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

arXiv: https://arxiv.org/abs/1809.01446 · HTML: https://mlsanity.com/paper/1809.01446/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2018.

## Built on by (most cited first, 8 of 8)

- [Automatic Speech Recognition in Sanskrit: A New Speech Corpus and Modelling Insights](https://mlsanity.com/paper/2106.05852/) (2021)
- [TransLIST: A Transformer-Based Linguistically Informed Sanskrit Tokenizer](https://mlsanity.com/paper/2210.11753/) (2022)
- [Evaluating Neural Word Embeddings for Sanskrit](https://mlsanity.com/paper/2104.00270/) (2021)
- [SanskritShala: A Neural Sanskrit NLP Toolkit with Web-Based Interface for Pedagogical and Annotation Purposes](https://mlsanity.com/paper/2302.09527/) (2023)
- [S=amayik: A Benchmark and Dataset for English-Sanskrit Translation](https://mlsanity.com/paper/2305.14004/) (2023)
- [Vedavani: A Benchmark Corpus for ASR on Vedic Sanskrit Poetry](https://mlsanity.com/paper/2506.00145/) (2025)
- [ARISE: Iterative Rule Induction and Synthetic Data Generation for Text Classification](https://mlsanity.com/paper/2502.05923/) (2025)
- [A Word-Level Digital Reader of the Prasthanatrayi with Sankara's Bhasya: Corpus, Method, and an Open, Offline Reading Aid for the Advaita Vedanta Canon](https://mlsanity.com/paper/2607.07282/) (2026)

## Abstract (arXiv)

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.