--- title: "A Lucene and Maximum Entropy Model Based Hedge Detection" description: "Lin Chen and Barbara Di Eugenio built a system for the CoNLL-2010 shared task on hedge detection. 5 references, cited by 1 paper here." authors: ["Lin Chen", "Barbara Di Eugenio"] year: 2010 venue: "Proceedings of the Fourteenth Conference on Computational Natural Language Learning – Shared Task" source: "https://aclanthology.org/W10-3016/" 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/chen-2010-lucene-maximum-entropy-model-based/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/chen-2010-lucene-maximum-entropy-model-based/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2010](https://md.research.elamur.ai/free/en/papers/2010/page/2/) / A Lucene and Maximum Entropy Model Based Hedge Detection System # A Lucene and Maximum Entropy Model Based Hedge Detection System **Authors:** [Lin Chen](https://md.research.elamur.ai/free/en/authors/lin-chen/) and [Barbara Di Eugenio](https://md.research.elamur.ai/free/en/authors/barbara-di-eugenio/) · **Year:** 2010 · **Venue:** [Proceedings of the Fourteenth Conference on Computational Natural Language Learning – Shared Task](https://md.research.elamur.ai/free/en/venues/conll/) ([CoNLL 2010](https://md.research.elamur.ai/free/en/venues/conll/2010/)) · **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/W10-3016/) · [PDF](https://aclanthology.org/W10-3016.pdf) · [OpenAlex](https://openalex.org/W1512428015) **Citations:** 4 citations (OpenAlex, 2026-10-14) · [1](https://md.research.elamur.ai/free/en/papers/chen-2010-lucene-maximum-entropy-model-based/cited-by/) in our library, of the 141,840 papers whose bibliographies we read ## Summary Lin Chen and Barbara Di Eugenio built a system for the CoNLL-2010 shared task on hedge detection. The task is to decide whether a sentence expresses uncertainty, and their system also points to the specific hedge cues that cause it. The cue annotations in the training data serve as seeds. A candidate generator then rewrites them: it widens single-word cues with neighbouring tokens, drops proper nouns, and substitutes POS tags for some words. Each candidate is scored by support (how many sentences it matches) and confidence (what share of those sentences are uncertain). Apache Lucene stores every sentence with words and POS tags at the same index positions, so a cue can mix words and tags and still match with small gaps. A separate MaxEnt classifier for each retained cue makes the final call on every sentence the cue matches. The submitted run placed third on Wikipedia. Later threshold tuning raised the F-scores to 0.612 on Wikipedia and 0.802 on the biological data. The work shows that explicit, inspectable cue patterns combined with small local classifiers give competitive uncertainty detection. ## Key points - Lucene setup: - Each sentence is one Lucene document, with OpenNLP tokenization and tagging. - Every word and its POS tag share an index position (position increment 0). - SpanNearQuery permits a position gap of 2 between cue tokens. - Single tokens made poor cues: - Thresholds of support 20 and confidence 0.4 left only 42 tokens on Wikipedia. - Thresholds of 5 and 0.3 gave 279 tokens, but these included junk such as *is* and *his*. - POS replacement covers VBN, NNS, DT and CD. Examples of resulting cues are *it is VBN* and *some of NNS*. - MaxEnt features, trained with OpenNLP MaxEnt for 100 iterations per cue: - sentence location (title or body); - sentence length; - cue position (start, middle or end); - word and POS collocations in a window of 5. - Results: - The official run (confidence 0.35, support 5) reached F 0.5741 on Wikipedia, ranking third. On the biological data it reached F 0.7692 and ranked third from last, because only the smaller training file was used. - The best thresholds were confidence 0.5 and support 5. At that setting the generator produced 52 cues with 100% confidence on Wikipedia and 332 on the biological data. ## Interesting ideas - Placing a word and its POS tag at the same index position lets a single query freely mix literal words and tag slots, which turns corpus pattern counting into cheap lookups. - Scoring mined patterns by support and confidence, then sweeping both thresholds, makes the trade-off between coverage and reliability explicit: higher confidence raised precision and lower support raised recall. - Seed annotations are generalized by three deliberate edits: extending with context, deleting proper nouns, and replacing content words with POS tags. Each edit is then re-scored against the corpus rather than trusted blindly. - Training one small classifier per cue keeps each decision local and traceable to the pattern that fired. As a result, the system reports why a sentence counts as uncertain, not just that it does. - Tolerating a small gap between cue tokens lets cues like "is said" survive inserted modifiers such as "widely". ## For Mova How these ideas could serve Mova, our small open language model — its files, stages and tests: - **I1** (learn): Indexing silver treebanks this way would let us quickly count how often a candidate repair-rule pattern such as "it is VBN" occurs and how often it co-occurs with a known error. - **I2** (learn): Self-induced repair rules could be admitted only above tuned support and confidence thresholds, with the sweep itself reported in evaluation. - **I3** (frames): The prag crate could grow its inventory of epistemic and hedging markers (e.g. "it is believed", "many NNS") from a few seeds, keeping only variants the data supports. - **I4** (world): Statements entering the world of a text could carry an uncertainty flag tied to the specific hedge cue and a per-cue check, so answers can say a fact is only reported as believed. - **I5** (tree): Hedge patterns could be matched along dependency paths (aux/advmod attachments to the verb) instead of surface gaps, giving the same robustness in an explainable, tree-based form. *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 - [University of Illinois Chicago](https://md.research.elamur.ai/free/en/universities/university-of-illinois-chicago/) — University, Chicago, United States · 38 works · 19,844 citations 📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 9,086 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 8,889 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 9,296 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 9,206 works ## Cite Cite as: Lin Chen and Barbara Di Eugenio. 2010. A Lucene and Maximum Entropy Model Based Hedge Detection System. Proceedings of the Fourteenth Conference on Computational Natural Language Learning – Shared Task. The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{chen2010lucene, title = {{A Lucene and Maximum Entropy Model Based Hedge Detection System}}, author = {Lin Chen and Barbara {Di Eugenio}}, booktitle = {Proceedings of the Fourteenth Conference on Computational Natural Language Learning – Shared Task}, year = {2010}, url = {https://aclanthology.org/W10-3016/} } ``` - [← previous Using Syntactic and Semantic based Relations for Dialogue Act Recognition](https://md.research.elamur.ai/free/en/papers/kluwer-2010-syntactic-semantic-based-relations-dialogue/) - [next → Expanding the Lexicon for a Resource-Poor Language Using a Morphological Analyzer and a Web Crawler](https://md.research.elamur.ai/free/en/papers/gasser-2010-expanding-lexicon-resource-poor-language/) - [↑ back to papers of 2010](https://md.research.elamur.ai/free/en/papers/2010/page/2/) ## References 5 references, 2 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Viola Ganter and Michael Strube. 2009. [Finding hedges by chasing weasels: Hedge detection using Wikipedia tags and shallow linguistic features](https://md.research.elamur.ai/free/en/papers/ganter-2009-finding-hedges-chasing-weasels-hedge/). In Proceedings of the ACL-IJCNLP 2009 Conference Short Papers, pages 173–176, Suntec, Singapore, August. Association for Computational Linguistics. 2. Marc Light, Xin Ying Qiu, and Padmini Srinivasan. 2004. The language of bioscience: Facts, speculations, and statements in between. In Proceedings of BioLink 2004 Workshop on Linking Biological Literature, Ontologies and Databases: Tools for Users, pages 17–24, Boston, Mass, May. [find](https://scholar.google.com/scholar?q=Marc+Light%2C+Xin+Ying+Qiu%2C+and+Padmini+Srinivasan%2E+2004%2E+The+language+of+bioscience%3A+Facts%2C+speculations%2C+and+statements+in+between%2E+In+Proceedings+of+BioLink+2004+Workshop+on+Linking+Biological+Litera) 3. Bing Liu. 2007. Web data mining: exploring hyperlinks, contents, and usage data. Springer. [find](https://scholar.google.com/scholar?q=Bing+Liu%2E+2007%2E+Web+data+mining%3A+exploring+hyperlinks%2C+contents%2C+and+usage+data%2E+Springer%2E) 4. Michael McCandless, Erik Hatcher, and Otis Gospod-neti. 2010. Lucene in action. Manning Publications Co, 2nd edition. [find](https://scholar.google.com/scholar?q=Michael+McCandless%2C+Erik+Hatcher%2C+and+Otis+Gospod%2Dneti%2E+2010%2E+Lucene+in+action%2E+Manning+Publications+Co%2C+2nd+edition%2E) 5. Ben Medlock and Ted Briscoe. 2007. [Weakly supervised learning for hedge classification in scientific literature](https://md.research.elamur.ai/free/en/papers/medlock-2007-weakly-supervised-learning-hedge-classification/). In Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 992–999. Association for Computational Linguistics, June. *Updated 24 October 2026*