# Playing Codenames with Language Graphs and Word Embeddings

arXiv: https://arxiv.org/abs/2105.05885 · HTML: https://mlsanity.com/paper/2105.05885/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 12 May 2021.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing](https://mlsanity.com/paper/1907.04433/) (2019)
- [The Text-Based Adventure AI Competition](https://mlsanity.com/paper/1808.01262/) (2018)
- [Comparing Models of Associative Meaning: An Empirical Investigation of Reference in Simple Language Games](https://mlsanity.com/paper/1810.03717/) (2018)

## Built on by (most cited first, 6 of 6)

- [Modeling Cross-Cultural Pragmatic Inference with Codenames Duet](https://mlsanity.com/paper/2306.02475/) (2023)
- [Codenames as a Benchmark for Large Language Models](https://mlsanity.com/paper/2412.11373/) (2024)
- [Communicate to Play: Pragmatic Reasoning for Efficient Cross-Cultural Communication in Codenames](https://mlsanity.com/paper/2408.04900/) (2024)
- [Towards automating Codenames spymasters with deep reinforcement learning](https://mlsanity.com/paper/2212.14104/) (2022)
- [Adapting to Teammates in a Cooperative Language Game](https://mlsanity.com/paper/2403.00823/) (2024)
- [Ad-hoc Concept Forming in the Game Codenames as a Means for Evaluating Large Language Models](https://mlsanity.com/paper/2502.11707/) (2025)

## Abstract (arXiv)

Although board games and video games have been studied for decades in artificial intelligence research, challenging word games remain relatively unexplored. Word games are not as constrained as games like chess or poker. Instead, word game strategy is defined by the players' understanding of the way words relate to each other. The word game Codenames provides a unique opportunity to investigate common sense understanding of relationships between words, an important open challenge. We propose an algorithm that can generate Codenames clues from the language graph BabelNet or from any of several embedding methods - word2vec, GloVe, fastText or BERT. We introduce a new scoring function that measures the quality of clues, and we propose a weighting term called DETECT that incorporates dictionary-based word representations and document frequency to improve clue selection. We develop BabelNet-Word Selection Framework (BabelNet-WSF) to improve BabelNet clue quality and overcome the computational barriers that previously prevented leveraging language graphs for Codenames. Extensive experiments with human evaluators demonstrate that our proposed innovations yield state-of-the-art performance, with up to 102.8% improvement in precision@2 in some cases. Overall, this work advances the formal study of word games and approaches for common sense language understanding.