Cross-view Embeddings for Information Retrieval

In this dissertation, we deal with the cross-view tasks related to information retrieval using embedding methods. We study existing methodologies and propose new methods to overcome their limitations. We formally introduce the concept of mixed-script IR, which deals with the challenges faced by an IR system when a language is written in different scripts because of various technological and sociological factors. Mixed-script terms are represented by a small and finite feature space comprised of character n-grams. We propose the cross-view autoencoder (CAE) to model such terms in an abstract space and CAE provides the state-of-the-art performance. We study a wide variety of models for cross-language information retrieval (CLIR) and propose a model based on compositional neural networks (XCNN) which overcomes the limitations of the existing methods and achieves the best results for many CLIR tasks such as ad-hoc retrieval, parallel sentence retrieval and cross-language plagiarism detection. We empirically test the proposed models for these tasks on publicly available datasets and present the results with analyses. In this dissertation, we also explore an effective method to incorporate contextual similarity for lexical selection in machine translation. Concretely, we investigate a feature based on context available in source sentence calculated using deep autoencoders. The proposed feature exhibits statistically significant improvements over the strong baselines for English-to-Spanish and English-to-Hindi translation tasks. Finally, we explore the the methods to evaluate the quality of autoencoder generated representations of text data and analyse its architectural properties. For this, we propose two metrics based on reconstruction capabilities of the autoencoders: structure preservation index (SPI) and similarity accumulation index (SAI). We also introduce a concept of critical bottleneck dimensionality (CBD) below which the structural information is lost and present analyses linking CBD and language perplexity.En esta disertación estudiamos problemas de vistas-múltiples relacionados con la recuperación de información utilizando técnicas de representación en espacios de baja dimensionalidad. Estudiamos las técnicas existentes y proponemos nuevas técnicas para solventar algunas de las limitaciones existentes. Presentamos formalmente el concepto de recuperación de información con escritura mixta, el cual trata las dificultades de los sistemas de recuperación de información cuando los textos contienen escrituras en distintos alfabetos debido a razones tecnológicas y socioculturales. Las palabras en escritura mixta son representadas en un espacio de características finito y reducido, compuesto por n-gramas de caracteres. Proponemos los auto-codificadores de vistas-múltiples (CAE, por sus siglas en inglés) para modelar dichas palabras en un espacio abstracto, y esta técnica produce resultados de vanguardia. En este sentido, estudiamos varios modelos para la recuperación de información entre lenguas diferentes (CLIR, por sus siglas en inglés) y proponemos un modelo basado en redes neuronales composicionales (XCNN, por sus siglas en inglés), el cual supera las limitaciones de los métodos existentes. El método de XCNN propuesto produce mejores resultados en diferentes tareas de CLIR tales como la recuperación de información ad-hoc, la identificación de oraciones equivalentes en lenguas distintas y la detección de plagio entre lenguas diferentes. Para tal efecto, realizamos pruebas experimentales para dichas tareas sobre conjuntos de datos disponibles públicamente, presentando los resultados y análisis correspondientes. En esta disertación, también exploramos un método eficiente para utilizar similitud semántica de contextos en el proceso de selección léxica en traducción automática. Específicamente, proponemos características extraídas de los contextos disponibles en las oraciones fuentes mediante el uso de auto-codificadores. El uso de las características propuestas demuestra mejoras estadísticamente significativas sobre sistemas de traducción robustos para las tareas de traducción entre inglés y español, e inglés e hindú. Finalmente, exploramos métodos para evaluar la calidad de las representaciones de datos de texto generadas por los auto-codificadores, a la vez que analizamos las propiedades de sus arquitecturas. Como resultado, proponemos dos nuevas métricas para cuantificar la calidad de las reconstrucciones generadas por los auto-codificadores: el índice de preservación de estructura (SPI, por sus siglas en inglés) y el índice de acumulación de similitud (SAI, por sus siglas en inglés). También presentamos el concepto de dimensión crítica de cuello de botella (CBD, por sus siglas en inglés), por debajo de la cual la información estructural se deteriora. Mostramos que, interesantemente, la CBD está relacionada con la perplejidad de la lengua.En aquesta dissertació estudiem els problemes de vistes-múltiples relacionats amb la recuperació d'informació utilitzant tècniques de representació en espais de baixa dimensionalitat. Estudiem les tècniques existents i en proposem unes de noves per solucionar algunes de les limitacions existents. Presentem formalment el concepte de recuperació d'informació amb escriptura mixta, el qual tracta les dificultats dels sistemes de recuperació d'informació quan els textos contenen escriptures en diferents alfabets per motius tecnològics i socioculturals. Les paraules en escriptura mixta són representades en un espai de característiques finit i reduït, composat per n-grames de caràcters. Proposem els auto-codificadors de vistes-múltiples (CAE, per les seves sigles en anglès) per modelar aquestes paraules en un espai abstracte, i aquesta tècnica produeix resultats d'avantguarda. En aquest sentit, estudiem diversos models per a la recuperació d'informació entre llengües diferents (CLIR , per les sevas sigles en anglès) i proposem un model basat en xarxes neuronals composicionals (XCNN, per les sevas sigles en anglès), el qual supera les limitacions dels mètodes existents. El mètode de XCNN proposat produeix millors resultats en diferents tasques de CLIR com ara la recuperació d'informació ad-hoc, la identificació d'oracions equivalents en llengües diferents, i la detecció de plagi entre llengües diferents. Per a tal efecte, realitzem proves experimentals per aquestes tasques sobre conjunts de dades disponibles públicament, presentant els resultats i anàlisis corresponents. En aquesta dissertació, també explorem un mètode eficient per utilitzar similitud semàntica de contextos en el procés de selecció lèxica en traducció automàtica. Específicament, proposem característiques extretes dels contextos disponibles a les oracions fonts mitjançant l'ús d'auto-codificadors. L'ús de les característiques proposades demostra millores estadísticament significatives sobre sistemes de traducció robustos per a les tasques de traducció entre anglès i espanyol, i anglès i hindú. Finalment, explorem mètodes per avaluar la qualitat de les representacions de dades de text generades pels auto-codificadors, alhora que analitzem les propietats de les seves arquitectures. Com a resultat, proposem dues noves mètriques per quantificar la qualitat de les reconstruccions generades pels auto-codificadors: l'índex de preservació d'estructura (SCI, per les seves sigles en anglès) i l'índex d'acumulació de similitud (SAI, per les seves sigles en anglès). També presentem el concepte de dimensió crítica de coll d'ampolla (CBD, per les seves sigles en anglès), per sota de la qual la informació estructural es deteriora. Mostrem que, de manera interessant, la CBD està relacionada amb la perplexitat de la llengua.Gupta, PA. (2017). Cross-view Embeddings for Information Retrieval [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/78457TESI

Reversing The Twenty Questions Game

Twenty questions is a widely popular verbal game. In recent years, many computerized versions of this game have been developed in which a user thinks of an entity and a computer attempts to guess this entity by asking a series of boolean-type (yes/no) questions. In this research, we aim to reverse this game by making the computer choose an entity at random. The human aims to guess this entity by quizzing the computer with natural language queries which the computer will then attempt to parse using a boolean question answering model. The game ends when the human is successfully able to guess the entity of the computer's choice.Comment: 14 pages, 9 figures, 2 tables, This paper is a graduate course project for North Carolina State University, written for the Natural Language Processing class in Fall 2021. The paper was submitted to and graded by Dr. Munindar P. Sing

Cross-view Embeddings for Information Retrieval

Tesis doctoral en Informática realizada por Parth Gupta bajo la supervisión del Dr. Paolo Rosso (Universitat Politècnica de València) y el Dr. Rafael E. Banchs (Institute for Infocomm Research, Singapore). La tesis se defendió en Valencia (España) el 26 de enero de 2017. El comité de doctorado estuvo compuesto por los siguientes doctores: Eneko Agirre (Universidad del País Vasco), Julio Gonzalo (Universidad Nacional de Educación a Distancia) y Jaap Kamps (Universidad de Amsterdam). La tesis obtuvo la calificación de sobresaliente Cum Laude.Ph.D. thesis in Computer Science written by Parth Gupta under the supervision of Dr. Paolo Rosso (Universitat Politècnica de València) and Dr. Rafael E. Banchs (Institute for Infocomm Research, Singapore). The thesis was defended in Valencia (Spain) on January 26, 2017. The doctoral committee comprised of the following doctors: Eneko Agirre (University of the Basque Country), Julio Gonzalo (Universidad Nacional de Educación a Distancia) and Jaap Kamps (University of Amsterdam). The thesis got the grade of outstanding Cum Laude

The Effect on the Telecom Industry and Consumers after the Introduction of Reliance Jio

In the world of intense competition amongst all the industries, the telecom industry also does not fail to stay behind. With the belief that the customer is the king, each and every company in India is willing to go to depths and cross lines every day so that they can be that one brand that customers look for. While choosing a Network, one looks for various factors such as Network coverage, the call rates, the internet plan offered and not to forget but the value-added services as well. Satisfying the consumers in each of this aspect is not an easy task. Based on the literature review and after considering the questions we want to answer; the research problem of the research paper is “The Effect on The Consumers and Telecom Industry after the Introduction of Reliance Jio.” The problem mainly focuses on how the telecom industry was before and after Jio, what people believe and perceive about Reliance Jio and what challenges the competitors faced with the introduction of Jio. Based on the research problem, these are some of the objectives of our study, To study the impact of Reliance Jio on the telecom industry, the change in composition of industry, change in market share and the reforms that were undertaken To identify the effect of Jio on common people and consumer behavior To identify the business strategy followed by Jio and its Competitors The methodology used in the research paper was s Single Cross-Sectional Descriptive Design. With the objective and design, the tool used for analysis were Mean, Standard Deviation to compare and analyze the data, also test like the Z-test and Chi-Square Test were done to test the hypothesis. Finally, the findings of the research paper concluded that Jio disrupted the market to such a level forcing competitors to exit or merge, amongst the consumers, the respondents were eager to test the new competitor in the market and thus the research witnessed a significant shift in the network from other networks to Jio. Through our research we recommend that Consumers should try to shift to Jio, with their very low monthly plans and Huge value-added services offered, which the competitors are still not able to achieve, adds to the success of Jio in India

Life threatening acute kidney injury in a patient of rheumatoid arthritis, is it drug or disease related?

Even low-dose MTX therapy for treatment of rheumatic diseases is claimed to cause impairment in renal function. We report an insidious and progressive deterioration of renal function of patient with RA on low-dose MTX in a 41-year-old woman. We suggest that patients on low-dose MTX therapy should be periodically monitored for creatinine levels

Cross-language plagiarism detection using multilingual semantic network

The final publication is available at Springer via http://10.1007/978-3-642-36973-5_66Cross-language plagiarism refers to the type of plagiarism where the source and suspicious documents are in different languages. Plagiarism detection across languages is still in its infancy state. In this article, we propose a new graph-based approach that uses a multilingual semantic network to compare document paragraphs in different languages. In order to investigate the proposed approach, we used the German-English and Spanish-English cross-language plagiarism cases of the PAN-PC¿11 corpus. We compare the obtained results with two state-of-the-art models. Experimental results indicate that our graph-based approach is a good alternative for cross-language plagiarism detectionWe thank the Conselleria d′educació, Formació i Ocupació of the Generalitat Valenciana for funding the work of the first author with the Gerónimo Forteza program. The research has been carried out in the framework of the European Commission WIQ-EI IRSES project (no. 269180) and the VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems.Franco Salvador, M.; Gupta, PA.; Rosso ., P. (2013). Cross-language plagiarism detection using multilingual semantic network. En Advances in Information Retrieval. Springer Verlag (Germany). 7814:710-713. https://doi.org/10.1007/978-3-642-36973-5_66S7107137814Barrón-Cedeño, A.: On the mono- and cross-language detection of text re-use and plagiarism. Ph.D. thesis, Universitat Politènica de València (2012)Barrón-Cedeño, A., Rosso, P., Pinto, D., Juan, A.: On cross-lingual plagiarism analysis using a statistical model. In: Proceedings of the ECAI 2008 Workshop on Uncovering Plagiarism, Authorship and Social Software Misuse, PAN 2008 (2008)Havasi, C.: Conceptnet 3: A flexible, multilingual semantic network for common sense knowledge. In: The 22nd Conference on Artificial Intelligence (2007)Mcnamee, P., Mayfield, J.: Character n-gram tokenization for European language text retrieval. Inf. Retr. 7(1-2), 73–97 (2004)Montes-y-Gómez, M., Gelbukh, A., López-López, A., Baeza-Yates, R.: Flexible Comparison of Conceptual GraphsWork done under partial support of CONACyT, CGEPI-IPN, and SNI, Mexico. In: Mayr, H.C., Lazanský, J., Quirchmayr, G., Vogel, P. (eds.) DEXA 2001. LNCS, vol. 2113, pp. 102–111. Springer, Heidelberg (2001)Navigli, R., Ponzetto, S.P.: Babelnet: building a very large multilingual semantic network. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, ACL 2010, Stroudsburg, PA, USA, pp. 216–225 (2010)Potthast, M., Barrón-Cedeño, A., Stein, B., Rosso, P.: Cross-language plagiarism detection. Language Resources and Evaluation, Special Issue on Plagiarism and Authorship Analysis 45(1) (2011)Potthast, M., Eiselt, A., Barrón-Cedeño, A., Stein, B., Rosso, P.: Overview of the 3rd international competition on plagiarism detection. In: CLEF (Notebook Papers/Labs/Workshop) (2011

Reliable Natural Language Understanding with Large Language Models and Answer Set Programming

Humans understand language by extracting information (meaning) from sentences, combining it with existing commonsense knowledge, and then performing reasoning to draw conclusions. While large language models (LLMs) such as GPT-3 and ChatGPT are able to leverage patterns in the text to solve a variety of NLP tasks, they fall short in problems that require reasoning. They also cannot reliably explain the answers generated for a given question. In order to emulate humans better, we propose STAR, a framework that combines LLMs with Answer Set Programming (ASP). We show how LLMs can be used to effectively extract knowledge -- represented as predicates -- from language. Goal-directed ASP is then employed to reliably reason over this knowledge. We apply the STAR framework to three different NLU tasks requiring reasoning: qualitative reasoning, mathematical reasoning, and goal-directed conversation. Our experiments reveal that STAR is able to bridge the gap of reasoning in NLU tasks, leading to significant performance improvements, especially for smaller LLMs, i.e., LLMs with a smaller number of parameters. NLU applications developed using the STAR framework are also explainable: along with the predicates generated, a justification in the form of a proof tree can be produced for a given output.Comment: In Proceedings ICLP 2023, arXiv:2308.1489

Smart Garbage Bin

Many times, in our city we see that the garbage bins or dustbins placed at public places are overflowing. It creates unhygienic conditions for people as well as ugliness to that place leaving bad smell. Also due to this many diseases like Malaria Dengue Plague can spread. To avoid all such situations we are going to implement a project called IoT Based Smart Garbage bins. As people are getting smarter so are the things. The idea of Smart Dustbin is for the Smart buildings, Colleges, Hospitals and Bus stands. The Smart Dustbin is an improvement of normal dustbin by improving it to be smart using sensors. Smart dustbins is a new idea of implementation which makes a normal dustbin smart using ultrasonic sensors for garbage level detection and sending message to the user updating the status of the bin using GSM module

Cross-language Plagiarism Detection over Continuous-space- and Knowledge Graph-based Representations of Language

This is the author’s version of a work that was accepted for publication in Knowledge-Based Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Knowledge-Based Systems 111 (2016) 87–99. DOI 10.1016/j.knosys.2016.08.004.Cross-language (CL) plagiarism detection aims at detecting plagiarised fragments of text among documents in different languages. The main research question of this work is on whether knowledge graph representations and continuous space representations can complement to each other and improve the state-of-the-art performance in CL plagiarism detection methods. In this sense, we propose and evaluate hybrid models to assess the semantic similarity of two segments of text in different languages. The proposed hybrid models combine knowledge graph representations with continuous space representations aiming at exploiting their complementarity in capturing different aspects of cross-lingual similarity. We also present the continuous word alignment-based similarity analysis, a new model to estimate similarity between text fragments. We compare the aforementioned approaches with several state-of-the-art models in the task of CL plagiarism detection and study their performance in detecting different length and obfuscation types of plagiarism cases. We conduct experiments over Spanish-English and GermanEnglish datasets. Experimental results show that continuous representations allow the continuous word alignment-based similarity analysis model to obtain competitive results and the knowledge-based document similarity model to outperform the state-of-the-art in CL plagiarism detection. © 2016 Elsevier B.V. All rights reserved.This research has been carried out in framework of the FPI-UPV pre-doctoral grant (No de registro - 3505) awarded to Parth Gupta and in the framework of the national projects DIANA-APPLICATIONS - Finding Hidden Knowledge in Texts: Applications (TIN2012-38603-C02-01), and SomEMBED: SOcial Media language understanding - EMBEDing contexts (TIN2015-71147-C2-1-P). We would like to thank Martin Potthast, Daniel Ortiz-Martinez, and Luis A. Leiva for their support and comments during this research.Franco-Salvador, M.; Gupta, PA.; Rosso, P.; Banchs, R. (2016). Cross-language Plagiarism Detection over Continuous-space- and Knowledge Graph-based Representations of Language. Knowledge-Based Systems. 111:87-99. https://doi.org/10.1016/j.knosys.2016.08.004S879911