# Sentence Embeddings and High-speed Similarity Search for Fast Computer Assisted Annotation of Legal Documents

arXiv: https://arxiv.org/abs/2112.11494 · HTML: https://mlsanity.com/paper/2112.11494/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 26 references that are in the map.
- First posted on arXiv 21 Dec 2021.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [Supervised Learning of Universal Sentence Representations from Natural Language Inference Data](https://mlsanity.com/paper/1705.02364/) (2017)
- [Identification of Rhetorical Roles of Sentences in Indian Legal Judgments](https://mlsanity.com/paper/1911.05405/) (2019)
- [Autonomy and Reliability of Continuous Active Learning for Technology-Assisted Review](https://mlsanity.com/paper/1504.06868/) (2015)
- [Computer-Assisted Creation of Boolean Search Rules for Text Classification in the Legal Domain](https://mlsanity.com/paper/2112.05807/) (2021)

## Built on by (most cited first, 9 of 9)

- [Unlocking Practical Applications in Legal Domain: Evaluation of GPT for Zero-Shot Semantic Annotation of Legal Texts](https://mlsanity.com/paper/2305.04417/) (2023)
- [Can GPT-4 Support Analysis of Textual Data in Tasks Requiring Highly Specialized Domain Expertise?](https://mlsanity.com/paper/2306.13906/) (2023)
- [Using Large Language Models to Support Thematic Analysis in Empirical Legal Studies](https://mlsanity.com/paper/2310.18729/) (2023)
- [From Text to Structure: Using Large Language Models to Support the Development of Legal Expert Systems](https://mlsanity.com/paper/2311.04911/) (2023)
- [JusticeBot: A Methodology for Building Augmented Intelligence Tools for Laypeople to Increase Access to Justice](https://mlsanity.com/paper/2308.02032/) (2023)
- [LLMediator: GPT-4 Assisted Online Dispute Resolution](https://mlsanity.com/paper/2307.16732/) (2023)
- [Data-Centric Machine Learning in the Legal Domain](https://mlsanity.com/paper/2201.06653/) (2022)
- [Toward an Intelligent Tutoring System for Argument Mining in Legal Texts](https://mlsanity.com/paper/2210.13635/) (2022)
- [Can Generative Pre-trained Transformers (GPT) Pass Assessments in Higher Education Programming Courses?](https://mlsanity.com/paper/2303.09325/) (2023)

## Abstract (arXiv)

Human-performed annotation of sentences in legal documents is an important prerequisite to many machine learning based systems supporting legal tasks. Typically, the annotation is done sequentially, sentence by sentence, which is often time consuming and, hence, expensive. In this paper, we introduce a proof-of-concept system for annotating sentences "laterally." The approach is based on the observation that sentences that are similar in meaning often have the same label in terms of a particular type system. We use this observation in allowing annotators to quickly view and annotate sentences that are semantically similar to a given sentence, across an entire corpus of documents. Here, we present the interface of the system and empirically evaluate the approach. The experiments show that lateral annotation has the potential to make the annotation process quicker and more consistent.