, the variability problem is solved by solutionthe Joint Factor Analysis (JFA) technique. Here, we introduce this paradigm toLanguage Recognition. The success of JFA relies on several assumptions: The globalJFA assumption is that the observed information can be decomposed into a universalglobal part, a language-dependent part and the language-independent variabilitypart. The second, more technical assumption consists in the unwanted variability part to be thought to live in a low-dimensional, globally defined subspace. In this work, we analyze how JFA behaves in the context of a GMM-UBM LR framework. We also introduce and analyze its combination with Support Vector Machines(SVMs).The first JFA publications put all unwanted information (hence the variability) improvemen tinto one and the same component, which is thought to follow a Gaussian distribution.This handles diverse kinds of variability in a unique manner. But in practice,we observe that this hypothesis is not always verified. We have for example thecase, where the data can be divided into two clearly separate subsets, namely datafrom telephony and from broadcast sources. In this case, our detailed investigations show that there is some benefit of handling the two kinds of data with two separatesystems and then to elect the output score of the system, which corresponds to the source of the testing utterance.For selecting the score of one or the other system, we need a channel source related analyses detector. We propose here different novel designs for such automatic detectors.In this framework, we show that JFA s variability factors (of the subspace) can beused with success for detecting the source. This opens the interesting perspectiveof partitioning the data into automatically determined channel source categories,avoiding the need of source-labeled training data, which is not always available.The JFA approach results in up to 72% relative cost reduction, compared to the overall resultsGMM-UBM baseline system. Using source specific systems followed by a scoreselector, we achieve 81% relative improvement.AVIGNON-Bib. numérique (840079901) / SudocSudocFranceF