--- title: "Open Text Semantic Parsing Using FrameNet and — Shi 2004" description: "The paper tackles a core weakness of syntax-based language understanding: one syntactic pattern can carry different meanings, and one meaning can surface…" authors: ["Lei Shi", "Rada Mihalcea"] year: 2004 venue: "Demonstration Papers at HLT-NAACL 2004" source: "https://aclanthology.org/N04-3006/" 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/shi-2004-open-text-semantic-parsing-framenet/" --- [Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/shi-2004-open-text-semantic-parsing-framenet/) [home](https://md.research.elamur.ai/) / [papers](https://md.research.elamur.ai/free/papers/) / [2004](https://md.research.elamur.ai/free/papers/2004/) / Open Text Semantic Parsing Using FrameNet and WordNet # Open Text Semantic Parsing Using FrameNet and WordNet **Authors:** Lei Shi, [Rada Mihalcea](https://md.research.elamur.ai/free/authors/rada-mihalcea/) · **Year:** 2004 · **Venue:** Demonstration Papers at HLT-NAACL 2004 · **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/N04-3006/) · [PDF](https://aclanthology.org/N04-3006.pdf) · [OpenAlex](https://openalex.org/W2161490907) **Citations:** 21 citations (openalex-snapshot · 2026-10-14) · [9](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary The paper tackles a core weakness of syntax-based language understanding: one syntactic pattern can carry different meanings, and one meaning can surface in many syntactic forms. To get a representation that does not depend on syntax, the authors built a rule-based semantic parser for open text instead of a statistical role labeler. The parser draws on two knowledge sources: - **FrameNet** supplies sentence-level knowledge. Each annotated example is turned into an ordered rule that maps syntactic features to frame roles. - **WordNet** supplies word-level features such as attribute links and adjective types. Parsing runs in three steps: 1. A feature-augmented grammar analyzes the sentence and can reject ill-formed input. 2. Semantic roles are assigned by scoring candidate FrameNet rules. 3. Hand-written default rules cover constituents that FrameNet leaves unlabeled. The output is a recursive, tree-like structure of entities, interactions, roles and modifiers. On 350 held-out FrameNet sentences, the parser assigned the correct role to 74.5% of correctly identified elements. Because it is rule-based, it also works for words with very few annotated examples. ## Key points - Each FrameNet-derived rule encodes, in sentence order: grammatical function (GF), phrase type (PT), position relative to the target word, voice, and the preposition for PP phrases. - Role assignment scans candidate rules in one direction only, from left to right. Each exact feature match adds 1 point, and the rule with the highest score determines the frame and roles. - About 100 manual default rules fill gaps that FrameNet does not cover. For example, an "on …" phrase is read as the location of an interaction. - WordNet-derived lexicon entries record features such as category, degree, type and attribute. For example, *slow* is linked to the attribute *speed*. - The parser also labels the sentence function as assertion, query, yn-query or command. - Evaluation covered only role assignment, since FrameNet has no gold annotations for the other outputs. The 350 test sentences were removed from the corpus before the rules were learned, and role accuracy reached 74.5%. *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: [Semantic role labeling](https://md.research.elamur.ai/free/topics/semantic-role-labeling/) ## 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. - [New Features for FrameNet - WordNet Mapping](https://md.research.elamur.ai/free/papers/tonelli-2009-new-features-framenet-wordnet-mapping/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{shi2004open, title = {{Open Text Semantic Parsing Using FrameNet and WordNet}}, author = {Shi, Lei and Mihalcea, Rada}, booktitle = {Demonstration Papers at HLT-NAACL 2004}, year = {2004}, doi = {10.3115/1614025.1614031}, url = {https://doi.org/10.3115/1614025.1614031} } ``` - [← previous OntoSem Methods for Processing Semantic Ellipsis](https://md.research.elamur.ai/free/papers/aclbulk-2004-mcshane-ontosem-methods-processing-semantic-ellipsis/) - [next → ORANGE: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation](https://md.research.elamur.ai/free/papers/lin-2004-orange-method-evaluating-automatic-evaluation/) - [↑ back to papers of 2004](https://md.research.elamur.ai/free/papers/2004/) ## References 4 references, 2 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. M. Fleischman, N. Kwon, and E. Hovy. 2003. Maximum entropy models for FrameNet classification. In Proceedings of 2003 Conference on Empirical Methods in Natural Language Processing EMNLP-2003, Sapporo, Japan. [find](https://scholar.google.com/scholar?q=M%2E+Fleischman%2C+N%2E+Kwon%2C+and+E%2E+Hovy%2E+2003%2E+Maximum+entropy+models+for+FrameNet+classification%2E+In+Proceedings+of+2003+Conference+on+Empirical+Methods+in+Natural+Language+Processing+EMNLP%2D2003%2C+Sapporo) [our card](https://md.research.elamur.ai/free/papers/fleischman-2003-maximum-entropy-models-framenet-classification/) 2. D. Gildea and D. Jurafsky. 2000. Automatic labeling of semantic roles. In Proceedings of the 38th Annual Conference of the Association for Computational Linguistics (ACL-00), pages 512– 520, Hong Kong, October. [find](https://scholar.google.com/scholar?q=D%2E+Gildea+and+D%2E+Jurafsky%2E+2000%2E+Automatic+labeling+of+semantic+roles%2E+In+Proceedings+of+the+38th+Annual+Conference+of+the+Association+for+Computational+Linguistics+%28ACL%2D00%29%2C+pages+512%E2%80%93+520%2C+Hong+Kong) [our card](https://md.research.elamur.ai/free/papers/gildea-2002-automatic-labeling-semantic-roles/) 3. C. Johnson, C. Fillmore, M. Petruck, C. Baker, M. Ellsworth, J. Ruppenhofer, and E. Wood. 2002. FrameNet: Theory and Practice. [http://www.icsi.berkeley.edu/](http://www.icsi.berkeley.edu/) framenet. [↗](http://www.icsi.berkeley.edu/) 4. G. Miller. 1995. Wordnet: A lexical database. Communication of the ACM, 38(11):39–41. [find](https://scholar.google.com/scholar?q=G%2E+Miller%2E+1995%2E+Wordnet%3A+A+lexical+database%2E+Communication+of+the+ACM%2C+38%2811%29%3A39%E2%80%9341%2E)