Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks

Binxuan Huang; Kathleen M. Carley

REVIEW 3 major objections 4 minor 32 references

Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks

T0 review · 3 major / 4 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read The paper claims that a target-dependent graph attention network over dependency trees outperforms sequence-based models on aspect-level sentiment classification, reaching roughly 80-83% accuracy on SemEval 2014 benchmarks.

desk verdict A plausible GAT-over-dependency-parse architecture for aspect sentiment, but the headline gains are small and not statistically demonstrated; worth reviewing for the idea, not for the current evidence. read the letter →

arxiv 1909.02606 v1 pith:TIDDYD47 submitted 2019-09-05 cs.CL

Binxuan Huang , Kathleen M. Carley This is my paper · ORCID

classification cs.CL

keywords aspect-levelsentimentclassificationtarget-dependentgraphattentionnetworkdependencyparsingsyntax-awareanalysisBERTrepresentationsSemEval2014polarity

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

An aspect-level sentiment classifier usually reads a sentence left to right, forcing it to connect an aspect term to distant sentiment words by long-range attention. This paper tries to establish that the task is better served by reading the dependency graph instead: a target-dependent graph attention network (TD-GAT) propagates sentiment features along syntactic edges from the aspect's modifiers back to the aspect node. On the SemEval 2014 laptop and restaurant benchmarks, the paper reports that TD-GAT with GloVe embeddings outperforms sequence-based and earlier syntax-based baselines, and that swapping in BERT representations pushes accuracy to 80.1% and 83.0% while keeping the trainable model at around 1.5 million parameters. A sympathetic reader would care because the result points to syntax structure, rather than longer word windows, as the missing ingredient for fine-grained sentiment.

What carries the argument

The load-bearing object is TD-GAT, a stack of graph attention layers over the sentence's dependency graph, with an LSTM unit attached to the aspect-target node. Each graph attention layer computes attention coefficients over syntactic neighbours and aggregates their hidden states; the LSTM then treats that aggregated state as a new observation and updates the target's hidden and cell states, so the target's identity is explicitly re-injected at every depth. This combination lets sentiment words located two or more hops from the target in the parse tree influence the final representation without relying on serial word order.

What would settle it

On a dataset with frequent parse errors (for example, informal or code-switched reviews), if TD-GAT fails to beat TD-LSTM, or if replacing the dependency graph with a random graph of the same degree distribution leaves accuracy unchanged, the claimed syntactic benefit would be falsified.

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Extended reading notes

Core claim

The core claim is that sentiment toward an aspect term is carried by a small syntactically delimited neighbourhood, and that a graph network can harvest it more reliably than a sequence model. Concretely, the paper proposes treating each sentence as an undirected dependency graph, replacing multi-word targets with a single meta-node, and stacking graph attention layers so that features from k hops away converge on the target node. An LSTM is inserted at the target node to carry target identity across layers, and the ablation isolates this as a consistent roughly one-point accuracy gain over the same graph network without target gating. The paper's experimental claim is that this architecture beats the strongest sequence baselines and even a fine-tuned BERT classifier on both SemEval 2014 domains, while using orders of magnitude fewer parameters and seconds per epoch.

Load-bearing premise

The entire advantage rests on the dependency parser being right: if sentiment-modifying words are not connected to the aspect target within a few hops, the graph attention has nothing useful to propagate, and the model degrades toward a noisier sequence model.

Editorial extensions

If this is right

Syntax-aware propagation should be part of aspect-level sentiment models; the reported gains over TD-LSTM directly compare syntactic neighbourhoods with left and right context windows.
Explicitly carrying target information across layers is worth about one accuracy point, so target-aware gating is separable from the graph machinery.
With BERT representations fixed and a small trainable head, the architecture reaches roughly 80% laptop and 83% restaurant accuracy, so deep fine-tuning of a large transformer is not necessary for this task.
Depth matters mainly for GloVe-based models: one layer is too shallow, and performance saturates around three to five layers, consistent with sentiment words living a few hops from the target.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Beyond the paper, the same target-node-plus-LSTM design should transfer to other target-conditioned classification tasks, such as relation extraction or premise selection, wherever a parse graph can be drawn around a marked span.
A testable extension is to corrupt the dependency graph (rewire edges or drop the parser's lowest-confidence arcs) and measure how quickly TD-GAT's advantage over sequence models erodes; that would quantify how much of the gain is genuinely syntactic.
Because the LSTM's per-layer gating is a generic memory mechanism, the paper implicitly suggests that any graph network over language could benefit from a node-specific recurrent state, not just the aspect node.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Here's my read of Huang and Carley's TD-GAT paper. The genuinely new piece is applying graph attention directly to the raw dependency parse for aspect-level sentiment, with an LSTM that carries the target node's state across GAT layers. The ablation shows the cross-layer target LSTM is worth about one to two points, and the depth analysis makes the mechanism concrete. The architecture is sensible, and the paper is honest about what it does and doesn't cover.

What the paper does well: it compares against a reasonable set of sequence baselines and an SVM, it reports model sizes, and it shows that BERT features with a small GAT head can reach high accuracy on SemEval 2014 laptop and restaurant. The model size comparison is useful, as is the depth sweep.

The soft spots are real and mostly statistical. The headline gains over the strongest baselines are small: 1.6 points on laptop over MemNet, 0.9 on restaurant, with no error bars, no significance tests, and no repeated-seed variance. With test sets of roughly 600 and 1,100 instances, those margins are within normal randomness. The stress-test note is right about that. Additionally, the BERT-CLS baseline is underfit: only 5 epochs, and the paper itself says it 'cannot converge in some trials.' So the claim of beating fine-tuned BERT is fragile. Parser noise is a secondary concern, also acknowledged in the conclusion. No code release makes the numbers harder to trust.

None of this kills the idea. The core mechanism is plausible, and the ablation gives some internal evidence that the target-aware LSTM helps. A serious referee could ask for repeated runs with confidence intervals, a properly tuned BERT-CLS baseline, and code release. With those, the paper would be much stronger.

I'd send this to peer review rather than desk reject: it's a real method on standard benchmarks, and the flaws are addressable. The reader's conditional verdict seems about right. If you end up reviewing it, I'd lean major revision.

Referee Report

3 major / 4 minor

Summary. This manuscript proposes a target-dependent graph attention network (TD-GAT) for aspect-level sentiment classification. A sentence is converted into a dependency graph; a multi-head graph attention network propagates features over syntactic neighbors, and an LSTM unit carries the aspect-target state across GAT layers. The classification is made from the target node's final hidden state. Experiments on the SemEval 2014 laptop and restaurant datasets compare GloVe- and BERT-representation versions of the model against sequence-based and syntax-based baselines, reporting accuracy improvements and a much smaller model size than fine-tuned BERT.

Significance. The idea of placing the aspect target on the dependency graph while preserving the original syntactic order is sensible, and the use of a lightweight LSTM to inject target information across GAT layers is technically coherent. The ablation in Table 3 supports the utility of the target-dependent LSTM, and Tables 4 and Section 4.6 show a clear efficiency advantage over fine-tuned BERT. If the accuracy differences were statistically supported, the paper would make a useful contribution. At present, however, the empirical case rests on single-point accuracies whose differences are within plausible noise, so the contribution is promising but not yet established.

major comments (3)

[§4.3, Table 2] The central claim that TD-GAT outperforms the listed baselines is not supported by any statistical evidence. All accuracies are reported as single numbers with no confidence intervals, no standard deviation over repeated runs, and no significance tests. On the Laptop test set (n=638), the largest GloVe advantage is +1.4 points (TD-GAT-GloVe 74.0 vs AOA-LSTM 72.6) and the BERT advantage is +3.0 points (TD-GAT-BERT 80.1 vs BERT-CLS 77.1); on Restaurant (n=1120) the corresponding differences are +0.9 and +1.8 points. For independent binomial proportions these differences correspond to z-statistics around 0.5–1.3, far below the 0.05 significance level, and no paired test is provided. The claim that the method outperforms the baselines therefore cannot be distinguished from random variation at the reported sample sizes. Please report variance across seeds, confidence intervals or significance tests, and specify whether the baseline numbers are reimplemented or quoted from prior publications.
[§4.3, BERT-CLS baseline] The BERT-CLS comparison appears underconfigured. BERT-CLS is fine-tuned for only 5 epochs with batch size 8 and learning rate 10^{-5}, and the text states that the model "cannot converge in some trials" without quantifying how often this happened or how the reported number was selected. With no convergence criterion, early stopping, or multiple restarts, the 77.1 and 81.2 numbers may reflect an underfit baseline. Because the BERT-based central results (80.1 and 83.0) are compared against this baseline, the comparison should be redone with longer training, early stopping, and multiple restarts, or the claim should be limited to the BERT-AVG comparison.
[§4.1–4.2, model selection and baseline provenance] The model selection procedure is underspecified. The paper states that 500 training instances are used as a development set to tune the model, but it reports no development-set results, no hyperparameter search ranges, and no selection criterion; the layer count k is then reported for k=3, 4, 5 directly on the test set. If any hyperparameter, including k, was chosen based on test accuracy, the reported best numbers involve multiple comparisons. In addition, Table 2 does not state whether the baseline accuracies are from prior publications or from reimplementation under the same dev split. Please clarify the selection protocol and the provenance of all baseline numbers, and report the variance of the final configurations.

minor comments (4)

[Eq. (2)] The sentence "where f represents vector concatenation" is inaccurate: in Eq. (2) the double vertical bar denotes concatenation, while f is the LeakyReLU nonlinearity; please correct the wording.
[§3.3, after Eq. (9)] The gates are referred to as "it, ft and ot", which conflicts with the indexed notation i_l, f_l, o_l used in Eqs. (4)–(6); clarify the notation to avoid confusion with the target node t.
[§4.6] The sentence "MemNet is the model ranks the first" contains a grammatical error; also, the training-time comparison (1.12–1.15 seconds/epoch vs 226.50 seconds/epoch) should state whether both measurements used the same GPU and whether the BERT-CLS time includes only the fine-tuning epoch or also inference.
[§5, Conclusion] The acknowledged sensitivity to dependency-parser noise would be better supported by a robustness check, for example comparing the Stanford parser with an alternative parser or reporting the fraction of target-sentiment edges that are correct in the test sentences.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the central claim is an empirical comparison against external benchmarks, and the only self-citation concerns a design motivation that is independently validated by the paper's own ablation study.

full rationale

The paper's central claim is empirical: TD-GAT outperforms sequence-based and syntax-based baselines on SemEval 2014 laptop and restaurant test sets. The derivation chain is a standard supervised classifier: dependency-graph features from the external Stanford parser, fixed GloVe or BERT representations, a graph attention network, an LSTM gate across layers, and cross-entropy training. No equation defines the prediction in terms of the target result, and no fitted parameter is renamed as a prediction. The one self-citation, in Section 3.3, says the LSTM unit is 'also helpful for overcoming noisy information in a graph (Huang and Carley, 2019)'. This is a motivational citation, not a load-bearing proof: Section 4.4 and Table 3 provide an independent ablation showing that removing the target-information LSTM hurts accuracy (about 1.2 points with GloVe and 0.95 points with BERT on average), so the paper does not rely on the cited work to justify the central claim. Self-cited baselines such as PG-CNN and AOA-LSTM are experimental comparisons, not premises. The conclusion's caveat about 'potential noise from dependency parsing errors' is a stated limitation about parser quality, not a circular step. The instability of BERT-CLS and the lack of significance tests are correctness risks, but under the circularity rubric they do not constitute circular reasoning. Overall, the paper is self-contained against external benchmarks and the self-citation is minor and non-load-bearing.

Assumptions & free parameters 7 free parameters · 4 assumptions · 0 invented entities

The central claim is empirical, so the ledger records hyperparameters and background assumptions rather than derived constants. No new entities are introduced.

free parameters (7)

hidden_dim = 300
Hidden state dimension for GAT and LSTM layers, chosen by hand in Section 4.2.
num_attention_heads = 6
Number of GAT attention heads, set in Section 4.2.
batch_size = 32
Training batch size, set in Section 4.2.
dropout_rate = 0.7
Dropout applied to input word embeddings, set in Section 4.2.
l2_regularization_lambda = 1e-4
L2 regularization coefficient in the loss, set in Section 4.2.
learning_rate = 1e-3 (Adam) then SGD without schedule
Optimizer settings in Section 4.2; no epochs or schedule specified.
num_layers = 3 to 5
Model depth explored in Section 4.5; best results reported for 3-5 layers.

assumptions (4)

domain assumption The dependency parser produces arcs that place sentiment words close to the aspect target within a few hops.
Invoked throughout Sections 3 and 4.2; the entire graph construction relies on parser output.
domain assumption GloVe and BERT embeddings encode enough semantic and contextual information for sentiment classification.
Used to initialize node features in Section 4.2; no additional features are provided.
domain assumption SemEval 2014 sentiment labels are correct and representative.
Used as ground truth in Section 4.1 for training and evaluation.
domain assumption The external 500-instance dev split from Tay et al. (2018) is representative of the training distribution.
Used for model tuning in Section 4.1; no analysis of potential distribution shift is provided.

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Cite this review

Pith. "Pith review of Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks." pith.science (2026). https://pith.science/paper/TIDDYD47

@misc{pith2026190902606,
  author       = {Pith},
  title        = {Pith review of: Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TIDDYD47}},
  note         = {Machine review of arXiv:1909.02606}
}

read the original abstract

Aspect level sentiment classification aims to identify the sentiment expressed towards an aspect given a context sentence. Previous neural network based methods largely ignore the syntax structure in one sentence. In this paper, we propose a novel target-dependent graph attention network (TD-GAT) for aspect level sentiment classification, which explicitly utilizes the dependency relationship among words. Using the dependency graph, it propagates sentiment features directly from the syntactic context of an aspect target. In our experiments, we show our method outperforms multiple baselines with GloVe embeddings. We also demonstrate that using BERT representations further substantially boosts the performance.

Figures

Figures reproduced from arXiv: 1909.02606 by the authors.

**Figure 1.** The dependency graph for “delivery was early too”. Features can be propagated from neighbour nodes to the aspect node “delivery”. We transform the sentence into a dependency graph using an off-the-shelf dependency parser (Chen and Manning, 2014). Each node represents a word and is associated with an embedding vector as its local feature vector. An undirected edge between two words means these two words are syntact… view at source ↗

**Figure 2.** The impact of model depth (number of lay [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

32 extracted references · 18 canonical work pages

[1]

URL: " 'urlintro :=

ENTRY address author booktitle chapter edition editor howpublished institution journal key month note number organization pages publisher school series title type volume year eprint doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRINGS urlintro eprinturl eprintpr...
[2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...
[3]

Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473

arXiv 2014
[4]

Danqi Chen and Christopher Manning. 2014. A fast and accurate dependency parser using neural networks. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pages 740--750

work page 2014
[5]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805

arXiv 2018
[6]

Li Dong, Furu Wei, Chuanqi Tan, Duyu Tang, Ming Zhou, and Ke Xu. 2014. Adaptive recursive neural network for target-dependent twitter sentiment classification. In Proceedings of the 52nd annual meeting of the association for computational linguistics (volume 2: Short papers), volume 2, pages 49--54

work page 2014
[7]

Matthias Fey and Jan Eric Lenssen. 2019. Fast graph representation learning with pytorch geometric. arXiv preprint arXiv:1903.02428

arXiv 2019
[8]

Binxuan Huang and Kathleen Carley. 2018. Parameterized convolutional neural networks for aspect level sentiment classification. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1091--1096

work page 2018

Show all 32 references

[9]

Binxuan Huang and Kathleen M Carley. 2019. Residual or gate? towards deeper graph neural networks for inductive graph representation learning. arXiv preprint arXiv:1904.08035

2019 arXiv
[10]

Binxuan Huang, Yanglan Ou, and Kathleen M Carley. 2018. Aspect level sentiment classification with attention-over-attention neural networks. In International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and...

2018
[11]

Long Jiang, Mo Yu, Ming Zhou, Xiaohua Liu, and Tiejun Zhao. 2011. Target-dependent twitter sentiment classification. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1, pages 151--160. Association fo...

2011
[12]

Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980

2014 arXiv
[13]

Svetlana Kiritchenko, Xiaodan Zhu, Colin Cherry, and Saif Mohammad. 2014. Nrc-canada-2014: Detecting aspects and sentiment in customer reviews. In Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pages 437--442

2014
[14]

Lishuang Li, Yang Liu, and AnQiao Zhou. 2018. Hierarchical attention based position-aware network for aspect-level sentiment analysis. In Proceedings of the 22nd Conference on Computational Natural Language Learning, pages 181--189

2018
[15]

Dehong Ma , Sujian Li , Xiaodong Zhang , and Houfeng Wang . 2017. Interactive attention networks for aspect-level sentiment classification. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI-17 , pages 4068--4074

2017
[16]

Andrew L Maas, Awni Y Hannun, and Andrew Y Ng. 2013. Rectifier nonlinearities improve neural network acoustic models. In Proc. icml, volume 30, page 3

2013
[17]

Thien Hai Nguyen and Kiyoaki Shirai. 2015. Phrasernn: Phrase recursive neural network for aspect-based sentiment analysis. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 2509--2514

2015
[18]

Bo Pang, Lillian Lee, et al. 2008. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval , 2(1--2):1--135

2008
[19]

Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014. Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pages 1532--1543

2014
[20]

Maria Pontiki, Dimitris Galanis, John Pavlopoulos, Harris Papageorgiou, Ion Androutsopoulos, and Suresh Manandhar. 2014. http://www.aclweb.org/anthology/S14-2004 Semeval-2014 task 4: Aspect based sentiment analysis . In Proceedings of the 8th International Workshop on Semantic...

2014
[21]

Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. 2009. The graph neural network model. IEEE Transactions on Neural Networks, 20(1):61--80

2009
[22]

Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1):1929--1958

2014
[23]

Chi Sun, Luyao Huang, and Xipeng Qiu. 2019. Utilizing bert for aspect-based sentiment analysis via constructing auxiliary sentence. arXiv preprint arXiv:1903.09588

2019 arXiv
[24]

Duyu Tang, Bing Qin, Xiaocheng Feng, and Ting Liu. 2016 a . Effective lstms for target-dependent sentiment classification. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 3298--3307

2016
[25]

Duyu Tang, Bing Qin, and Ting Liu. 2016 b . Aspect level sentiment classification with deep memory network. arXiv preprint arXiv:1605.08900

2016 arXiv
[26]

Yi Tay, Anh Tuan Luu, and Siu Cheung Hui. 2018. Learning to attend via word-aspect associative fusion for aspect-based sentiment analysis. In AAAI

2018
[27]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in neural information processing systems, pages 5998--6008

2017
[28]

Petar Veli c kovi \'c , Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903

2017 arXiv
[29]

Joachim Wagner, Piyush Arora, Santiago Cortes, Utsab Barman, Dasha Bogdanova, Jennifer Foster, and Lamia Tounsi. 2014. Dcu: Aspect-based polarity classification for semeval task 4. In Proceedings of the 8th international workshop on semantic evaluation (SemEval 2014), pages 223--229

2014
[30]

Yequan Wang, Minlie Huang, Li Zhao, et al. 2016. Attention-based lstm for aspect-level sentiment classification. In Proceedings of the 2016 conference on empirical methods in natural language processing, pages 606--615

2016
[31]

Hu Xu, Bing Liu, Lei Shu, and Philip S Yu. 2019. Bert post-training for review reading comprehension and aspect-based sentiment analysis. arXiv preprint arXiv:1904.02232

2019 arXiv
[32]

Wei Xue and Tao Li. 2018. Aspect based sentiment analysis with gated convolutional networks. arXiv preprint arXiv:1805.07043

2018 arXiv

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.

[1] [1]

URL: " 'urlintro :=

ENTRY address author booktitle chapter edition editor howpublished institution journal key month note number organization pages publisher school series title type volume year eprint doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRINGS urlintro eprinturl eprintpr...

[2] [2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

[3] [3]

Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473

arXiv 2014

[4] [4]

Danqi Chen and Christopher Manning. 2014. A fast and accurate dependency parser using neural networks. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pages 740--750

work page 2014

[5] [5]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805

arXiv 2018

[6] [6]

Li Dong, Furu Wei, Chuanqi Tan, Duyu Tang, Ming Zhou, and Ke Xu. 2014. Adaptive recursive neural network for target-dependent twitter sentiment classification. In Proceedings of the 52nd annual meeting of the association for computational linguistics (volume 2: Short papers), volume 2, pages 49--54

work page 2014

[7] [7]

Matthias Fey and Jan Eric Lenssen. 2019. Fast graph representation learning with pytorch geometric. arXiv preprint arXiv:1903.02428

arXiv 2019

[8] [8]

Binxuan Huang and Kathleen Carley. 2018. Parameterized convolutional neural networks for aspect level sentiment classification. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1091--1096

work page 2018