Post-processing filter for (Named) Entity Linking
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Updated
Jan 27, 2022 - Python
Entity resolution (also known as data matching, data linkage, record linkage, and many other terms) is the task of finding entities in a dataset that refer to the same entity across different data sources (e.g., data files, books, websites, and databases). Entity resolution is necessary when joining different data sets based on entities that may or may not share a common identifier (e.g., database key, URI, National identification number), which may be due to differences in record shape, storage location, or curator style or preference.
Post-processing filter for (Named) Entity Linking
Web interface to manually annotate named entity mentions in newspaper articles with the correct DBpedia link(s), if any. Produces labeled data sets for training and evaluating the DAC Entity Linker.
Entity search engines for Google and Wikidata. Helping to link the two together and provide a way to ground entities
Tool for Information extraction from Russian texts
Code of "A Read-and-Select Framework for Zero-shot Entity Linking" (EMNLP 2023 Findings).
[EMNLP 2021] MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations
Code for the ASONAM2018 paper Acquiring Background Knowledge to Improve Moral Value Prediction
The code for paper “Modeling Fine-grained Information via Knowledge-aware Hierarchical Graph for Zero-shot Entity Retrieval” in WSDM2023
MIC-CIS entry in PharmaCoNER, Bacteria Biotope (BB 2029) & SeeDev 2019 Shared Tasks in EMNLP '19
Simple exact-matching algorithm to standardise drug names appearing on the DrugBank open-access repository
Simple SNOMED-CT assistant app demo by Streamlit.
Collection of Python scripts to build a Solr index from selected Dutch and English DBpedia dumps.
Entity Linking system combination using weighted voting (*SEM 2015)
Entity linking of different keyphrase-extraction datasets via TagMe
A Python package to generate document profiles and extract metadata from text in parallel using several Docker images and NLP tools/frameworks.
A bi-encoder model for named entity linking
Python implementation of OPTIC approach for Entity Linking
Created by Halbert L. Dunn
Released 1946