Plaban Kumar Bhowmick

Reader Perspective Emotion Analysis in Text through Ensemble based Multi-Label Classification Framework

Multiple emotions are often triggered in readers in response to text stimuli like news article. In this paper, we present a novel method for classifying news sentences into multiple emotion categories using an ensemble based multi-label classification technique called RAKEL. The emotion data consists of 1305 news sentences and the emotion classes considered are disgust, fear, happiness and sadness. Words are the most obvious choice as feature for emotion recognition. In addition to that we have introduced two novel feature sets: polarity of subject, verb and object of the sentences and semantic frames. Experiments concerning the comparison of features revealed that semantic frame feature combined with polarity based feature performs best in emotion classification. Experiments on feature selection over word and semantic frame features have been performed in order to handle feature sparseness problem. In both word and semantic frame feature, improvements in the overall performance have been observed after optimal feature selection.

An Agreement Measure for Determining Inter-Annotator Reliability of Human Judgements on Affective Text

An affective text may be judged to belong to multiple affect categories as it may evoke different affects with varying degree of intensity. For affect classification of text, it is often required to annotate text corpus with affect categories. This task is often performed by a number of human judges. This paper presents a new agreement measure inspired by Kappa coefficient to compute inter-annotator reliability when the annotators have freedom to categorize a text into more than one class. The extended reliability coefficient has been applied to measure the quality of an affective text corpus. An analysis of the factors that influence corpus quality has been provided.

Shikshak: An Intelligent Tutoring System Authoring tool for rural education

Low literacy scenario in India and other developing nation demands an alternative learning environment to deal with the problem. Lack of trained teachers, high dropout rates are some of the major problems that need to be addressed. Intelligent Tutoring System (ITS) or ITS Authoring tools (ITSAT) can be thought of as a possible solution to these problems. In this paper we present Shikshak, an ITSAT developed by us and discuss its deployment in the district of Paschim Medinipur, West Bengal along with its sample effect on primary education.

Multi-label Text Classification Approach for Sentence Level News Emotion Analysis

Multiple emotions are often evoked in readers in response to text stimuli like news article. In this paper, we present a novel method for classifying news sentences into multiple emotion categories using Multi-Label K Nearest Neighbor classification technique. The emotion data consists of 1305 news sentences and the emotion classes considered are disgust, fear, happiness and sadness. Words and polarity of subject, verb and object of the sentences and semantic frames have been used as features. Experiments have been performed on feature comparison and feature selection.

An authoring system for developing Intelligent Tutoring System

Intelligent Tutoring Systems (ITS) are computer-based education tools used for adaptive learning. An ITS authoring system allows teachers to create different modules of an intelligent tutoring system. In this paper, we present an ITS authoring system that allows teachers with very less computer skill to author most important modules of an ITS with minimum efforts. The salient features of the system include authoring multilingual documents, defining teaching strategies using fuzzy rules, representing student models through fuzzy state transition automata. The authoring system was used by the teachers and evaluated. The results shows fair degree of accuracy, which may further be improved by adding few more features.

Samvidha: A ICT system for personalized offline Internet Access for rural schools

Internet is a huge repository of quality learning materials and continues to grow in a faster rate. The school students may be benefited immensely as these learning materials may well supplement their curricular requirements. But Access to the Internet is costly, because it is very expensive to maintain a persistent Internet connection. For some schools in the developing countries like India, this cost may not be affordable specifically in rural schools. This makes way to a digital divide between the rural and urban schools which is unwanted. For these rural schools, limiting the amount of bandwidth consumed is of paramount importance. It is necessary that the schools be connected to the Internet for the least time, in order to minimize the access cost. In this paper, we present a system Samvidha that allows the rural school students to access the Internet contents in an offline fashion.

Automatic Generation and Insertion of Assessment Items in Online Video Courses

In this paper, we propose a prototype system for automatic generation and insertion of assessment items in online video courses. The proposed system analyzes text transcript of a requested video lecture to suggest self-assessment items in runtime through automatic discourse segmentation and question generation. To deal with the problem of question generation from noisy transcription, the system relies on semantically similar Wikipedia text segments. We base our study on a popular video lecture portal - National Programme on Technology Enhanced Learning (NPTEL). However, it can be adapted to other portals as well.

Sahayika: A framework for participatory authoring of knowledge structures for education domain

In countries like India, a great deal of diversity exists in language, culture and socio-economic conditions. In order to deliver computer aided education, efforts have to be put for proper management of concerned domain and learning materials keeping in mind this diversity. Participatory authoring of domain knowledge structure is of immense importance in this regard. In this paper, we describe a knowledge structure which is effective in capturing the structure of the learning materials of school level subjects. Ontologies have gained importance in representing the knowledge of the domain in a formal and machine understandable form in areas like intelligent information processing. Thus it can provide the platform for effective extraction of information and many other applications. We describe different aspects of manual ontology engineering in developing application specific domain ontology. The domain of our interest is the education domain where we are interested in retrieving relevant Web documents for the curriculum related requirements of school students. We identify an effective way of structuring the knowledge about these domains, which allows us to clearly demarcate the roles of topics, concepts, and actual words. We also describe applications in the area of information retrieval and in indexing document repositories in connection with e-learning where our ontology plays an important role. In this paper, we have provided a framework, Sahayika, for building knowledge structures in education domain.

Surrogator: A Tool to Enrich a Digital Library with Open Access Surrogate Resources

Large digital libraries often index articles without curating their digital copies in their own repositories. Examples include the National Digital Library of India (NDLI) and ACM Digital Library. Full text view generally requires subscription to libraries that host the contents. The problem is particularly severe for researchers, given high journal subscription charges. However, authors often keep a free copy in preprint servers. Sometimes a conference paper behind a paywall has a closely resembling journal version freely available on the Web. These open access surrogates are immensely valuable to researchers who cannot afford to access the original publications. We present a lightweight tool called Surrogator to automatically identify open access surrogates of access-restricted scholarly papers present in a digital library. Its focus on approximate matches makes it different from many existing applications. In this poster, we describe the design and interface of the tool and our initial experiences of using it on articles indexed in NDLI.

Learning to Extract Comparison Points of Entity Pairs from Wikipedia Articles

In this paper, we present preliminary results on a novel task of extracting comparison points for a pair of entities from the text articles describing them. The task is challenging as comparison points in a typical pair of articles tend to be sparse. We presented a multi-level document analysis (viz. document, paragraph and sentence level) for extracting the comparisons. For extracting sentence level comparisons, which is the hardest task among three, we have used Convolutional Neural Network (CNN) with features extracted around triple. Experiments conducted on a small dataset provide encouraging performance.