Search Results for author: Sheshera Mysore

Found 8 papers, 3 papers with code

MS-Mentions: Consistently Annotating Entity Mentions in Materials Science Procedural Text

no code implementations EMNLP 2021 Tim O’Gorman, Zach Jensen, Sheshera Mysore, Kevin Huang, Rubayyat Mahbub, Elsa Olivetti, Andrew McCallum

Material science synthesis procedures are a promising domain for scientific NLP, as proper modeling of these recipes could provide insight into new ways of creating materials.

Named Entity Recognition

Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity

1 code implementation16 Nov 2021 Sheshera Mysore, Arman Cohan, Tom Hope

We present a new scientific document similarity model based on matching fine-grained aspects of texts.

CSFCube -- A Test Collection of Computer Science Research Articles for Faceted Query by Example

1 code implementation24 Mar 2021 Sheshera Mysore, Tim O'Gorman, Andrew McCallum, Hamed Zamani

Query by Example is a well-known information retrieval task in which a document is chosen by the user as the search query and the goal is to retrieve relevant documents from a large collection.

Information Retrieval

An Instance Level Approach for Shallow Semantic Parsing in Scientific Procedural Text

no code implementations Findings of the Association for Computational Linguistics 2020 Daivik Swarup, Ahsaas Bajaj, Sheshera Mysore, Tim O{'}Gorman, Rajarshi Das, Andrew McCallum

Fortunately, such specific domains often use rather formulaic writing, such that the different ways of expressing relations in a small number of grammatically similar labeled sentences may provide high coverage of semantic structures in the corpus, through an appropriately rich similarity metric.

Semantic Parsing

Roll Call Vote Prediction with Knowledge Augmented Models

no code implementations CONLL 2019 Pallavi Patil, Kriti Myer, Ronak Zala, Arpit Singh, Sheshera Mysore, Andrew McCallum, Adrian Benton, Am Stent, a

The sources of knowledge we use are news text and Freebase, a manually curated knowledge base.

Automatically Extracting Action Graphs from Materials Science Synthesis Procedures

no code implementations18 Nov 2017 Sheshera Mysore, Edward Kim, Emma Strubell, Ao Liu, Haw-Shiuan Chang, Srikrishna Kompella, Kevin Huang, Andrew McCallum, Elsa Olivetti

In this work, we present a system for automatically extracting structured representations of synthesis procedures from the texts of materials science journal articles that describe explicit, experimental syntheses of inorganic compounds.

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