Search Results for author: Robin Kurtz

Found 6 papers, 3 papers with code

Direct parsing to sentiment graphs

1 code implementation ACL 2022 David Samuel, Jeremy Barnes, Robin Kurtz, Stephan Oepen, Lilja Øvrelid, Erik Velldal

This paper demonstrates how a graph-based semantic parser can be applied to the task of structured sentiment analysis, directly predicting sentiment graphs from text.

Sentiment Analysis

Structured Sentiment Analysis as Dependency Graph Parsing

2 code implementations ACL 2021 Jeremy Barnes, Robin Kurtz, Stephan Oepen, Lilja Øvrelid, Erik Velldal

Structured sentiment analysis attempts to extract full opinion tuples from a text, but over time this task has been subdivided into smaller and smaller sub-tasks, e, g,, target extraction or targeted polarity classification.

Sentiment Analysis

End-to-End Negation Resolution as Graph Parsing

no code implementations WS 2020 Robin Kurtz, Stephan Oepen, Marco Kuhlmann

We present a neural end-to-end architecture for negation resolution based on a formulation of the task as a graph parsing problem.


Improving Semantic Dependency Parsing with Syntactic Features

no code implementations WS 2019 Robin Kurtz, Daniel Roxbo, Marco Kuhlmann

We extend a state-of-the-art deep neural architecture for semantic dependency parsing with features defined over syntactic dependency trees.

Dependency Parsing Semantic Dependency Parsing

Exploiting Structure in Parsing to 1-Endpoint-Crossing Graphs

no code implementations WS 2017 Robin Kurtz, Marco Kuhlmann

Deep dependency parsing can be cast as the search for maximum acyclic subgraphs in weighted digraphs.

Dependency Parsing Sentence

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