---
title: "A Primer on Neural Network Models for Natural Language"
description: "For years, NLP relied on linear classifiers such as SVMs and logistic regression applied to huge… 150 references, cited by 102 papers here."
authors: ["Yoav Goldberg"]
year: 2015
venue: "arXiv:1510.00726"
source: "https://arxiv.org/abs/1510.00726"
doi: "10.48550/arxiv.1510.00726"
arxiv: "1510.00726"
licence: "arXiv — non-exclusive license to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/goldberg-2015-primer-neural-network-models-natural/"
---
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# A Primer on Neural Network Models for Natural Language Processing

**Authors:** [Yoav Goldberg](https://md.research.elamur.ai/free/en/authors/yoav-goldberg/) · **Year:** 2015 · **Venue:** [arXiv:1510.00726](https://md.research.elamur.ai/free/en/venues/journal-of-artificial-intelligence-research/) ([Journal of Artificial Intelligence Research 2015](https://md.research.elamur.ai/free/en/venues/journal-of-artificial-intelligence-research/2015/)) · **Publisher:** [AI Access Foundation](https://md.research.elamur.ai/free/en/publishers/ai-access-foundation/) · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/1510.00726) · [PDF](https://arxiv.org/pdf/1510.00726) · [DOI 10.48550/arxiv.1510.00726](https://doi.org/10.48550/arxiv.1510.00726) · [arXiv 1510.00726](https://arxiv.org/abs/1510.00726) · [OpenAlex](https://openalex.org/W1888026739)

**Citations:** 1,201 citations (OpenAlex, 2026-10-14) · [102](https://md.research.elamur.ai/free/en/papers/goldberg-2015-primer-neural-network-models-natural/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
For years, NLP relied on linear classifiers such as SVMs and logistic regression applied to huge, sparse feature vectors. Many researchers lacked the background to adopt the non-linear neural models that were starting to outperform these classifiers. Goldberg's primer addresses this gap. It is a self-contained tutorial rather than an empirical study, so it introduces no new dataset, corpus or benchmark. Using one consistent notation, it first explains how to replace one-hot feature dimensions with trainable dense embeddings. It then covers fully connected feed-forward networks, convolution and pooling layers, recurrent networks, and recursive (tree-structured) networks. It also presents the computation-graph abstraction that makes gradient computation automatic. Each architecture is tied to NLP tasks where it has reportedly helped, including parsing, tagging, sentiment analysis and question answering. The main contribution is teaching rather than results. It gives practitioners a practical map for applying existing neural tools to their own problems and is intended as an entry point to neural NLP.

## Key points
- **Covered:** input encoding, MLPs, CNNs, RNNs (including a stack extension), RecNNs and computation graphs.
- **Deliberately excluded:** in-depth treatment of language and acoustic modeling, machine translation, multimodal work, attention, large-vocabulary training tricks and autoencoders.
- **Core pipeline:**
  - extract core linguistic features;
  - look up an embedding vector for each;
  - concatenate and/or sum the vectors;
  - feed the result to a non-linear classifier, training the embeddings jointly with the network.
- **Cited evidence:**
  - Swapping a parser's linear model for a feed-forward net improved syntactic parsing (Chen & Manning, 2014; Weiss et al., 2015; Pei et al., 2015; Durrett & Klein, 2015).
  - CNNs worked well for document classification (Johnson & Zhang, 2015) and sentiment (Kim, 2014).
- **Recursive network variants discussed:**
  - label embeddings in the composition function (Qian et al., 2015);
  - per-symbol-pair matrices (Socher et al., 2013);
  - the matrix-vector model and the recursive neural tensor network;
  - Tree-LSTMs (Tai, Socher & Manning, 2015).
  
  All are trained with backpropagation (historically called BPTS) and SGD.
- **Terminology and further reading:** "feature" means a linguistic input such as a word or POS tag, as opposed to an input-vector entry. The Bengio et al. (2015) book is recommended for deeper theory.

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

## Institutions

- [Bar-Ilan University](https://md.research.elamur.ai/free/en/universities/bar-ilan-university/) — University, Ramat Gan, Israel · 146 works · 24,845 citations

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## Cite

Cite as: Yoav Goldberg. 2015. A Primer on Neural Network Models for Natural Language Processing. arXiv:1510.00726.

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

```bibtex
@misc{goldberg2015primer,
  title = {{A Primer on Neural Network Models for Natural Language Processing}},
  author = {Yoav Goldberg},
  year = {2015},
  doi = {10.48550/arxiv.1510.00726},
  eprint = {1510.00726},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1510.00726}
}
```

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## References

150 references, 48 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 2 November 2026*