Papers by Emily Saldanha

2 papers
Unsupervised Keyphrase Extraction via Interpretable Neural Networks (2023.findings-eacl)

Copied to clipboard

Challenge: Prior approaches for unsupervised keyphrase extraction relied on heuristic notions of phrase importance via embedding clustering or graph centrality.
Approach: They propose an approach which defines keyphrases as document phrases that are salient for predicting the topic of the document.
Outcome: The proposed method alleviates the need for ad-hoc heuristics and achieves state-of-the-art results in scientific publications and news articles.
Extracting Material Property Measurement Data from Scientific Articles (2021.emnlp-main)

Copied to clipboard

Challenge: a lack of large training datasets hampers machine learning-based prediction of material properties . relevant measurements and information exist only in unstructured formats such as the published literature .
Approach: They propose a framework for automatic property extraction using material solubility as the target property.
Outcome: The proposed framework extracts solubility data from scientific literature and compares it with other frameworks.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations