spaCy pipeline object for negating concepts in text
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Updated
Feb 26, 2024 - Python
spaCy pipeline object for negating concepts in text
SFU Opinion and Comments Corpus
Create a javascript regular expression for matching everything except for the given string.
Relational Operators for Intervals
Implementation of an Image Processing Library for time consuming operations such as Image Blurring,Negation,Edge Detection and Contrast Stretching.
Returns an object with a `negated` boolean and the `!` stripped from negation patterns. Also respects extglobs.
A Certifier algorithm to check a particular solution to the NP-Complete 3-Sat problem
Supplementary data for the LREC 2018 paper "Introducing a Lexicon of Verbal Polarity Shifters for English" by Schulder, Wiegand, Ruppenhofer and Köser.
code for our paper "Understanding by Understanding Not: Modeling Negation in Language Models"
negate_sentence(A Python module that doesn't negate sentences.)
Program that show user sentiments while giving reviews for different restaurants (Negative or Positive). Positive= 1.0 | >1.0 Negative= -1.0 | < -1.0 In case of positive sentiments, It will give 4 stars for 1.0 and 5 for > 1.0 In case of Negative sentiments, It will give 2 stars for -1.0 and 1 for < -1.0 i.e: -2.0 or less In case of Neutral sent…
Supplementary data for the IJCNLP 2017 paper "Towards Bootstrapping a Polarity Shifter Lexicon using Linguistic Features" by Schulder, Wiegand, Ruppenhofer and Roth.
Software project
CIDOC-CRM extension for typed and negative typed properties
High-performance tool for negation and uncertainty detection in radiology reports
Supplementary data for the COLING 2018 paper "Automatically Creating a Lexicon of Verbal Polarity Shifters: Mono- and Cross-lingual Methods for German" by Schulder, Wiegand and Ruppenhofer.
An alternative to the sneaky ! negation operator for Javascript
Code and data for negation cue detection project.
Supplementary data for the LREC 2020 paper "Enhancing a Lexicon of Polarity Shifters through the Automatic Addition of Shifting Directions" by Schulder, Wiegand and Ruppenhofer.
Comparing Data-Driven Techniques for Enhancing Negation Sensitivity in MLM-Based Laguage-Models
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