# OpBerg: Discovering causal sentences using optimal alignments

arXiv: https://arxiv.org/abs/1904.02032 · HTML: https://mlsanity.com/paper/1904.02032/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2019.

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

- [Causal DAG extraction from a library of books or videos/movies](https://mlsanity.com/paper/2211.00486/) (2022)

## Abstract (arXiv)

The biological literature is rich with sentences that describe causal relations. Methods that automatically extract such sentences can help biologists to synthesize the literature and even discover latent relations that had not been articulated explicitly. Current methods for extracting causal sentences are based on either machine learning or a predefined database of causal terms. Machine learning approaches require a large set of labeled training data and can be susceptible to noise. Methods based on predefined databases are limited by the quality of their curation and are unable to capture new concepts or mistakes in the input. We address these challenges by adapting and improving a method designed for a seemingly unrelated problem: finding alignments between genomic sequences. This paper presents a novel and outperforming method for extracting causal relations from text by aligning the part-of-speech representations of an input set with that of known causal sentences. Our experiments show that when applied to the task of finding causal sentences in biological literature, our method improves on the accuracy of other methods in a computationally efficient manner.