---
title: "Document Modeling with Gated Recurrent Neural — Tang 2015"
description: "The paper addresses document-level sentiment classification, i.e."
authors: ["Duyu Tang", "Bing Qin", "Ting Liu"]
year: 2015
venue: "Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/D15-1167/"
doi: "10.18653/v1/d15-1167"
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/tang-2015-document-modeling-gated-recurrent-neural/"
---
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# Document Modeling with Gated Recurrent Neural Network for Sentiment Classification

**Authors:** [Duyu Tang](https://md.research.elamur.ai/free/en/authors/duyu-tang/), [Bing Qin](https://md.research.elamur.ai/free/en/authors/bing-qin/), [Ting Liu](https://md.research.elamur.ai/free/en/authors/ting-liu/) · **Year:** 2015 · **Venue:** Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · **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/D15-1167/) · [PDF](https://aclanthology.org/D15-1167.pdf) · [DOI 10.18653/v1/d15-1167](https://doi.org/10.18653/v1/d15-1167) · [OpenAlex](https://openalex.org/W2250966211)

**Citations:** 1519 citations (openalex-snapshot · 2026-10-14) · [156](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
The paper addresses document-level sentiment classification, i.e. predicting the overall polarity or star rating of a review. The authors argue that bag-of-ngrams SVMs and Paragraph Vector treat a document as a flat collection of local word patterns and miss how sentences relate to each other, for example through contrast or cause. Their neural model works hierarchically. First, a sentence encoder (either a multi-filter CNN or an LSTM) turns each sentence's pretrained word embeddings into a fixed-size sentence vector. Then a gated recurrent network reads these sentence vectors in order and produces a single document vector, which is fed to a softmax classifier. The whole model is trained end-to-end with a cross-entropy loss. The authors evaluate on four large review datasets built from IMDB and the Yelp Dataset Challenge, using two evaluation metrics. The resulting models, Conv-GRNN and LSTM-GRNN, beat SVM, Paragraph Vector, CNN and JMARS baselines. A plain RNN at the document level performs poorly, while adding gates gives a large improvement. This makes the case for explicit two-level document composition.

## Key points
- The architecture has two composition stages: words → sentence (CNN or LSTM) and sentences → document (gated RNN). The final variants are named Conv-GRNN and LSTM-GRNN.
- The CNN sentence encoder combines three filters of widths 1, 2 and 3 (unigrams, bigrams, trigrams). Each filter's output goes through average pooling and tanh, and the results of the filters are then averaged.
- The document-level unit is essentially an LSTM without an output gate (the output gate is effectively always open). It uses input and forget gates over the previous hidden state and the current sentence vector.
- Two ways of reading out the document vector are compared: taking the final hidden state (GatedNN) or averaging all hidden states (GatedNN Avg).
- Results:
  - The models are evaluated on four IMDB/Yelp review datasets against SVM with bag-of-ngrams, Paragraph Vector, CNN and the JMARS recommender.
  - The proposed models score best across the board.
  - As a sentence encoder, LSTM outperforms the multi-filter CNN.
  - The authors release their code and datasets publicly.

## Interesting ideas
- **Hierarchical encoding instead of one long sequence.** Building sentence vectors first and then composing them keeps each recurrent sequence short. It also matches how people read longer texts. This suits any long-document task, such as news, legal texts or customer feedback.
- **Removing the output gate.** Dropping the output gate from an LSTM-like unit means nothing from the sentence-level state is hidden from the document vector. This is a simple, cheaper variant worth trying wherever every input chunk should contribute to the final representation.
- **Averaging hidden states as a readout.** Mean-pooling the recurrent states, rather than using only the last one, gives early sentences a direct path to the output. This is an easy option to test for long inputs where the final state may underweight the beginning.
- **Plain RNN versus gated RNN as a diagnostic.** Comparing an ungated and a gated recurrent model on the same pipeline clearly isolates the value of gating at the document level. Such a controlled ablation is a useful template for justifying architectural choices.
- **Implicit discourse modeling.** Sentence relations are learned from the classification signal alone, with no discourse parser. This matters for languages or domains where discourse parsers are unavailable or do not scale to web-sized data.

*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

156 papers of our library cite it (of the 140884 whose bibliographies we read); 17 of them have a page here, 139 not yet.

- [Automatic Arabic Dialect Identification Systems for Written Texts: A Survey](https://md.research.elamur.ai/free/en/papers/althobaiti-2020-automatic-arabic-dialect-identification-systems/)
- [ArSentD-LEV: A Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweets](https://md.research.elamur.ai/free/en/papers/baly-2019-arsentd-lev-multi-topic-corpus/)
- [Attention-based Recurrent Convolutional Neural Network for Automatic Essay Scoring](https://md.research.elamur.ai/free/en/papers/dong-2017-attention-based-recurrent-convolutional-neural/)
- [A Language-Independent Neural Network for Event Detection](https://md.research.elamur.ai/free/en/papers/feng-2016-language-independent-neural-network-event/)
- [Unleashing the Power of Neural Discourse Parsers - A Context and Structure Aware Approach Using Large Scale Pretraining](https://md.research.elamur.ai/free/en/papers/guz-2020-unleashing-power-neural-discourse-parsers/)
- [Few-Shot Charge Prediction with Discriminative Legal Attributes](https://md.research.elamur.ai/free/en/papers/hu-2018-few-shot-charge-prediction-discriminative/)
- [MEGA RST Discourse Treebanks with Structure and Nuclearity from Scalable Distant Sentiment Supervision](https://md.research.elamur.ai/free/en/papers/huber-2020-mega-rst-discourse-treebanks-structure/)
- [EmoNet: Fine-Grained Emotion Detection with Gated Recurrent Neural Networks](https://md.research.elamur.ai/free/en/papers/mageed-2017-emonet-fine-grained-emotion-detection/)
- [Text Classification Using Label Names Only: A Language Model Self-Training Approach](https://md.research.elamur.ai/free/en/papers/meng-2020-text-classification-label-names-only/)
- [Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree](https://md.research.elamur.ai/free/en/papers/sun-2019-aspect-level-sentiment-analysis-convolution/)
- [Aspect Level Sentiment Classification with Deep Memory Network](https://md.research.elamur.ai/free/en/papers/tang-2016-aspect-level-sentiment-classification-deep/)
- [Effective LSTMs for Target-Dependent Sentiment Classification](https://md.research.elamur.ai/free/en/papers/tang-2016-effective-lstms-target-dependent-sentiment/)
- [Attention-based LSTM for Aspect-level Sentiment Classification](https://md.research.elamur.ai/free/en/papers/wang-2016-attention-based-lstm-aspect-level/)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://md.research.elamur.ai/free/en/papers/wei-2019-eda-easy-data-augmentation-techniques/)
- [CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction](https://md.research.elamur.ai/free/en/papers/xiao-2018-cail2018-large-scale-legal-dataset/)
- [Hierarchical Attention Networks for Document Classification](https://md.research.elamur.ai/free/en/papers/yang-2016-hierarchical-attention-networks-document-classification/)
- [Legal Judgment Prediction via Topological Learning](https://md.research.elamur.ai/free/en/papers/zhong-2018-legal-judgment-prediction-topological-learning/)

## Cite

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

```bibtex
@inproceedings{tang2015document,
  title = {{Document Modeling with Gated Recurrent Neural Network for Sentiment Classification}},
  author = {Tang, Duyu and Qin, Bing and Liu, Ting},
  booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing},
  year = {2015},
  doi = {10.18653/v1/d15-1167},
  url = {https://doi.org/10.18653/v1/d15-1167}
}
```

- [← previous Do Supervised Distributional Methods Really Learn Lexical Inference Relations?](https://md.research.elamur.ai/free/en/papers/levy-2015-do-supervised-distributional-methods-really/)
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- [↑ back to papers of 2015](https://md.research.elamur.ai/free/en/papers/2015/)

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