Abstract

Social media has become one of the main channels for peo-ple to communicate and share their views with the society. We can often detect from these views whether the person is in favor, against or neu-tral towards a given topic. These opinions from social media are very useful for various companies. We present a new dataset that consists of 3545 English-Hindi code-mixed tweets with opinion towards Demoneti-sation that was implemented in India in 2016 which was followed by a large countrywide debate. We present a baseline supervised classification system for stance detection developed using the same dataset that uses various machine learning techniques to achieve an accuracy of 58.7% on 10-fold cross validation.

Abstract

Social media platforms like twitter and facebook have be-come two of the largest mediums used by people to express their views to-wards different topics. Generation of such large user data has made NLP tasks like sentiment analysis and opinion mining much more important. Using sarcasm in texts on social media has become a popular trend lately. Using sarcasm reverses the meaning and polarity of what is implied by the text which poses challenge for many NLP tasks. The task of sarcasm detection in text is gaining more and more importance for both commer-cial and security services. We present the first English-Hindi code-mixed dataset of tweets marked for presence of sarcasm and irony where each token is also annotated with a language tag. We present a baseline su-pervised classification system developed using the same dataset which achieves an average F-score of 78.4 after using random forest classifier and performing 10-fold cross validation.

Abstract

Bug reporting is a major part of software maintenance and due to its inherently asynchronous nature, duplicate bug reporting has become fairly common. Detecting duplicate bug reports is an important task in order to avoid the assignment of a same bug to different developers. Earlier approaches have improved duplicate bug report detection by using the notions of word embeddings, topic models and other machine learning approaches. In this poster, we attempt to combine Latent Dirichlet Allocation (LDA) and word embeddings to leverage the strengths of both approaches for this task. As a first step towards this idea, we present initial analysis and an approach which is able to outperform both word embeddings and LDA for this task. We validate our hypothesis on a real world dataset of Firefox project and show that there is potential in combining both LDA and word embeddings for duplicate bug report detection.

Abstract

Bug report filing is a major part of software maintenance. Due to extensive number of bugs filed everyday in large software projects and the asynchronous nature of bug report filing ecosystem, duplicate bug reports are filed. Capturing and tagging duplicate bug reports is crucial in order to avoid assignment of the same bug to different developers. Efforts have been made in the past to detect duplicate bug reports by using topic modelling [2], discriminative methods [5], meta-attributes [6], etc. Recently, Yang et al.[8] proposed an approach to combine word embeddings, TF-IDF and meta-attributes to compute bug similarity between two bug reports.

Abstract

Real-time Indoor theft detection from surveillancevideos is not only a challenging problem of object detection and human activity recognition in the field of computer vision, but also an urgent need for preventing theft crimes in real life. The system uses digital cameras to scan the faces of people approaching a security gate i.e. the entry gate, and then matches its faces with the faces in the database, and if found not to be a known one it automatically generates an alert. In this paper, we propose a framework for real-time indoor theft detection based on the combining result of face recognition and pattern matching by analyzing the activities with that of thieves. At last, if detected abnormal it automatically sends a message using Multimedia Message Service with the help of GPRS/GSM modem.

Abstract

With news stories coming from a variety of sources, it is crucial for news aggregators to present interesting articles to the user to maximize their engagement. This creates the need for a news recommendation system which understands the content of the articles as well as accounts for the users’ preferences. Methods such as Collaborative Filtering, which are well known for general recommendations, are not suitable for news because of the short life span of articles and because of the large number of articles published each day. Apart from this, such methods do not harness the information present in the sequence in which the articles are read by the user and hence are unable to account for the specific and generic interests of the user which may keep changing with time. In order to address these issues for news recommendation, we propose the Recurrent Attentive Recommendation Engine (RARE). RARE consists of two components and utilizes the distributed representations of news articles. The first component is used to model the user’s sequential behaviour of news reading in order to understand her general interests, i.e., to get a summary of her interests. The second component utilizes an article level attention mechanism to understand her specific interests. We feed the information obtained from both the components to a Siamese Network in order to make predictions which pertain to

Abstract

Bug report filing is a major part of software maintenance. Due to the asynchronous nature of the bug filing process, duplicate bug reports are filed. Detecting duplicate bug reports is an important aspect of software maintenance since the same bug should not be assigned to different developers. In this poster, we present Deep Word Embedding Network for computing similarity between two bug reports for the task of duplicate bug report detection. We propose to learn a two step model to calculate similarity between two bug reports by means of word embeddings and a deep neural network. We run experiments on two large datasets of Mozilla Project and Open Office Project and compare the proposed approach with baselines and related approaches. Through this initial work, we show that a combination of word embeddings and deep neural networks can be used to improve duplicate bug report detection.

Abstract

CESNA model consists of twin Bi-directional Long Short-Term Memory Recurrent Neural Networks (Bi-LSTM RNN) with shared parameters joined by a contrastive loss function based on a similarity metric. The model learns the representations of resource-poor and resource-rich language in a common emoji space by using a similarity metric based on the emojis present in sentences from both languages. The model, hence, projects sentences with similar emojis closer to each other and the sentences with different emojis farther from one another. Experiments on large-scale Twitter datasets of resource-rich languages - English and Spanish and resource-poor languages - Hindi and Telugu reveal that CESNA outperforms the state-of-the-art emoji prediction approaches based on distributional semantics,

Abstract

Social media platforms such as Twitter and Facebook are becoming popular in multilingual societies. This trend induces portmanteau of South Asian languages with English. The blend of multiple languages as code-mixed data has recently become popular in research communities for various NLP tasks. Code-mixed data consist of anomalies such as grammatical errors and spelling variations. In this paper, we leverage the contextual property of words where the different spelling variation of words share similar context in a large noisy social media text. We capture different variations of words belonging to same context in an unsupervised manner using distributed representations of words. Our experiments reveal that preprocessing of the code-mixed dataset based on our approach improves the performance in state-of-the-art part-of-speech tagging (POS-tagging) and sentiment analysis tasks.

Abstract

The furtherance of social media led people to share the reviews in various ways such as video, audio and text. Recently, the performance of sentiment classification is achieved success using neural networks. In this paper, neural network approach is presented to detect the sentiment from audio and text models. For audio, features like Mel Frequency Cepstral Coefficients (MFCC) are used to build Deep Neural Network (DNN) and Deep Neural Network Attention Mechanism (DNNAM) classifiers. From the results, it is noticed that DNNAM gives better results compared to DNN because the DNN is a frame based one where as the DNNAM is an utterance level classification thereby efficiently use the context. Additionally, textual features are extracted from the transcript of the audio input using Word2vec model. Support Vector Machine (SVM) and Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) classifiers are used to develop a sentiment model. From the experiments it is noticed the LSTM-RNN outperforms the SVM as the LSTM-RNN is able to memorize long temporal context. The performance is also significantly improved by combining both the audio and text modalities.