Sequence to Sequence Learning for Query Expansion

Using sequence to sequence algorithms for query expansion has not been explored yet in Information Retrieval literature nor in Question-Answering's. We tried to fill this gap in the literature with a custom Query Expansion engine trained and tested on open datasets. Starting from open datasets, we built a Query Expansion training set using sentence-embeddings-based Keyword Extraction. We therefore assessed the ability of the Sequence to Sequence neural networks to capture expanding relations in the words embeddings' space.Comment: 8 pages, 2 figures, AAAI-19 Student Abstract and Poster Progra

Proceedings of QG2010: The Third Workshop on Question Generation

These are the peer-reviewed proceedings of "QG2010, The Third Workshop on Question Generation". The workshop included a special track for "QGSTEC2010: The First Question Generation Shared Task and Evaluation Challenge". QG2010 was held as part of The Tenth International Conference on Intelligent Tutoring Systems (ITS2010)

Simplifying Deep-Learning-Based Model for Code Search

To accelerate software development, developers frequently search and reuse existing code snippets from a large-scale codebase, e.g., GitHub. Over the years, researchers proposed many information retrieval (IR) based models for code search, which match keywords in query with code text. But they fail to connect the semantic gap between query and code. To conquer this challenge, Gu et al. proposed a deep-learning-based model named DeepCS. It jointly embeds method code and natural language description into a shared vector space, where methods related to a natural language query are retrieved according to their vector similarities. However, DeepCS' working process is complicated and time-consuming. To overcome this issue, we proposed a simplified model CodeMatcher that leverages the IR technique but maintains many features in DeepCS. Generally, CodeMatcher combines query keywords with the original order, performs a fuzzy search on name and body strings of methods, and returned the best-matched methods with the longer sequence of used keywords. We verified its effectiveness on a large-scale codebase with about 41k repositories. Experimental results showed the simplified model CodeMatcher outperforms DeepCS by 97% in terms of MRR (a widely used accuracy measure for code search), and it is over 66 times faster than DeepCS. Besides, comparing with the state-of-the-art IR-based model CodeHow, CodeMatcher also improves the MRR by 73%. We also observed that: fusing the advantages of IR-based and deep-learning-based models is promising because they compensate with each other by nature; improving the quality of method naming helps code search, since method name plays an important role in connecting query and code

Automatic taxonomy evaluation

This thesis would not be made possible without the generous support of IATA.Les taxonomies sont une représentation essentielle des connaissances, jouant un rôle central dans de nombreuses applications riches en connaissances. Malgré cela, leur construction est laborieuse que ce soit manuellement ou automatiquement, et l'évaluation quantitative de taxonomies est un sujet négligé. Lorsque les chercheurs se concentrent sur la construction d'une taxonomie à partir de grands corpus non structurés, l'évaluation est faite souvent manuellement, ce qui implique des biais et se traduit souvent par une reproductibilité limitée. Les entreprises qui souhaitent améliorer leur taxonomie manquent souvent d'étalon ou de référence, une sorte de taxonomie bien optimisée pouvant service de référence. Par conséquent, des connaissances et des efforts spécialisés sont nécessaires pour évaluer une taxonomie. Dans ce travail, nous soutenons que l'évaluation d'une taxonomie effectuée automatiquement et de manière reproductible est aussi importante que la génération automatique de telles taxonomies. Nous proposons deux nouvelles méthodes d'évaluation qui produisent des scores moins biaisés: un modèle de classification de la taxonomie extraite d'un corpus étiqueté, et un modèle de langue non supervisé qui sert de source de connaissances pour évaluer les relations hyperonymiques. Nous constatons que nos substituts d'évaluation corrèlent avec les jugements humains et que les modèles de langue pourraient imiter les experts humains dans les tâches riches en connaissances.Taxonomies are an essential knowledge representation and play an important role in classification and numerous knowledge-rich applications, yet quantitative taxonomy evaluation remains to be overlooked and left much to be desired. While studies focus on automatic taxonomy construction (ATC) for extracting meaningful structures and semantics from large corpora, their evaluation is usually manual and subject to bias and low reproducibility. Companies wishing to improve their domain-focused taxonomies also suffer from lacking ground-truths. In fact, manual taxonomy evaluation requires substantial labour and expert knowledge. As a result, we argue in this thesis that automatic taxonomy evaluation (ATE) is just as important as taxonomy construction. We propose two novel taxonomy evaluation methods for automatic taxonomy scoring, leveraging supervised classification for labelled corpora and unsupervised language modelling as a knowledge source for unlabelled data. We show that our evaluation proxies can exert similar effects and correlate well with human judgments and that language models can imitate human experts on knowledge-rich tasks

What a Nerd! Beating Students and Vector Cosine in the ESL and TOEFL Datasets

In this paper, we claim that Vector Cosine, which is generally considered one of the most efficient unsupervised measures for identifying word similarity in Vector Space Models, can be outperformed by a completely unsupervised measure that evaluates the extent of the intersection among the most associated contexts of two target words, weighting such intersection according to the rank of the shared contexts in the dependency ranked lists. This claim comes from the hypothesis that similar words do not simply occur in similar contexts, but they share a larger portion of their most relevant contexts compared to other related words. To prove it, we describe and evaluate APSyn, a variant of Average Precision that, independently of the adopted parameters, outperforms the Vector Cosine and the co-occurrence on the ESL and TOEFL test sets. In the best setting, APSyn reaches 0.73 accuracy on the ESL dataset and 0.70 accuracy in the TOEFL dataset, beating therefore the non-English US college applicants (whose average, as reported in the literature, is 64.50%) and several state-of-the-art approaches.Comment: in LREC 201

Cyber bullying identification and tackling using natural language processing techniques

Abstract. As offensive content has a detrimental influence on the internet and especially in social media, there has been much research identifying cyberbullying posts from social media datasets. Previous works on this topic have overlooked the problems for cyberbullying categories detection, impact of feature choice, negation handling, and dataset construction. Indeed, many natural language processing (NLP) tasks, including cyberbullying detection in texts, lack comprehensive manually labeled datasets limiting the application of powerful supervised machine learning algorithms, including neural networks. Equally, it is challenging to collect large scale data for a particular NLP project due to the inherent subjectivity of labeling task and man-made effort. For this purpose, this thesis attempts to contribute to these challenges by the following. We first collected and annotated a multi-category cyberbullying (10K) dataset from the social network platform (ask.fm). Besides, we have used another publicly available cyberbullying labeled dataset, ’Formspring’, for comparison purpose and ground truth establishment. We have devised a machine learning-based methodology that uses five distinct feature engineering and six different classifiers. The results showed that CNN classifier with Word-embedding features yielded a maximum performance amidst all state-of-art classifiers, with a detection accuracy of 93\% for AskFm and 92\% for FormSpring dataset. We have performed cyberbullying category detection, and CNN architecture still provide the best performance with 81\% accuracy and 78\% F1-score on average. Our second purpose was to handle the problem of lack of relevant cyberbullying instances in the training dataset through data augmentation. For this end, we developed an approach that makes use of wordsense disambiguation with WordNet-aided semantic expansion. The disambiguation and semantic expansion were intended to overcome several limitations of the social media (SM) posts/comments, such as unstructured content, limited semantic content, among others, while capturing equivalent instances induced by the wordsense disambiguation-based approach. We run several experiments and disambiguation/semantic expansion to estimate the impact of the classification performance using both original and the augmented datasets. Finally, we have compared the accuracy score for cyberbullying detection with some widely used classifiers before and after the development of datasets. The outcome supports the advantage of the data-augmentation strategy, which yielded 99\% of classifier accuracy, a 5\% improvement from the base score of 93\%. Our third goal related to negation handling was motivated by the intuitive impact of negation on cyberbullying statements and detection. Our proposed approach advocates a classification like technique by using NegEx and POS tagging that makes the use of a particular data design procedure for negation detection. Performances using the negation-handling approach and without negation handling are compared and discussed. The result showed a 95\% of accuracy for the negated handed dataset, which corresponds to an overall accuracy improvement of 2\% from the base score of 93\%. Our final goal was to develop a software tool using our machine learning models that will help to test our experiments and provide a real-life example of use case for both end-users and research communities. To achieve this objective, a python based web-application was developed and successfully tested

Sentiment Analysis in Social Streams

In this chapter we review and discuss the state of the art on sentiment analysis in social streams –such as web forums, micro-blogging systems, and so- cial networks–, aiming to clarify how user opinions, affective states, and intended emotional effects are extracted from user generated content, how they are modeled, and how they could be finally exploited. We explain why sentiment analysis tasks are more difficult for social streams than for other textual sources, and entail going beyond classic text-based opinion mining techniques. We show, for example, that social streams may use vocabularies and expressions that exist outside the main- stream of standard, formal languages, and may reflect complex dynamics in the opinions and sentiments expressed by individuals and communities