Discover @maria-becker Open Source projects

Maria Becker (@maria-becker)

maria-becker

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Registered almost 5 years ago
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Python
66.7 %

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Location 🇩🇪 Germany
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CoCo-Ex

CoCo-Ex extracts meaningful concepts from natural language texts and maps them to conjunct concept nodes in ConceptNet, utilizing the maximum of relational information stored in the ConceptNet knowledge graph.

LMs4Implicit-Knowledge-Generation

Code for equipping pretrained language models (BART, GPT-2, XLNet) with commonsense knowledge for generating implicit knowledge statements between two sentences, by (i) finetuning the models on corpora enriched with implicit information; and by (ii) constraining models with key concepts and commonsense knowledge paths connecting them.

IKAT-EN

English version of IKAT: A corpus consisting of high-quality human annotations of missing and implied information in argumentative texts. The data is further annotated with semantic clause types and commonsense knowledge relations.

Moralization

Jupyter Notebook

CO-NNECT

This repository contains our path generation framework Co-NNECT, in which we combine two models for establishing knowledge relations and paths between concepts from sentences, as a form of explicitation of implicit knowledge: COREC-LM (COmmonsense knowledge RElation Classification using Language Models), a relation classification system that we developed for classifying commonsense knowledge relations; and COMET, a target prediction system developed by Bosselut et al., 2019.

IKAT-DE

German version of IKAT: A corpus consisting of high-quality human annotations of missing and implied information in argumentative texts. The data is further annotated with semantic clause types and commonsense knowledge relations.

Python4NLP

Jupyter Notebook

RNN_for_Clause_Classification

This is a Classifier for situation entity types as described in Becker et al., 2017. These clause types depend on a combination of syntactic-semantic and contextual features. We explore this task in a deeplearning framework, where tuned word representations capture lexical, syntactic and semantic features.