Popular GitHub Repositories for Mentions

Multimodal entity linking (MEL) aims to utilize multimodal information to map mentions to corresponding entities defined in knowledge bases. We release three MEL datasets: Weibo-MEL, Wikidata-MEL and Richpedia-MEL, containing 25,602, 18,880 and 17,806 samples from social media, encyclopedia and multimodal knowledge graphs respectively. A MEL dataset construction approach is proposed, including five stages: multimodal information extraction, mention extraction, entity extraction, triple construction and dataset construction. Experiment results demonstrate the usability of the datasets and the distinguishability between baseline models.

A real time analytics dashboard to analyze the trending hashtags and @ mentions at any location using kafka and spark streaming.

Twitter AI Tweet bot automatically responds to mentions and tweet replies on Twitter.

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.

This repository contains model for NER trained on clinical data to extract names of diseases from unstructured text. Named-entity recognition (NER) (also known as entity extraction) is a sub-task of information extraction that seeks to locate and classify named entity mentions in unstructured text into pre-defined categories such as the person names, organizations etc.

This app searches reddit posts and comments to determine if a product or service has a positive or negative sentiment and predicts top product mentions using Named Entity Recognition

Finds location mentions in speech using an LSTM with prosodic features as input

Experiments for the paper "Detecting ongoing events using contextual word and sentence embeddings" using BERT embeddings and LSTMs neural networks to detect ongoing event mentions in news articles.

System for Training-based Expansion of Tools for Proper Name Mentions Recognition Based on Active Learning