---
title: "Frame-Semantic Role Labeling with — Kshirsagar 2015"
description: "The paper addresses argument identification in frame-semantic parsing: given a predicate and the FrameNet frame it evokes, the system must find the…"
authors: ["Meghana Kshirsagar", "Sam Thomson", "Nathan Schneider", "Jaime Carbonell", "Noah A. Smith", "Chris Dyer"]
year: 2015
venue: "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)"
source: "https://aclanthology.org/P15-2036/"
doi: "10.3115/v1/p15-2036"
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/kshirsagar-2015-frame-semantic-role-labeling-heterogeneous/"
---
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# Frame-Semantic Role Labeling with Heterogeneous Annotations

**Authors:** Meghana Kshirsagar, [Sam Thomson](https://md.research.elamur.ai/free/authors/sam-thomson/), [Nathan Schneider](https://md.research.elamur.ai/free/authors/nathan-schneider/), [Jaime Carbonell](https://md.research.elamur.ai/free/authors/jaime-carbonell/), [Noah A. Smith](https://md.research.elamur.ai/free/authors/noah-a-smith/), [Chris Dyer](https://md.research.elamur.ai/free/authors/chris-dyer/) · **Year:** 2015 · **Venue:** Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) · **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/P15-2036/) · [PDF](https://aclanthology.org/P15-2036.pdf) · [DOI 10.3115/v1/p15-2036](https://doi.org/10.3115/v1/p15-2036) · [OpenAlex](https://openalex.org/W2251332263)

**Citations:** 60 citations (openalex-snapshot · 2026-10-14) · [28](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
The paper addresses argument identification in frame-semantic parsing: given a predicate and the FrameNet frame it evokes, the system must find the phrases that fill the frame's roles and label them. Fully annotated training data is scarce, with only a few thousand sentences. The authors extend SEMAFOR, an open-source frame-semantic parser for English, with three additional sources of supervision:

- **FrameNet hierarchy features:** features copied onto parent roles via Inheritance and Subframe relations, so related roles share evidence.
- **Lexicographic exemplars:** FrameNet's large set of partially annotated sentences, added as training data through Daumé-style feature augmentation for domain adaptation.
- **PropBank guide features:** predictions from an external PropBank-style SRL system used as extra features.

Training was also made faster by switching to AdaDelta with a squared structured hinge loss. On the FrameNet 1.5 full-text test set, combining these techniques raises argument F1 by about 4 points absolute. With automatically predicted frames, full parsing improves by about 1 point. The work shows that heterogeneous, imperfectly matched resources can compensate for small in-domain corpora in semantic analysis.

## Key points
- Argument identification F1 on the full-text test set:
  - FT baseline: 59.1%
  - Hierarchy features: 60.4%
  - FT+Exemplars with domain adaptation: 62.2%
  - Exemplars as guide features: 60.2%
  - PB-SRL guide features: 59.5%
  - Best combination (FT+Exemplars + PB-SRL guide + hierarchy): 63.1%
- Data used:
  - FrameNet 1.5 has over 1,000 frames and 12,000 lexical predicates.
  - The full-text portion has about 5,000 sentences.
  - The exemplar corpus has over 140,000 sentences.
  - Role types are connected through 589 Subframe relations and 4,138 Inheritance relations.
- PropBank guide features came from the Illinois Curator 1.1.4 SRL system.
- In preliminary experiments, training directly on PropBank data mapped to FrameNet through SemLink lowered performance.
- Replacing L-BFGS with minibatch AdaDelta cut training time from 729 to 82 minutes, at a cost of only 0.2 F1 (59.3% → 59.1%).

*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

28 papers of our library cite it (of the 140673 whose bibliographies we read); 6 of them have a page here, 22 not yet.

- [Open-source Frame Semantic Parsing](https://md.research.elamur.ai/free/papers/chanin-2023-open-source-frame-semantic-parsing/)
- [Incorporating Syntax and Frame Semantics in Neural Network for Machine Reading Comprehension](https://md.research.elamur.ai/free/papers/guo-2020-incorporating-syntax-frame-semantics-neural/)
- [Out-of-domain FrameNet Semantic Role Labeling](https://md.research.elamur.ai/free/papers/hartmann-2017-out-domain-framenet-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/)
- [A Joint Sequential and Relational Model for Frame-Semantic Parsing](https://md.research.elamur.ai/free/papers/yang-2017-joint-sequential-relational-model-frame/)
- [A Double-Graph Based Framework for Frame Semantic Parsing](https://md.research.elamur.ai/free/papers/zheng-2022-double-graph-based-framework-frame/)

## Cite

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

```bibtex
@inproceedings{kshirsagar2015frame,
  title = {{Frame-Semantic Role Labeling with Heterogeneous Annotations}},
  author = {Kshirsagar, Meghana and Thomson, Sam and Schneider, Nathan and Carbonell, Jaime and Smith, Noah A. and Dyer, Chris},
  booktitle = {Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
  year = {2015},
  doi = {10.3115/v1/p15-2036},
  url = {https://doi.org/10.3115/v1/p15-2036}
}
```

- [← previous Fitting Linear Mixed-Effects Models Using lme4](https://md.research.elamur.ai/free/papers/bates-2015-fitting-linear-mixed-effects-models/)
- [next → FrameNet+: Fast Paraphrastic Tripling of FrameNet](https://md.research.elamur.ai/free/papers/pavlick-2015-framenet-fast-paraphrastic-tripling-framenet/)
- [↑ back to papers of 2015](https://md.research.elamur.ai/free/papers/2015/)

## References

34 references, 14 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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