--- title: "Resolving Object and Attribute Coreference in — Ding 2010" description: "Opinion mining often fails to determine what a pronoun or other referring phrase points to, such as \"it\" for a product or \"they\" for one of its parts." authors: ["Xiaowen Ding", "Bing Liu"] year: 2010 venue: "Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)" source: "https://aclanthology.org/C10-1031/" 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/papers/ding-2010-resolving-object-attribute-coreference-opinion/" --- [Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/ding-2010-resolving-object-attribute-coreference-opinion/) [home](https://md.research.elamur.ai/) / [papers](https://md.research.elamur.ai/free/papers/) / [2010](https://md.research.elamur.ai/free/papers/2010/) / Resolving Object and Attribute Coreference in Opinion Mining # Resolving Object and Attribute Coreference in Opinion Mining **Authors:** Xiaowen Ding, [Bing Liu](https://md.research.elamur.ai/free/authors/bing-liu/) · **Year:** 2010 · **Venue:** Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010) · **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/C10-1031/) · [PDF](https://aclanthology.org/C10-1031.pdf) · [OpenAlex](https://openalex.org/W1755776945) **Citations:** 69 citations (openalex-snapshot · 2026-10-14) · [9](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary Opinion mining often fails to determine what a pronoun or other referring phrase points to, such as "it" for a product or "they" for one of its parts. The opinion then loses its target or gets attached to the wrong one. Ding and Liu define a task of linking mentions of objects (products) and their attributes in opinionated text. They treat it as supervised pairwise classification and extend the Soon et al. (2001) decision-tree framework with two opinion-aware features: - **Sentiment consistency (SC):** writers tend to keep the same polarity toward an entity across normal and comparative sentences. - **Entity–opinion association (EOA):** a PMI-based score, mined from the corpus, of how plausibly an opinion word describes a given object or attribute. For example, "expensive" suits a camera but not picture quality. The authors annotated forum and blog posts in three domains. The full system clearly beats a Soon-style baseline and a centering-theory baseline. This shows that sentiment signals can guide reference resolution and help keep opinions attached to the correct targets. ## Key points - **Data:** 453 posts and 3,939 sentences covering cellphones (168 posts), TVs (173) and cars (112). Mentions are annotated with OBJ/ATTR types and coreference links, and the authors plan to release the annotations. - **Pipeline:** - Stanford POS tagger and a CRF-based NP chunker for preprocessing. - Pairwise feature vectors that include string similarity and a keywords-between feature. - A J48 decision tree in WEKA, evaluated with 10-fold cross-validation. - **Baselines:** - Baseline1: Soon et al. (2001), reduced to 9 features. - Baseline2: semantic centering theory following Fang et al. (2009). - **F-scores (full system vs. Baseline1 vs. Baseline2):** | Domain | Full system | Baseline1 | Baseline2 | |---|---|---|---| | Cellphones | 0.72 | 0.61 | 0.67 | | TVs | 0.73 | 0.64 | 0.68 | | Cars | 0.75 | 0.66 | 0.73 | - **Feature contributions:** SC alone adds about 5–6 F points over Baseline1, and EOA adds a further 2–4. Paired t-tests show significance at 95% in most comparisons; the gain over Baseline2 on cars is significant only at 90%. *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/) Topics: [Coreference resolution](https://md.research.elamur.ai/free/topics/coreference-resolution/) ## Cited by Cited by 9 papers of our library cite it (of the 140673 whose bibliographies we read); 1 of them have a page here, 8 not yet. - [It's absolutely divine! Can fine-grained sentiment analysis benefit from coreference resolution?](https://md.research.elamur.ai/free/papers/clercq-2020-it-s-absolutely-divine-can/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{ding2010resolving, title = {{Resolving Object and Attribute Coreference in Opinion Mining}}, author = {Ding, Xiaowen and Liu, Bing}, booktitle = {Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)}, year = {2010}, url = {https://aclanthology.org/C10-1031/} } ``` - [← previous RelaxCor: A Global Relaxation Labeling Approach to Coreference Resolution](https://md.research.elamur.ai/free/papers/sapena-2010-relaxcor-global-relaxation-labeling-approach/) - [next → SemEval-2010 Task 8: Multi-Way Classification of Semantic Relations between Pairs of Nominals](https://md.research.elamur.ai/free/papers/hendrickx-2010-semeval-2010-task-8-multi/) - [↑ back to papers of 2010](https://md.research.elamur.ai/free/papers/2010/) ## References 38 references, 13 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. A. Andreevskaia and S. 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