# Text Segmentation based on Semantic Word Embeddings

arXiv: https://arxiv.org/abs/1503.05543 · HTML: https://mlsanity.com/paper/1503.05543/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2015.

## Built on by (most cited first, 11 of 11)

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- [A Comparative Study of Sentence Embedding Models for Assessing Semantic Variation](https://mlsanity.com/paper/2308.04625/) (2023)
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- [GPT-Calls: Enhancing Call Segmentation and Tagging by Generating Synthetic Conversations via Large Language Models](https://mlsanity.com/paper/2306.07941/) (2023)
- [The impact of fine tuning in LLaMA on hallucinations for named entity extraction in legal documentation](https://mlsanity.com/paper/2506.08827/) (2025)
- [SegNSP: Revisiting Next Sentence Prediction for Linear Text Segmentation](https://mlsanity.com/paper/2601.03474/) (2026)
- [Evaluation of Chunking Strategies for Effective Text Embedding in Low-Resource Language on Agricultural Documents](https://mlsanity.com/paper/2605.22203/) (2026)
- [SemChunk-C: Semantic Segmentation for C Code](https://mlsanity.com/paper/2606.23697/) (2026)

## Abstract (arXiv)

We explore the use of semantic word embeddings in text segmentation algorithms, including the C99 segmentation algorithm and new algorithms inspired by the distributed word vector representation. By developing a general framework for discussing a class of segmentation objectives, we study the effectiveness of greedy versus exact optimization approaches and suggest a new iterative refinement technique for improving the performance of greedy strategies. We compare our results to known benchmarks, using known metrics. We demonstrate state-of-the-art performance for an untrained method with our Content Vector Segmentation (CVS) on the Choi test set. Finally, we apply the segmentation procedure to an in-the-wild dataset consisting of text extracted from scholarly articles in the arXiv.org database.