Free as in Free Word Order: An Energy Based Model for Word Segmentation and Morphological Tagging in Sanskrit

TL;DR

This paper introduces an energy-based structured prediction model for joint word segmentation and morphological tagging in Sanskrit, leveraging sentence-wide context and graph-based parsing to outperform existing methods with less training data.

Contribution

It presents a novel graph-based energy model that jointly handles segmentation and tagging in Sanskrit, improving accuracy over state-of-the-art approaches with reduced data requirements.

Findings

Achieved an F-Score of 96.92%, a 7.06% improvement.

Graph-based approach increased segmentation F-Score by 12.6%.

Outperformed previous methods using less than 10% of training data.

Abstract

The configurational information in sentences of a free word order language such as Sanskrit is of limited use. Thus, the context of the entire sentence will be desirable even for basic processing tasks such as word segmentation. We propose a structured prediction framework that jointly solves the word segmentation and morphological tagging tasks in Sanskrit. We build an energy based model where we adopt approaches generally employed in graph based parsing techniques (McDonald et al., 2005a; Carreras, 2007). Our model outperforms the state of the art with an F-Score of 96.92 (percentage improvement of 7.06%) while using less than one-tenth of the task-specific training data. We find that the use of a graph based ap- proach instead of a traditional lattice-based sequential labelling approach leads to a percentage gain of 12.6% in F-Score for the segmentation task.