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

While extractive summarization is a well studied problem, it is far from solved. In recent years a large number of interesting and complex models have been used to achieve significant improvements in performance. This can easily be attributed to Deep Learning models and dense vector representations but the performance gain comes with the cost of computational and representational complexity. In this work, we present a simple, yet effective approach for extractive summarization of news articles. In line with many recent works in this area we propose an encoder-decoder architecture with a simple bag of word encoder for sentences followed by an attention based decoder for relevant sentence selection. Our model is trained end-to-end and its performance is comparable to the state-of-the-art models while being simpler both in terms of the number of parameters (significantly lesser) as well as the representational complexity.

Abstract

When a human interacts with an information retrieval chat bot, he/she can ask multiple questions at the same time. Current question answering systems can’t handle this scenario effectively. In this paper we propose an approach to identify question spans in a given utterance, by posing this as a sequence labeling problem. The model is trained and evaluated over 4 different freely available datasets. To get a comprehensive coverage of the compound question scenarios, we also synthesize a dataset based on the natural question combination patterns. We exhibit improvement in the performance of the DrQA system when it encounters compound questions which suggests that this approach is vital for real-time human-chatbot interaction.

Abstract

Humor is an essential characteristic of language. It has been a topic of research in linguistics and philosophy from historical times. In computer science, computational humor, as a part of Natural Language Processing, is a growing area of research. Social Media is rapidly growing as a platform for communication but processing of social media, owing to its semantic perplexity, is still a challenge. These two facts lead us to present a novel dataset for humor classification which captures diversity in humor on web resources. The large size of this dataset is to meet the data requirements for modern machine learning algorithms. This paper also deals with creating a model for detecting and analyzing humor in social media text extracted from eclectic sources on the Internet.

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.

Abstract

Sentiment analysis research has predominantly been on English texts. There exists many sentiment resources for English but very less exist for other languages. To improve sentiment analysis in a low resource language, sentiment labeled corpora are translated from English into the focus language and use them as additional resources for sentiment analysis research in the focus language [3]. But when text is translated from one language into another, sentiment is preserved to varying degrees. In this paper, we use product and book reviews in English as stand-in for source language text and determine loss in sentiment and sentiment predictability when they are translated into Telugu (a low resource South Asian language), manually and automatically. For this purpose, we use manually and automatically determined sentiment labels of the English text as a benchmark. We show that sentiment analysis of Telugu manual translations of English text produces competitive results w.r.t English sentiment analysis. We discover that even though machine translation ignificantly reduces the human ability to recover sentiment, automatic sentiment systems are still able to capture sentiment information from the translations in certain cases. In the process, we created a Telugu-English parallel corpus that is independently annotated for sentiment using a 5-value scale by Telugu and English speakers. We also created a Telugu lexicon annotated at both sentiment and emphasis level.

Abstract

Keywords of a document are a representative of its content, and it helps to have meaningful words to facilitate search and organization of documents. Hence, finding methods that can automatically identify keywords in a document is very important as manual processes for this is very cumbersome and error-prone. If this task is accomplished for song lyrics, it has varied applications such as recommendation systems and digital music library management. This work proposes and compares methods to identify keywords from lyrics of Bollywood songs. We use a collection of lyrics of 1055 Bollywood songs, all written in the Devanagari script. Experiments include looking at the spatial distribution of the terms, their occurrence in a certain context or position, and using WordNet to generate keywords not present in the document. Validation was done by human annotators by providing a score to each method based on the results obtained on a subset of the data. We also used Latent Dirichlet Allocation and Latent Semantic Indexing to validate the results, as further explained in the paper.

Abstract

Many methods have been developed for various sentence classification tasks for English, which usually exploit linguistic resources like parsers or rely on the large amount of annotated or unannotated data, making it difficult to adapt them to other languages. In this paper, we present an evaluation of popular deep learning methods for sentence classification on the morphologically rich Indian languages, specifically, Hindi and Telugu. For this purpose, we also created a question classification dataset for Hindi, by translating the TREC-UIUC dataset. We show that character based input can enhance the performance of current classification systems for morphologically rich languages. Finally, we show that our multiInput-CNN variant is able to perform better than our baselines in two out of three tasks in Hindi and Telugu, while giving comparable results for others.

Abstract

Movie ratings play an important role in tasks such as user movie recommendations, verifying the relationship between usersubmitted reviews and ratings etc. The ability to predict the rating of a movie would be useful considering these aspects. In this work, firstly, we propose methods to predict the movie rating based on its summary. We then set out to use priors that are generally available with movie summaries in order to improve the accuracy. In order to achieve this, we consider the associated movie reviews as well while predicting the rating and provide insights on why this helps our models perform better. We use the review based sentiment along with the summary in order to predict the rating more accurately since the sentiment captures a lot of essential information that can aid rating prediction. We experiment with various deep learning architectures and the results show a significant accuracy boost of around 2% in most of the models which show the generalizability of our approach.

Abstract

The paper presents the development of a phonetically balanced read speech corpus of code-mixed Hindi-English. Phonetic balance in the corpus has been created by selecting sentences that contained triphones lower in frequency than a predefined threshold. The assumption with a compulsory inclusion of such rare units was that the high frequency triphones will inevitably be included. Using this metric, the Pearson’s correlation coefficient of the phonetically balanced corpus with a large code-mixed reference corpus was recorded to be 0.996. The data for corpus creation has been extracted from selected sections of Hindi newspapers. These sections contain frequent English insertions in a matrix of Hindi sentence. Statistics on the phone and triphone distribution have been presented, to graphically display the phonetic likeness between the reference corpus and the corpus sampled through our method.