--- title: "pkudblab at SemEval-2016 Task 6 — Wei 2016" description: "The paper describes pkudblab's system for SemEval-2016 Task 6, which classifies tweets as favoring, opposing, or neutral toward a given target." authors: ["Wan Wei", "Xiao Zhang", "Xuqin Liu", "Wei Chen", "Tengjiao Wang"] year: 2016 venue: "Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)" source: "https://aclanthology.org/S16-1062/" doi: "10.18653/v1/s16-1062" licence: "CC BY 4.0 (ACL Anthology)" licence_url: "https://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/papers/wei-2016-pkudblab-semeval-2016-task-6/" --- [Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/wei-2016-pkudblab-semeval-2016-task-6/) [home](https://md.research.elamur.ai/) / [papers](https://md.research.elamur.ai/free/papers/) / [2016](https://md.research.elamur.ai/free/papers/2016/page/3/) / pkudblab at SemEval-2016 Task 6 : A Specific Convolutional Neural Network System for Effective Stance Detection # pkudblab at SemEval-2016 Task 6 : A Specific Convolutional Neural Network System for Effective Stance Detection **Authors:** Wan Wei, [Xiao Zhang](https://md.research.elamur.ai/free/authors/xiao-zhang/), Xuqin Liu, Wei Chen, Tengjiao Wang · **Year:** 2016 · **Venue:** Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://aclanthology.org/S16-1062/) · [PDF](https://aclanthology.org/S16-1062.pdf) · [DOI 10.18653/v1/s16-1062](https://doi.org/10.18653/v1/s16-1062) · [OpenAlex](https://openalex.org/W2462365838) **Citations:** 124 citations (openalex-snapshot · 2026-10-14) · [31](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary The paper describes pkudblab's system for SemEval-2016 Task 6, which classifies tweets as favoring, opposing, or neutral toward a given target. The authors adapt Kim's (2014) sentence-classification CNN. It uses 300-dimensional word2vec embeddings pre-trained on Google News, convolution filters of several widths, ReLU, max-pooling, and a softmax output layer. Training uses Adadelta, dropout, and L2 regularization. Three task-specific additions are proposed: - **Voting-based prediction:** predictions from many training iterations and runs are combined by majority vote, replacing the usual choice of the single best validation checkpoint. - **Per-target models (subtask A):** a separate model is trained for each of the five targets. - **Weak labeling (subtask B):** an unlabeled domain corpus is turned into favor/against training data using stance-revealing phrases and hashtags. A "None" label is assigned when the two class probabilities are nearly equal. The system placed 2nd in subtask A and 1st in subtask B, where its margin over other teams was large. This suggests that a fairly simple CNN with careful ensembling and weak supervision works well for stance detection. ## Key points - **Results:** subtask A ranked 2nd of 19 teams with a score of 0.6733 (MITRE scored 0.6782). Subtask B ranked 1st of 9 teams with 0.5628, about 0.12 above the runner-up (0.4466). The official metric is the mean of F-favor and F-against. - **Setup:** 300-d Google News word2vec vectors; 300 filters in total (100 each of widths 3, 4, and 5); dropout 0.5; L2 regularization 1e-6; Adadelta optimizer. - **Vote scheme:** ten parallel runs, each with its own non-overlapping validation split. Labels are first voted within each run across selected iterations, then voted again across the ten runs. - **Per-target vs. single model (subtask A):** per-target models scored 0.6733 overall, compared with 0.6498 for one model trained on all data. The gain was largest for Atheism (0.6334 vs. 0.5652), but per-target models did worse on Climate Change and Feminist Movement. - **Subtask B training data:** labels came from cues such as "#MakeAmericaGreatAgain" (favor) and "idiot" (against). The resulting set has 2,562 favor and 3,418 against tweets, per the dataset table. "None" is predicted when the probability gap is below a random α in [0.05, 0.1]. *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: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/) ## Cited by Cited by 31 papers of our library cite it (of the 140673 whose bibliographies we read); 14 of them have a page here, 17 not yet. - [MGKM at StanceEval2024 Fine-Tuning Large Language Models for Arabic Stance Detection](https://md.research.elamur.ai/free/papers/alghaslan-2024-mgkm-stanceeval2024-fine-tuning-large/) - [Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations](https://md.research.elamur.ai/free/papers/allaway-2020-zero-shot-stance-detection-dataset/) - [Adversarial Learning for Zero-Shot Stance Detection on Social Media](https://md.research.elamur.ai/free/papers/allaway-2021-adversarial-learning-zero-shot-stance/) - [Stance Detection with Bidirectional Conditional Encoding](https://md.research.elamur.ai/free/papers/augenstein-2016-stance-detection-bidirectional-conditional-encoding/) - [Stance Detection in COVID-19 Tweets](https://md.research.elamur.ai/free/papers/glandt-2021-stance-detection-covid-19-tweets/) - [Knowledge Enhanced Masked Language Model for Stance Detection](https://md.research.elamur.ai/free/papers/kawintiranon-2021-knowledge-enhanced-masked-language-model/) - [Multi-Task Stance Detection with Sentiment and Stance Lexicons](https://md.research.elamur.ai/free/papers/li-2019-multi-task-stance-detection-sentiment/) - [A Multi-Task Learning Framework for Multi-Target Stance Detection](https://md.research.elamur.ai/free/papers/li-2021-multi-task-learning-framework-multi/) - [POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detection](https://md.research.elamur.ai/free/papers/liu-2022-politics-pretraining-same-story-article/) - [Exploiting Sentiment and Common Sense for Zero-shot Stance Detection](https://md.research.elamur.ai/free/papers/luo-2022-exploiting-sentiment-common-sense-zero/) - [Multilingual Target-Stance Extraction](https://md.research.elamur.ai/free/papers/mines-2026-multilingual-target-stance-extraction/) - [Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection Corpus](https://md.research.elamur.ai/free/papers/schuff-2017-annotation-modelling-analysis-fine-grained/) - [C-STANCE: A Large Dataset for Chinese Zero-Shot Stance Detection](https://md.research.elamur.ai/free/papers/zhao-2023-c-stance-large-dataset-chinese/) - [Multilingual Stance Detection in Tweets: The Catalonia Independence Corpus](https://md.research.elamur.ai/free/papers/zotova-2020-multilingual-stance-detection-tweets-catalonia/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{wei2016pkudblab, title = {{pkudblab at SemEval-2016 Task 6 : A Specific Convolutional Neural Network System for Effective Stance Detection}}, author = {Wei, Wan and Zhang, Xiao and Liu, Xuqin and Chen, Wei and Wang, Tengjiao}, booktitle = {Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)}, year = {2016}, doi = {10.18653/v1/s16-1062}, url = {https://doi.org/10.18653/v1/s16-1062} } ``` - [← previous Phrase Detectives Corpus 1.0 Crowdsourced Anaphoric Coreference.](https://md.research.elamur.ai/free/papers/chamberlain-2016-phrase-detectives-corpus-1-0/) - [next → Pointer Sentinel Mixture Models](https://md.research.elamur.ai/free/papers/merity-2016-pointer-sentinel-mixture-models/) - [↑ back to papers of 2016](https://md.research.elamur.ai/free/papers/2016/page/3/) ## References 14 references, 2 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. 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