Abstract

In this paper, we discuss the enrichment of a manually developed resource of Telugu lexicon, OntoSenseNet. OntoSenseNet is a ontological sense annotated lexicon that marks each verb of Telugu with a primary and a secondary sense. The area of research is relatively recent but has a large scope of development. We provide an introductory work to enrich the OntoSenseNet to promote further research in Telugu. Classifiers are adopted to learn the sense relevant features of the words in the resource and also to automate the tagging of sense-types for verbs. We perform a comparative analysis of different classifiers applied on OntoSenseNet. The results of the experiment prove that automated enrichment of the resource is effective using SVM classifiers and Adaboost ensemble.

Abstract

The presented work aims at generating a systematically annotated corpus that can support the enhancement of sentiment analysis tasks in Telugu using wordlevel sentiment annotations. From OntoSenseNet, we extracted 11,000 adjectives, 253 adverbs, 8483 verbs and sentiment annotation is being done by language experts. We discuss the methodology followed for the polarity annotations and validate the developed resource. This work aims at developing a benchmark corpus, as an extension to SentiWordNet, and baseline accuracy for a model where lexeme annotations are applied for sentiment predictions. The fundamental aim of this paper is to validate and study the possibility of utilizing machine learning algorithms, word-level sentiment annotations in the task of automated sentiment identification. Furthermore, accuracy is improved by annotating the bi-grams extracted from the target corpus.

Abstract

This paper illustrates the interface of the tool we developed for crowd sourcing and we explain the annotation procedure in detail. Our tool is named as ‘Parupalli Padajaalam1 ’ which means web of words by Parupalli. The aim of this tool is to populate the OntoSenseNet, sentiment polarity annotated Telugu resource. Recent works have shown the importance of word-level annotations on sentiment analysis. With this as basis, we aim to analyze the importance of sense-annotations obtained from OntoSenseNet in performing the task of sentiment analysis. We explain the features extracted from OntoSenseNet (Telugu). Furthermore we compute and explain the adverbial class distribution of verbs in OntoSenseNet. This task is known to aid in disambiguating wordsenses which helps in enhancing the performance of word-sense disambiguation (WSD) task(s).

Abstract

Since the beginning of the 20th century, people have started studying the correlation between language and culture. They observed how language is used in discourses to establish power relations in the society. Eggins (2004) found that the link between language and the choice made by the speaker in the exchange enable us to see speakers making meaning about interpersonal: the extent of their intimacy, their level of familiarity with each other and their attitudes and judgments. In political speeches, the speaker uses language to persuade the voters, influence their perceptions and build a positive interpersonal identity. Keeping in mind this result-oriented attempt by the speakers, Van Dijk and others (1997) describe discourse as political when it has a direct functional role as a form of political action in the political process. In this paper, we will look at four such speeches given by notable politicians from both winning and losing parties during the campaign of Andhra Pradesh State Assembly elections of 2014 and closely observe the linguistic choices made at the lexical and semantic levels. By a contrastive analysis of the speeches of winning and losing parties, we can identify the linguistic features which contribute to the outcome.

Abstract

Sentence Classification is one of the most fundamental tasks in NLP, where the aim is to classify a given sentence into a pre-defined set of classes. A lot of work has been done in English in the last few years, which vary in their methodologies. A huge proportion of these works represent the input sentences as a sequence of words in their models. Only a few of them rely on character level representation. Through this work, we introduce a new method for representing a sentence - as a sequence of syllables. As we show in this work, syllables are a better choice to represent the sub-word level information in a sentence, which is essential for morphologically rich languages. We consider the tasks of Sentiment Analysis and Question Classification in three languages showing varied morphological richness - English, Hindi and Telugu. Through extensive evaluation, we show that syllables are the best performing input type when compared to words or characters for the morphologically rich languages - Hindi and Telugu.

Abstract

In this paper, an approach to detect the sentiment of a song based on its multi-modality natures (text and audio) is presented. The textual lyric features are extracted from the bag of words. By using these features, Doc2Vec will generate a single vector for each song. Support Vector Machine (SVM), Naive Bayes (NB) and a combination of both these classifiers are developed to classify the sentiment using the textual lyric features. Audio features are used as an add-on to the lyrical ones which include prosody features, temporal features, spectral features, tempo and chroma features. Gaussian Mixture Models (GMM), SVM and a combination of both these classifiers are developed to classify the sentiment using audio features. GMM are known for capturing the distribution in the features and SVM are known for discriminating the features. Hence these models are combined to improve the performance of sentiment analysis. Performance is further improved by combining the text and audio feature domains. These text and audio features are extracted at the beginning, ending and for the whole song. From our experimental results, it is observed that the first 30 seconds (s) of a song gives better performance for detecting the sentiment of the song rather than the last 30s or from the whole song.

Abstract

We propose the use of the Newsela corpus for Complex Word Identification, a sub-problem of Lexical Simplification and conduct an empirical evaluation by comparing it with benchmark corpora previously employed for this task. Our experiments suggest that the proposed corpus is effective for Complex Word Identification, thus helping Lexical Simplification.

Abstract

When it comes to computational language generation systems, humour is a relatively unexplored domain, especially more so for Hindi (or rather, for most languages other than English). Most researchers agree that a joke consists of two main parts-the setup and the punchline, with humour being encoded in the incongruity between the two. In this paper, we look at Dur se Dekha jokes, a restricted domain of humorous three liner poetry in Hindi. We analyze their structure to understand how humour is encoded in them and formalize it. We then develop a system which is successfully able to generate a basic form of these jokes.

Abstract

Sexism is prevalent in today’s society, both offline and online, and poses a credible threat to social equality with respect to gender. According to ambivalent sexism theory (Glick and Fiske, 1996), it comes in two forms: Hostile and Benevolent. While hostile sexism is characterized by an explicitly negative attitude, benevolent sexism is more subtle. Previous works on computationally detecting sexism present online are restricted to identifying the hostile form. Our objective is to investigate the less pronounced form of sexism demonstrated online. We achieve this by creating and analyzing a dataset of tweets that exhibit benevolent sexism. By using Support Vector Machines (SVM), sequence-to-sequence models and FastText classifier, we classify tweets into ‘Hostile’,‘Benevolent’or ‘Others’ class depending on the kind of sexism they exhibit. We have been able to achieve an F1-score of 87.22% using FastText classifier. Our work helps analyze and understand the much prevalent ambivalent sexism in social media.

Abstract

This work presents a corpus of Bollywood song lyrics and its metadata, annotated with sentiment polarity. We call this BolLy. It contains lyrics of 1055 songs ranging from those composed in the year 1970 to the most recent ones. This dataset is of utmost value as all the annotation is done manually by three annotators and this makes it a very rich dataset for training purposes. In this work, we describe the creation and annotation process, content, and the possible uses of the dataset. As an experiment, we have built a basic classification system to identify the emotion polarity of the song based solely on the lyrics and this can be used as a baseline algorithm for the same. BolLy can also be used for studying code-mixing with respect to lyrics.