Amrith Krishna

A Dataset for Sanskrit Word Segmentation

The last decade saw a surge in digitisation efforts for ancient manuscripts in Sanskrit. Due to various linguistic peculiarities inherent to the language, even the preliminary tasks such as word segmentation are non-trivial in Sanskrit. Elegant models for Word Segmentation in Sanskrit are indispensable for further syntactic and semantic processing of the manuscripts. Current works in word segmentation for Sanskrit, though commendable in their novelty, often have variations in their objective and evaluation criteria. In this work, we set the record straight. We formally define the objectives and the requirements for the word segmentation task. In order to encourage research in the field and to alleviate the time and effort required in pre-processing, we release a dataset of 115,000 sentences for word segmentation. For each sentence in the dataset we include the input character sequence, ground truth segmentation, and additionally lexical and morphological information about all the phonetically possible segments for the given sentence. In this work, we also discuss the linguistic considerations made while generating the candidate space of the possible segments.

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.

A Graph Based Semi-Supervised Approach for Analysis of Derivational Nouns in Sanskrit.

Derivational nouns are widely used in Sanskrit corpora and represent an important cornerstone of productivity in the language. Currently there exists no analyser that identifies the derivational nouns. We propose a semi supervised approach for identification of derivational nouns in Sanskrit. We not only identify the derivational words, but also link them to their corresponding source words. Our novelty comes in the design of the network structure for the task. The edge weights are featurised based on the phonetic, morphological, syntactic and the semantic similarity shared between the words to be identified. We find that our model is effective for the task, even when we employ a labelled dataset which is only 5 % to that of the entire dataset.