# Sources of Complexity in Semantic Frame Parsing for Information Extraction

arXiv: https://arxiv.org/abs/1812.09193 · HTML: https://mlsanity.com/paper/1812.09193/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2018.

## Built on by (most cited first, 1 of 1)

- [A linguistically-motivated evaluation methodology for unraveling model's abilities in reading comprehension tasks](https://mlsanity.com/paper/2501.17569/) (2025)

## Abstract (arXiv)

This paper describes a Semantic Frame parsing System based on sequence labeling methods, precisely BiLSTM models with highway connections, for performing information extraction on a corpus of French encyclopedic history texts annotated according to the Berkeley FrameNet formalism. The approach proposed in this study relies on an integrated sequence labeling model which jointly optimizes frame identification and semantic role segmentation and identification. The purpose of this study is to analyze the task complexity, to highlight the factors that make Semantic Frame parsing a difficult task and to provide detailed evaluations of the performance on different types of frames and sentences.