# Learning to Understand Phrases by Embedding the Dictionary

arXiv: https://arxiv.org/abs/1504.00548 · HTML: https://mlsanity.com/paper/1504.00548/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2015.

## Built on by (most cited first, 30 of 33)

- [DefSent: Sentence Embeddings using Definition Sentences](https://mlsanity.com/paper/2105.04339/) (2021)
- [Semeval-2022 Task 1: CODWOE -- Comparing Dictionaries and Word Embeddings](https://mlsanity.com/paper/2205.13858/) (2022)
- [WinoDict: Probing language models for in-context word acquisition](https://mlsanity.com/paper/2209.12153/) (2022)
- [Learning Disentangled Representations for Natural Language Definitions](https://mlsanity.com/paper/2210.02898/) (2022)
- [Automated Crossword Solving](https://mlsanity.com/paper/2205.09665/) (2022)
- [Extracting Sentence Embeddings from Pretrained Transformer Models](https://mlsanity.com/paper/2408.08073/) (2024)
- [On the Tip of the Tongue: Analyzing Conceptual Representation in Large Language Models with Reverse-Dictionary Probe](https://mlsanity.com/paper/2402.14404/) (2024)
- [Revealing emergent human-like conceptual representations from language prediction](https://mlsanity.com/paper/2501.12547/) (2025)
- [A Unified Model for Reverse Dictionary and Definition Modelling](https://mlsanity.com/paper/2205.04602/) (2022)
- [Lacking the embedding of a word? Look it up into a traditional dictionary](https://mlsanity.com/paper/2109.11763/) (2021)
- [Multi-Relational Hyperbolic Word Embeddings from Natural Language Definitions](https://mlsanity.com/paper/2305.07303/) (2023)
- ["Definition Modeling: To model definitions." Generating Definitions With Little to No Semantics](https://mlsanity.com/paper/2306.08433/) (2023)
- [Rapid Word Learning Through Meta In-Context Learning](https://mlsanity.com/paper/2502.14791/) (2025)
- [Updater-Extractor Architecture for Inductive World State Representations](https://mlsanity.com/paper/2104.05500/) (2021)
- [WordAlchemy: A transformer-based Reverse Dictionary](https://mlsanity.com/paper/2204.10181/) (2022)
- [Forming Trees with Treeformers](https://mlsanity.com/paper/2207.06960/) (2022)
- [When Large Language Models Meet Law: Dual-Lens Taxonomy, Technical Advances, and Ethical Governance](https://mlsanity.com/paper/2507.07748/) (2025)
- [PREDICT: Persian Reverse Dictionary](https://mlsanity.com/paper/2105.00309/) (2021)
- [Spanish Built Factual Freectianary (Spanish-BFF): the first AI-generated free dictionary](https://mlsanity.com/paper/2302.12746/) (2023)
- [CuRIAM: Corpus re Interpretation and Metalanguage in U.S. Supreme Court Opinions](https://mlsanity.com/paper/2305.14719/) (2023)
- [S~onajaht: Definition Embeddings and Semantic Search for Reverse Dictionary Creation](https://mlsanity.com/paper/2404.19430/) (2024)
- [Automatic Construction of Sememe Knowledge Bases via Dictionaries](https://mlsanity.com/paper/2105.12585/) (2021)
- [IRB-NLP at SemEval-2022 Task 1: Exploring the Relationship Between Words and Their Semantic Representations](https://mlsanity.com/paper/2205.06840/) (2022)
- [3D-EX : A Unified Dataset of Definitions and Dictionary Examples](https://mlsanity.com/paper/2308.03043/) (2023)
- [Multilingual Definition Modeling](https://mlsanity.com/paper/2506.01489/) (2025)
- [Idiomify -- Building a Collocation-supplemented Reverse Dictionary of English Idioms with Word2Vec for non-native learners](https://mlsanity.com/paper/2204.05634/) (2022)
- [1Cademy at Semeval-2022 Task 1: Investigating the Effectiveness of Multilingual, Multitask, and Language-Agnostic Tricks for the Reverse Dictionary Task](https://mlsanity.com/paper/2206.03702/) (2022)
- [Rosetta Stone at KSAA-RD Shared Task: A Hop From Language Modeling To Word--Definition Alignment](https://mlsanity.com/paper/2310.15823/) (2023)
- [Towards a Transformer-Based Reverse Dictionary Model for Quality Estimation of Definitions](https://mlsanity.com/paper/2311.02985/) (2023)
- [Building another Spanish dictionary, this time with GPT-4](https://mlsanity.com/paper/2406.11218/) (2024)

## Abstract (arXiv)

Distributional models that learn rich semantic word representations are a success story of recent NLP research. However, developing models that learn useful representations of phrases and sentences has proved far harder. We propose using the definitions found in everyday dictionaries as a means of bridging this gap between lexical and phrasal semantics. Neural language embedding models can be effectively trained to map dictionary definitions (phrases) to (lexical) representations of the words defined by those definitions. We present two applications of these architectures: "reverse dictionaries" that return the name of a concept given a definition or description and general-knowledge crossword question answerers. On both tasks, neural language embedding models trained on definitions from a handful of freely-available lexical resources perform as well or better than existing commercial systems that rely on significant task-specific engineering. The results highlight the effectiveness of both neural embedding architectures and definition-based training for developing models that understand phrases and sentences.