---
title: "Better Word Representations with Recursive — Luong 2013"
description: "Standard word embeddings give every surface form its own vector, ignoring links between related forms such as \"distinct\" and \"distinctiveness\"."
authors: ["Thang Luong", "Richard Socher", "Christopher Manning"]
year: 2013
venue: "Proceedings of the Seventeenth Conference on Computational Natural Language Learning"
source: "https://aclanthology.org/W13-3512/"
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/luong-2013-better-word-representations-recursive-neural/"
---
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# Better Word Representations with Recursive Neural Networks for Morphology

**Authors:** [Thang Luong](https://md.research.elamur.ai/free/en/authors/thang-luong/), [Richard Socher](https://md.research.elamur.ai/free/en/authors/richard-socher/), [Christopher Manning](https://md.research.elamur.ai/free/en/authors/christopher-manning/) · **Year:** 2013 · **Venue:** Proceedings of the Seventeenth Conference on Computational Natural Language Learning · **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/W13-3512/) · [PDF](https://aclanthology.org/W13-3512.pdf) · [OpenAlex](https://openalex.org/W2251012068)

**Citations:** 769 citations (openalex-snapshot · 2026-10-14) · [248](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
Standard word embeddings give every surface form its own vector, ignoring links between related forms such as "distinct" and "distinctiveness". As a result, rare and morphologically complex words get weak vectors, and unseen words collapse into a generic placeholder. Luong, Socher and Manning propose morphological Recursive Neural Networks (morphoRNN), which use morphemes rather than whole words as the atomic units. Word vectors are assembled recursively by merging stem and affix vectors through a shared composition layer.

The paper presents two variants. The context-insensitive cimRNN learns to reconstruct existing pretrained embeddings (C&W, HSMN) from their morphemes. The context-sensitive csmRNN embeds this composition inside a neural language model trained on Wikipedia text, so gradients from context reach the morpheme level.

The authors also release a new Rare Word (RW) similarity dataset. On most benchmarks, csmRNN beats the original embeddings by a clear margin, and the gains are largest on rare words. The approach can build vectors for any unseen word made of known morphemes, which makes it promising for morphologically richer languages.

## Key points
- The composition step computes a parent vector as tanh(W_m[x_stem; x_affix] + b_m). It is applied recursively, so "unfortunately" is built from un + fortunate + ly.
- On the RW dataset (Spearman ρ×100), csmRNN raised HSMN from 1.97 to 22.31 and C&W from 26.75 to 34.36.
- On WS353, csmRNN improved C&W from 49.77 to 57.01 and HSMN from 62.58 to 64.58. MC and RG also improved.
- SCWS* was the exception: csmRNN did not beat the C&W baseline there (48.48 vs 48.59).
- The RW dataset and trained vectors are released at nlp.stanford.edu/~lmthang/morphoNLM.

*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/)

## Cited by

Cited by

248 papers of our library cite it (of the 140884 whose bibliographies we read); 20 of them have a page here, 228 not yet.

- [Morphological Word Embeddings for Arabic Neural Machine Translation in Low-Resource Settings](https://md.research.elamur.ai/free/en/papers/aclbulk-2018-shapiro-morphological-word-embeddings-arabic-neural/)
- [Massively Multilingual Word Embeddings](https://md.research.elamur.ai/free/en/papers/ammar-2016-massively-multilingual-word-embeddings/)
- [Analysis Methods in Neural Language Processing: A Survey](https://md.research.elamur.ai/free/en/papers/belinkov-2019-analysis-methods-neural-language-processing/)
- [A Character-level Decoder without Explicit Segmentation for Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/chung-2016-character-level-decoder-without-explicit/)
- [Rethinking embedding coupling in pre-trained language models](https://md.research.elamur.ai/free/en/papers/chung-2020-rethinking-embedding-coupling-pre-trained/)
- [A Distributional and Orthographic Aggregation Model for English Derivational Morphology](https://md.research.elamur.ai/free/en/papers/deutsch-2018-distributional-orthographic-aggregation-model-english/)
- [Problems With Evaluation of Word Embeddings Using Word Similarity Tasks](https://md.research.elamur.ai/free/en/papers/faruqui-2016-problems-evaluation-word-embeddings-word/)
- [SimVerb-3500: A Large-Scale Evaluation Set of Verb Similarity](https://md.research.elamur.ai/free/en/papers/gerz-2016-simverb-3500-large-scale-evaluation/)
- [SimLex-999: Evaluating Semantic Models With (Genuine) Similarity Estimation](https://md.research.elamur.ai/free/en/papers/hill-2015-simlex-999-evaluating-semantic-models/)
- [Character-Aware Neural Language Models](https://md.research.elamur.ai/free/en/papers/kim-2015-character-aware-neural-language-models/)
- [Probabilistic Modelling of Morphologically Rich Languages](https://md.research.elamur.ai/free/en/papers/langstream-2015-botha-probabilistic-modelling-morphologically-rich-languages/)
- [Superbizarre Is Not Superb: Derivational Morphology Improves BERT's Interpretation of Complex Words](https://md.research.elamur.ai/free/en/papers/lex-2021-hofmann-superbizarre-derivational/)
- [Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation](https://md.research.elamur.ai/free/en/papers/ling-2015-finding-function-form-compositional-character/)
- [Bilingual Word Representations with Monolingual Quality in Mind](https://md.research.elamur.ai/free/en/papers/luong-2015-bilingual-word-representations-monolingual-quality/)
- [Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models](https://md.research.elamur.ai/free/en/papers/luong-2016-achieving-open-vocabulary-neural-machine/)
- [Advances in Pre-Training Distributed Word Representations](https://md.research.elamur.ai/free/en/papers/mikolov-2017-advances-pre-training-distributed-word/)
- [Using the Output Embedding to Improve Language Models](https://md.research.elamur.ai/free/en/papers/press-2017-output-embedding-improve-language-models/)
- [Improving Lexical Embeddings with Semantic Knowledge](https://md.research.elamur.ai/free/en/papers/yu-2014-improving-lexical-embeddings-semantic-knowledge/)
- [Learning Gender-Neutral Word Embeddings](https://md.research.elamur.ai/free/en/papers/zhao-2018-learning-gender-neutral-word-embeddings/)
- [Bilingual Word Embeddings for Phrase-Based Machine Translation](https://md.research.elamur.ai/free/en/papers/zou-2013-bilingual-word-embeddings-phrase-based/)

## Cite

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

```bibtex
@inproceedings{luong2013better,
  title = {{Better Word Representations with Recursive Neural Networks for Morphology}},
  author = {Luong, Thang and Socher, Richard and Manning, Christopher},
  booktitle = {Proceedings of the Seventeenth Conference on Computational Natural Language Learning},
  year = {2013},
  url = {https://aclanthology.org/W13-3512/}
}
```

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37. R. Socher, J. Pennington, E. H. Huang, A. Y. Ng, and C. D. Manning. 2011c. Semi-supervised recursive autoencoders for predicting sentiment distributions. In EMNLP. [find](https://scholar.google.com/scholar?q=R%2E+Socher%2C+J%2E+Pennington%2C+E%2E+H%2E+Huang%2C+A%2E+Y%2E+Ng%2C+and+C%2E+D%2E+Manning%2E+2011c%2E+Semi%2Dsupervised+recursive+autoencoders+for+predicting+sentiment+distributions%2E+In+EMNLP%2E) [our card](https://md.research.elamur.ai/free/en/papers/socher-2011-semi-supervised-recursive-autoencoders-predicting/)
38. R. Socher, B. Huval, C. D. Manning, and A. Y. Ng. 2012. Semantic compositionality through recursive matrix-vector spaces. In EMNLP. [find](https://scholar.google.com/scholar?q=R%2E+Socher%2C+B%2E+Huval%2C+C%2E+D%2E+Manning%2C+and+A%2E+Y%2E+Ng%2E+2012%2E+Semantic+compositionality+through+recursive+matrix%2Dvector+spaces%2E+In+EMNLP%2E) [our card](https://md.research.elamur.ai/free/en/papers/socher-2012-semantic-compositionality-through-recursive-matrix/)
39. J. Turian, L. Ratinov, and Y. Bengio. 2010. Word representations: a simple and general method for semi-supervised learning. In ACL. [find](https://scholar.google.com/scholar?q=J%2E+Turian%2C+L%2E+Ratinov%2C+and+Y%2E+Bengio%2E+2010%2E+Word+representations%3A+a+simple+and+general+method+for+semi%2Dsupervised+learning%2E+In+ACL%2E) [our card](https://md.research.elamur.ai/free/en/papers/turian-2010-word-representations-simple-general-method/)