---
title: "A Unified Model for Word Sense Representation — Chen 2014"
description: "Standard word embeddings give each word one vector, which blends the meanings of ambiguous words."
authors: ["Xinxiong Chen", "Zhiyuan Liu", "Maosong Sun"]
year: 2014
venue: "Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)"
source: "https://aclanthology.org/D14-1110/"
doi: "10.3115/v1/d14-1110"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/chen-2014-unified-model-word-sense-representation/"
---
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# A Unified Model for Word Sense Representation and Disambiguation

**Authors:** Xinxiong Chen, [Zhiyuan Liu](https://md.research.elamur.ai/free/en/authors/zhiyuan-liu/), [Maosong Sun](https://md.research.elamur.ai/free/en/authors/maosong-sun/) · **Year:** 2014 · **Venue:** Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/D14-1110/) · [PDF](https://aclanthology.org/D14-1110.pdf) · [DOI 10.3115/v1/d14-1110](https://doi.org/10.3115/v1/d14-1110) · [OpenAlex](https://openalex.org/W2251537235)

**Citations:** 265 citations (openalex-snapshot · 2026-10-14) · [93](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
Standard word embeddings give each word one vector, which blends the meanings of ambiguous words. Earlier multi-prototype approaches (Reisinger and Mooney, 2010; Huang et al., 2012) induce senses by clustering contexts. This has three drawbacks: the number of clusters must be chosen in advance, training runs offline, and the resulting prototypes are not linked to any dictionary sense, so they cannot be used directly for disambiguation.

Chen, Liu and Sun propose a WordNet-anchored framework in which learning sense vectors (WSR) and resolving which sense a word takes in context (WSD) reinforce one another. The method has three steps:
1. Train word vectors with Skip-gram.
2. Initialise each sense vector from gloss words that are similar to the target word, then use a knowledge-based disambiguator to label raw text with senses.
3. Retrain with a modified Skip-gram objective that learns word and sense vectors jointly from the disambiguated occurrences.

The model improves agreement with human judgments on contextual word similarity compared with earlier WSR methods. It outperforms state-of-the-art supervised systems on domain-specific WSD and is competitive on coarse-grained all-words WSD. Because it needs only unlabeled text and a sense inventory with definitions, it can be applied to other lexicons.

## Key points
- **Sense vector initialisation.** The gloss is filtered to content words (noun, verb, adjective, adverb), excluding the target word itself. A word is kept only if its cosine similarity to the target exceeds a threshold δ, and the kept vectors are averaged.
  - For *bank* (sloping-land sense) with δ = 0.05, the kept words are sloping, land, slope, water, canoe, sat and river.
  - Weakly related words such as body, pulled, watch and currents are dropped.
- **Two disambiguation algorithms.** Both start from a context vector equal to the mean of the sentence's content-word vectors, skip monosemous and non-content words, and choose the sense with the highest cosine similarity to the context.
  - L2R processes words in natural left-to-right order.
  - S2C processes words with fewer WordNet senses first; for example, *lake* (3 senses) is handled before *bank* and *sat* (10 senses each).
- **Confidence-gated context update.** If the best sense beats the runner-up by more than a margin ε, that sense vector replaces the word vector inside the context. This sharpens the context for words still waiting to be disambiguated.
- **Evaluation.** The model is tested on three tasks: contextual word similarity, domain-specific WSD and coarse-grained all-words WSD. The learned sense representations are released for download.
- **Inventory independence.** WordNet is used in the experiments, but the authors state that any resource with sense distinctions and definitions could serve as input.

## Interesting ideas
- **Similarity-filtered gloss averaging.** Building a starting vector for a dictionary entry from its definition, while discarding definition words unrelated to the headword, is a cheap way to seed sense or concept embeddings. It suits any language that has a dictionary but no sense-annotated corpus.
- **Easiest-first decoding.** Resolving low-ambiguity items before high-ambiguity ones, so that confident decisions inform harder ones, avoids searching over all sense combinations. The same ordering heuristic could apply to entity linking or toponym resolution.
- **Margin-gated self-training.** Only decisions with a clear winner are allowed to update the shared context. This limits error propagation when a system bootstraps from its own predictions, and the idea transfers to other iterative labelling loops.
- **Mutual bootstrapping of representation and labelling.** Better vectors produce better automatic sense labels, and better labels produce better vectors. This loop turns unlabeled text plus a lexicon into training data without manual annotation.
- **Anchoring vectors to inventory entries.** Tying each vector to an explicit sense ID, rather than an anonymous cluster, fixes the number of senses per word from the resource. It also makes outputs interpretable and lets a new sense be added just by writing its definition.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

Topics: [Word sense disambiguation](https://md.research.elamur.ai/free/en/topics/word-sense-disambiguation/)

## Cited by

Cited by

93 papers of our library cite it (of the 140884 whose bibliographies we read); 7 of them have a page here, 86 not yet.

- [SensEmbed: Learning Sense Embeddings for Word and Relational Similarity](https://md.research.elamur.ai/free/en/papers/iacobacci-2015-sensembed-learning-sense-embeddings-word/)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://md.research.elamur.ai/free/en/papers/krishna-2016-visual-genome-connecting-language-vision/)
- [Large Scale Substitution-based Word Sense Induction](https://md.research.elamur.ai/free/en/papers/lex-2022-eyal-substitution-wsi/)
- [Automatic Thesaurus Construction for Modern Hebrew](https://md.research.elamur.ai/free/en/papers/lingbulk-2018-liebeskind-automatic-thesaurus-construction-modern-hebrew/)
- [context2vec: Learning Generic Context Embedding with Bidirectional LSTM](https://md.research.elamur.ai/free/en/papers/melamud-2016-context2vec-learning-generic-context-embedding/)
- [Ontology-Aware Token Embeddings for Prepositional Phrase Attachment](https://md.research.elamur.ai/free/en/papers/onto-2017-dasigi-ontology-aware-token-embeddings-prepositional/)
- [Word Sense Disambiguation: A Unified Evaluation Framework and Empirical Comparison](https://md.research.elamur.ai/free/en/papers/onto-2017-raganato-word-sense-disambiguation-unified-evaluation/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{chen2014unified,
  title = {{A Unified Model for Word Sense Representation and Disambiguation}},
  author = {Chen, Xinxiong and Liu, Zhiyuan and Sun, Maosong},
  booktitle = {Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year = {2014},
  doi = {10.3115/v1/d14-1110},
  url = {https://doi.org/10.3115/v1/d14-1110}
}
```

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