Abstract

Neural word embeddings have been able to deliver impressive results in many Natural Language Processing tasks. The quality of the word embedding determines the performance of a supervised model. However, choosing the right set of word embeddings for a given dataset is a major challenging task for enhancing the results. In this paper, we have evaluated neural word embeddings on sentiment analysis task in two steps: (i) proposed a mixture of classification experts (MoCE) model for sentiment classification task, (ii) to compare and improve the classification accuracy by different combination of word embedding as first level of features and pass it to cascade model inspired by gcForest for extracting diverse features. We argue that in the first step, each expert learns a certain positive or negative examples corresponding to its category and in the second step resulting features on a given task (polarity identification) can achieve competitive performance with state-ofthe-art methods in terms of accuracy, precision and recall using gcForest.

Abstract

Language is a powerful tool which can be used to state the facts as well as express our views and perceptions. Most of the times, we find a subtle bias towards or against someone or something. When it comes to politics, media houses and journalists are known to create bias by shrewd means such as misinterpreting reality and distorting viewpoints towards some parties. This misinterpretation on a large scale can lead to the production of biased news and conspiracy theories. Automating bias detection in newspaper articles could be a good challenge for research in NLP. We proposed a headline attention network for this bias detection. Our model has two distinctive characteristics: (i) it has a structure that mirrors a person’s way of reading a news article (ii) it has attention mechanism applied on the article based on its headline, enabling it to attend to more critical content to predict bias. As the required datasets were not available, we created a dataset comprising of 1329 news articles collected from various Telugu newspapers and marked them for bias towards a particular political party. The experiments conducted on it demonstrated that our model outperforms various baseline methods by a substantial margin.

Abstract

Contemporary datasets on tobacco consumption focus on one of two topics, either public health mentions and disease surveillance, or sentiment analysis on topical tobacco products and services. However, two primary considerations are not accounted for, the language of the demographic affected and a combination of the topics mentioned above in a finegrained classification mechanism. In this paper, we create a dataset of 3144 tweets, which are selected based on the presence of colloquial slang related to smoking and analyze it based on the semantics of the tweet. Each class is created and annotated based on the content of the tweets such that further hierarchical methods can be easily applied. Further, we prove the efficacy of standard text classification methods on this dataset, by designing experiments which do both binary as well as multi-class classification. Our experiments tackle the identification of either a specific topic (such as tobacco product promotion), a general mention (cigarettes and related products) or a more fine-grained classification.This methodology paves the way for further analysis, such as understanding sentiment or style, which makes this dataset a vital contribution to both disease surveillance and tobacco use research.

Abstract

Newspapers are a popular form of written discourse, read by many people, thanks to the novelty of the information provided by the news content in it. A headline is the most widely read part of any newspaper due to its appearance in a bigger font and sometimes in colour print. In this paper, we suggest and implement a method for computing inferences from English news headlines, excluding the information from the context in which the headlines appear. This method attempts to generate the possible assumptions a reader formulates in mind upon reading a fresh headline. The generated inferences could be useful for assessing the impact of the news headline on readers including children. The understandability of the current state of social affairs depends greatly on the assimilation of the headlines. As the inferences that are independent of the context depend mainly on the syntax of the headline, dependency trees of headlines are used in this approach, to find the syntactical structure of the headlines and to compute inferences out of them.

Abstract

In this paper, a dialogue system for Hospital domain in Telugu, which is a resource-poor Dravidian language, has been built. It handles various hospital and doctor related queries. The main aim of this paper is to present an approach for modelling a dialogue system in a resource-poor language by combining linguistic and domain knowledge. Focusing on the question answering aspect of the dialogue system, we identified Question Classification and Query Processing as the two most important parts of the dialogue system. Our method combines deep learning techniques for question classification and computational rule-based analysis for query processing. Human evaluation of the system has been performed as there is no automated evaluation tool for dialogue systems in Telugu. Our system achieves a high overall rating along with a significantly accurate context-capturing method as shown in the results.

Abstract

Anaphora resolution is a challenging task which has been the interest of NLP researchers for a long time. Traditional resolution techniques like eliminative constraints and weighted preferences were successful in many languages. However, they are ineffective in free word order languages like most South Asian languages. Heuristic and rule-based techniques were typical in these languages, which are constrained to context and domain. In this paper, we venture a new strategy using neural networks for resolving anaphora in human-human dialogues. The architecture chiefly consists of three components, a shallow parser for extracting features, a feature vector generator which produces the word embeddings, and a neural network model which will predict the antecedent mention of an anaphora. The system has been trained and tested on Telugu conversation corpus we generated. Given the advantage of the semantic information in word embeddings and appending actor, gender, number, person and part of plural features the model has reached an F1-score of 86.

Abstract

The content on social media now-a-days includes a huge number of messages both in textual and visual forms that are satirical in nature. Satire in the form of irony, sarcasm, and ridicule to a person, is depicted by memes on social media. Satire detection in text is an active area of research, but in the visual domain it is relatively less explored. The objective of our work is detection of satire in images taken from the popular photo-sharing platform - Flickr. Traditional methods for visual satire detection are based on supervised learning which has a necessary requirement of annotating the data which is a tedious task. In our work, to address this issue, we propose a novel, unsupervised approach which leverages the visual semantics of the images. We provide a study of clustering methods, where the difference between visual semantics of the two classes - satirical and non-satirical - becomes the basis for classification of visual content. Here, we suggest an autoencoder based clustering framework to effectively combine embedded feature learning and clustering assignments for detection of satire.

Abstract

In a multilingual or sociolingual configuration Intra-sentential Code Switching (ICS) or Code Mixing (CM) is frequently observed nowadays. In the world most of the people know more than one language. The CM usage is especially apparent in social media platforms. Moreover, ICS is particularly significant in the context of technology, health and law where conveying the upcoming developments are difficult in ones native language. In applications like dialog systems, machine translation, semantic parsing, shallow parsing, etc. CM and Code Switching pose serious challenges. To do any further advancement in code-mixed data, the necessary step is Language Identification. So, in this paper we present a study of various models - Nave Bayes Classifier, Random Forest Classifier, Conditional Random Field (CRF) and Hidden Markov Model (HMM) for Language Identification in English - Telugu Code Mixed Data. Considering the paucity of resources in code mixed languages, we proposed CRF model and HMM model for word level language identification. Our best performing system is CRF-based with an f1-score of 0.91.

Abstract

Movies are one of the most prominent means of entertainment. The widespread use of the Internet in recent times has led to large volumes of data related to movies being generated and shared online. People often prefer to express their views online in English as compared to other local languages. This leaves us with a very little amount of data in languages apart from English to work on. To overcome this, we created the Multi-Language Movie Review Dataset (MLMRD). The dataset consists of genre, rating, and synopsis of a movie across multiple languages, namely Hindi, Telugu, Tamil, Malayalam, Korean, French, and Japanese. The genre of a movie can be identified by its synopsis. Though the rating of a movie may depend on multiple factors like the performance of actors, screenplay, direction etc but in most of the cases, synopsis plays a crucial role in the movie rating. In this work, we provide various model architectures that can be used to predict the genre and the rating of a movie across various languages present in our dataset based on the synopsis

Abstract

Resource poor languages, like Odia, inherently lack the necessary resources and tools for the task of sentiment analysis to give promising results. With more user-generated raw data readily available today, it is of prime importance to have annotated corpora from various domains. This paper is a first attempt towards building an annotated corpus of Odia poetry with sentiment labels. The annotated corpus is further used for sentiment classification using machine learning techniques in order to establish a baseline. Stylistic variations and structural differences between poetic and non-poetic texts make the task of sentiment classification challenging for the former. Using the annotated corpus of poems, we obtained comparable accuracies across various classification models. Linear-SVM outperformed other classifiers with a macro F1- Score of 0.68. The annotated corpus contains a total of 730 Odia Poems of various genres with a vocabulary of more than 23k words. Fleiss Kappa score of 0.83 was obtained which corresponds to near perfect agreement among the annotators.