--- title: "HIT-IR-WSD: A WSD System for English Lexical Sample Task" description: "The paper describes HIT-IR-WSD, a supervised word sense disambiguation system from… 1. accuracy. 4 references, cited by 2 papers here." authors: ["Yuhang Guo", "Wanxiang Che", "Yuxuan Hu", "Wei Zhang", "Ting Liu"] year: 2007 venue: "Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007)" source: "https://aclanthology.org/S07-1034/" 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/guo-2007-hit-ir-wsd-wsd-system/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/guo-2007-hit-ir-wsd-wsd-system/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2007](https://md.research.elamur.ai/free/en/papers/2007/page/2/) / HIT-IR-WSD: A WSD System for English Lexical Sample Task # HIT-IR-WSD: A WSD System for English Lexical Sample Task **Authors:** [Yuhang Guo](https://md.research.elamur.ai/free/en/authors/yuhang-guo/), [Wanxiang Che](https://md.research.elamur.ai/free/en/authors/wanxiang-che/), [Yuxuan Hu](https://md.research.elamur.ai/free/en/authors/yuxuan-hu/), [Wei Zhang](https://md.research.elamur.ai/free/en/authors/wei-zhang/) and [Ting Liu](https://md.research.elamur.ai/free/en/authors/ting-liu/) · **Year:** 2007 · **Venue:** [Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007)](https://md.research.elamur.ai/free/en/venues/international-workshop-on-semantic-evaluations/) ([International Workshop on Semantic Evaluations 2007](https://md.research.elamur.ai/free/en/venues/international-workshop-on-semantic-evaluations/2007/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **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/S07-1034/) · [PDF](https://aclanthology.org/S07-1034.pdf) · [OpenAlex](https://openalex.org/W1967642404) **Citations:** 10 citations (OpenAlex, 2026-10-14) · 2 in our library, of the 149,439 papers whose bibliographies we read ## Summary The paper describes HIT-IR-WSD, a supervised word sense disambiguation system from the Information Retrieval Lab at Harbin Institute of Technology, created for SemEval 2007's Task 11, which tested disambiguation of English words in a lexical sample setting. The authors frame sense selection as per-word classification. They train a libsvm classifier using only the official training data, which covers 20 nouns and 20 adjectives in the web-sourced track. Each instance becomes a binary vector built from four groups of cues: - lemmatized context words, - POS tags of nearby words, marked by side, - lemma collocations in a fixed window, also marked by side, - dependency information linking the target to its head, taken from MaltParser. On a held-out development split, they compare every combination of these groups, tune the SVM's C parameter and test several kernels. The linear kernel clearly wins. The final system scored 81.9% micro-averaged on the test set, the best result among participating runs. The work shows that a careful feature design with a simple linear classifier is enough for strong lexical-sample WSD. ## Key points - Toolchain: WordNet morph for root forms, SVMTool for Penn Treebank POS tags, MaltParser for dependencies and libsvm for multi-class classification. No extra training data was used. - Development set: 1,781 instances, about half the size of the remaining training portion. All 15 feature-group combinations were evaluated on it. - Context words alone reached 80.3%, and the best combinations reached 82.9%. Syntax alone gave 72.6% and POS alone 70.4%. Syntactic features gave inconsistent gains, which the authors partly attribute to parser errors. - Kernel comparison: the linear kernel scored 82.9%, while RBF, polynomial and sigmoid each scored 68.3% with default settings. Polynomial kernels improved as their degree approached 1. - Accuracy kept rising with more training data. POS tagging and parsing took about 2 hours, while SVM training and prediction took under 5 minutes. ## Interesting ideas - Ablate every subset of feature groups, labelled with a bit vector (e.g. 1011), so that both the value of each group and its interactions with the others become visible. - Describe a word's sense context through its dependency head: the head's lemma, the head's POS, the relation label and the head's direction. - Build the stop-word list empirically, keeping a candidate only if removing it measurably helps the dev score. - Use side-marked lemma collocations in a ±5 window that keep function words, since order and closeness carry information that unordered context loses. - Diagnose the model with a learning curve and a kernel-degree sweep, which show whether more data or more model capacity is the better investment. ## For Mova How these ideas could serve Mova, our small open language model — its files, stages and tests: - **I1** (learn): Run all on/off combinations of evidence sources (parser witnesses, VerbNet constraints, coreference cues) against a fixed dev set, to see which ones actually pull their weight. - **I2** (frames): Use the UD head and deprel of an ambiguous noun or adjective as structured evidence when choosing its WordNet sense for a frame slot, and keep a record of which arc decided the choice. - **I3** (learn): Apply the same "admit each item only if it helps" test to self-induced repair rules or filter lists, so that no rule enters without its own measured gain. - **I4** (tokens): Feed lemma-plus-side features near ambiguous tokens into the tagger and lemmatizer as an explainable local cue. - **I5** (learn): Plot parser and reader accuracy against the amount of silver data to decide when gathering more witness-agreed data stops paying off. *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 - [Harbin Institute of Technology](https://md.research.elamur.ai/free/en/universities/harbin-institute-of-technology/) — University, Harbin, China · 158 works · 28,841 citations 📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 16,039 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 15,891 works 🧭 Topics (2): [English](https://md.research.elamur.ai/free/en/topics/english/) · 2,263 works · [🔀 Word sense disambiguation](https://md.research.elamur.ai/free/en/topics/word-sense-disambiguation/) · 83 works ## Cite Cite as: Yuhang Guo, Wanxiang Che, Yuxuan Hu, Wei Zhang and Ting Liu. 2007. HIT-IR-WSD: A WSD System for English Lexical Sample Task. Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007). The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{guo2007hit, title = {{HIT-IR-WSD: A WSD System for English Lexical Sample Task}}, author = {Yuhang Guo, Wanxiang Che, Yuxuan Hu, Wei Zhang and Ting Liu}, booktitle = {Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007)}, year = {2007}, doi = {10.3115/1621474.1621508}, url = {https://doi.org/10.3115/1621474.1621508} } ``` - [← previous Advances in the CMU/Interact Arabic GALE Transcription System](https://md.research.elamur.ai/free/en/papers/noamany-2007-advances-cmu-interact-arabic-gale/) - [next → Machine Learning Based Semantic Inference: Experiments and Observations at RTE-3](https://md.research.elamur.ai/free/en/papers/li-2007-machine-learning-based-semantic-inference/) - [↑ back to papers of 2007](https://md.research.elamur.ai/free/en/papers/2007/page/2/) ## References 4 references, 3 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Lee, Y. K., and Ng, H. T. 2002. [An empirical evaluation of knowledge sources and learning algorithms for word sense disambiguation](https://md.research.elamur.ai/free/en/papers/lee-2002-empirical-evaluation-knowledge-sources-learning/). In Proceedings of EMNLP02, 41–48. 2. Chih-Chung Chang and Chih-Jen Lin, 2001. LIBSVM: a library for support vector machines. [find](https://scholar.google.com/scholar?q=Chih%2DChung+Chang+and+Chih%2DJen+Lin%2C+2001%2E+LIBSVM%3A+a+library+for+support+vector+machines%2E) 3. Jesús Giménez and Lluís Márquez. 2004. [SVMTool: A general POS tagger generator based on Support Vector Machines](https://md.research.elamur.ai/free/en/papers/gimenez-2004-svmtool-general-pos-tagger-generator/). Proceedings of the 4th International Conference on Language Resources and Evaluation (LREC'04). Lisbon, Portugal. 4. Nivre, J., Hall, J., Nilsson, J., Eryigit, G. and Marinov, S. 2006. [Labeled Pseudo-Projective Dependency Parsing with Support Vector Machines](https://md.research.elamur.ai/free/en/papers/nivre-2006-labeled-pseudo-projective-dependency-parsing/). In Proceedings of the Tenth Conference on Computational Natural Language Learning (CoNLL). *Updated 31 October 2026*