# LGDE: Local Graph-based Dictionary Expansion

arXiv: https://arxiv.org/abs/2405.07764 · HTML: https://mlsanity.com/paper/2405.07764/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 63 references that are in the map.
- First posted on arXiv 13 May 2024.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Probing Pretrained Language Models for Lexical Semantics](https://mlsanity.com/paper/2010.05731/) (2020)
- [Query2doc: Query Expansion with Large Language Models](https://mlsanity.com/paper/2303.07678/) (2023)
- [A Benchmark Dataset for Learning to Intervene in Online Hate Speech](https://mlsanity.com/paper/1909.04251/) (2019)
- [SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection](https://mlsanity.com/paper/2007.11464/) (2020)
- [Query Expansion by Prompting Large Language Models](https://mlsanity.com/paper/2305.03653/) (2023)
- [BERT-QE: Contextualized Query Expansion for Document Re-ranking](https://mlsanity.com/paper/2009.07258/) (2020)
- [Query Expansion with Locally-Trained Word Embeddings](https://mlsanity.com/paper/1605.07891/) (2016)
- [Corpus-Steered Query Expansion with Large Language Models](https://mlsanity.com/paper/2402.18031/) (2024)
- [Topology of Word Embeddings: Singularities Reflect Polysemy](https://mlsanity.com/paper/2011.09413/) (2020)
- [Using Word Embeddings for Automatic Query Expansion](https://mlsanity.com/paper/1606.07608/) (2016)
- [A Triangle Inequality for Cosine Similarity](https://mlsanity.com/paper/2107.04071/) (2021)
- [The many facets of community detection in complex networks](https://mlsanity.com/paper/1611.07769/) (2016)
- [Mittens: An Extension of GloVe for Learning Domain-Specialized Representations](https://mlsanity.com/paper/1803.09901/) (2018)
- [Graph-based data clustering via multiscale community detection](https://mlsanity.com/paper/1909.04491/) (2019)
- [Geometric graphs from data to aid classification tasks with graph convolutional networks](https://mlsanity.com/paper/2005.04081/) (2020)
- [From Free Text to Clusters of Content in Health Records: An Unsupervised Graph Partitioning Approach](https://mlsanity.com/paper/1811.05711/) (2018)
- [Community as a Vague Operator: Epistemological Questions for a Critical Heuristics of Community Detection Algorithms](https://mlsanity.com/paper/2210.02753/) (2022)
- [Severability of mesoscale components and local time scales in dynamical networks](https://mlsanity.com/paper/2006.02972/) (2020)

## Abstract (arXiv)

We present Local Graph-based Dictionary Expansion (LGDE), a method for data-driven discovery of the semantic neighbourhood of words using tools from manifold learning and network science. At the heart of LGDE lies the creation of a word similarity graph from the geometry of word embeddings followed by local community detection based on graph diffusion. The diffusion in the local graph manifold allows the exploration of the complex nonlinear geometry of word embeddings to capture word similarities based on paths of semantic association, over and above direct pairwise similarities. Exploiting such semantic neighbourhoods enables the expansion of dictionaries of pre-selected keywords, an important step for tasks in information retrieval, such as database queries and online data collection. We validate LGDE on two user-generated English-language corpora and show that LGDE enriches the list of keywords with improved performance relative to methods based on direct word similarities or co-occurrences. We further demonstrate our method through a real-world use case from communication science, where LGDE is evaluated quantitatively on the expansion of a conspiracy-related dictionary from online data collected and analysed by domain experts. Our empirical results and expert user assessment indicate that LGDE expands the seed dictionary with more useful keywords due to the manifold-learning-based similarity network.