---
title: "Semi-Supervised Frame-Semantic Parsing for — Das 2011"
description: "Frame-semantic parsers trained on FrameNet struggle with predicates that never appear in the lexicon or its annotated sentences."
authors: ["Dipanjan Das", "Noah A. Smith"]
year: 2011
venue: "Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies"
source: "https://aclanthology.org/P11-1144/"
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/das-2011-semi-supervised-frame-semantic-parsing/"
---
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# Semi-Supervised Frame-Semantic Parsing for Unknown Predicates

**Authors:** [Dipanjan Das](https://md.research.elamur.ai/free/authors/dipanjan-das/), [Noah A. Smith](https://md.research.elamur.ai/free/authors/noah-a-smith/) · **Year:** 2011 · **Venue:** Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies · **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/P11-1144/) · [PDF](https://aclanthology.org/P11-1144.pdf)

**Citations:** [41](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary

Frame-semantic parsers trained on FrameNet struggle with predicates that never appear in the lexicon or its annotated sentences. FrameNet lists only about 9,300 annotated target types. Das and Smith address this coverage gap with graph-based semi-supervised learning. They build a large graph whose vertices are lemmatized targets with coarse POS tags. Most vertices come from Lin's dependency-based distributional thesaurus, and a smaller share are targets already annotated in FrameNet 1.5. Edge weights combine distributional similarity with similarity between frame distributions of observed targets. Label propagation then spreads frame distributions from known targets to unknown ones. When the SEMAFOR parser meets an unseen target, it considers only the top-ranked frames from the propagated distribution rather than the whole frame inventory. For unseen targets, this raises frame identification accuracy by 15.7 points and full-parse F1 by 13.7 points over the supervised SEMAFOR baseline. It also makes decoding considerably faster. The method matters because it extends a hand-built semantic resource using unlabeled data rather than other expert lexicons. The authors expect even larger benefits in domains outside news.

## Key points

- **Graph construction:** The graph has 61,702 target vertices, roughly six times the number of FrameNet targets. Vertices come from Lin's thesaurus, which was built from a 64-million-word parsed corpus. Each vertex is linked to its 20 nearest neighbours, and adjectives and adverbs were split using Gigaword POS statistics.
- **Gains on unseen targets:** Frame identification accuracy rises by 15.7 points and full frame-semantic parsing F1 by 13.7 points; both gains are significant. On the full test set, relative error drops by 5.4% for frame identification and 1.3% for full parsing, since only 3.2% of test targets are unseen.
- **Baseline comparison:** A self-training baseline did worse than SEMAFOR. A graph built only from the thesaurus (LinGraph) beat both baselines but trailed the combined-similarity graph (FullGraph).
- **Speed:** Restricting candidates to M = 2 frames instead of all 877 cuts frame identification time on unknown targets from 111 s to 19 s. On the full test set, it drops from 131 s to 39 s.
- **Availability:** The extended parser is released as SEMAFOR at ark.cs.cmu.edu.

*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

41 papers of our library cite it (of the 140673 whose bibliographies we read); 8 of them have a page here, 33 not yet.

- [The Dependency-Parsed FrameNet Corpus](https://md.research.elamur.ai/free/papers/bauer-2012-dependency-parsed-framenet-corpus/)
- [Frame-Semantic Parsing](https://md.research.elamur.ai/free/papers/das-2014-frame-semantic-parsing/)
- [Out-of-domain FrameNet Semantic Role Labeling](https://md.research.elamur.ai/free/papers/hartmann-2017-out-domain-framenet-semantic-role/)
- [Frame-Semantic Role Labeling with Heterogeneous Annotations](https://md.research.elamur.ai/free/papers/kshirsagar-2015-frame-semantic-role-labeling-heterogeneous/)
- [FrameNet+: Fast Paraphrastic Tripling of FrameNet](https://md.research.elamur.ai/free/papers/pavlick-2015-framenet-fast-paraphrastic-tripling-framenet/)
- [Context-aware Frame-Semantic Role Labeling](https://md.research.elamur.ai/free/papers/roth-2015-context-aware-frame-semantic-role/)
- [Frame-Semantic Parsing with Softmax-Margin Segmental RNNs and a Syntactic Scaffold](https://md.research.elamur.ai/free/papers/swayamdipta-2017-frame-semantic-parsing-softmax-margin/)
- [Revisiting the Evaluation for Cross Document Event Coreference](https://md.research.elamur.ai/free/papers/upadhyay-2016-revisiting-evaluation-cross-document-event/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{das2011semi,
  title = {{Semi-Supervised Frame-Semantic Parsing for Unknown Predicates}},
  author = {Das, Dipanjan and Smith, Noah A.},
  booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
  year = {2011},
  url = {https://aclanthology.org/P11-1144/}
}
```

- [← previous Selected Operations, Algorithms, and Applications of n-Tape Weighted Finite-State Machines](https://md.research.elamur.ai/free/papers/langstream-2011-kempe-selected-operations-algorithms-applications-n/)
- [next → Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions](https://md.research.elamur.ai/free/papers/socher-2011-semi-supervised-recursive-autoencoders-predicting/)
- [↑ back to papers of 2011](https://md.research.elamur.ai/free/papers/2011/)

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