xhuang28

anago

Bidirectional LSTM-CRF and ELMo for Named-Entity Recognition, Part-of-Speech Tagging and so on.

ASER

ASER (activities, states, events, and their relations), a large-scale eventuality knowledge graph extracted from more than 11-billion-token unstructured textual data.

AutoPhrase

AutoPhrase: Automated Phrase Mining from Massive Text Corpora

BERT-NER

Use Google's BERT for named entity recognition （CoNLL-2003 as the dataset）.

biobert

BioBERT: a pre-trained biomedical language representation model for biomedical text mining

blockchain_bootcamp

commonsenseqa

CoreNLP

Stanford CoreNLP: A Java suite of core NLP tools.

flair

A very simple framework for state-of-the-art Natural Language Processing (NLP)

NewBioNer

xhuang28/NewBioNer

xhuang28/anago

Bidirectional LSTM-CRF and ELMo for Named-Entity Recognition, Part-of-Speech Tagging and so on.

xhuang28/ASER

ASER (activities, states, events, and their relations), a large-scale eventuality knowledge graph extracted from more than 11-billion-token unstructured textual data.

xhuang28/AutoPhrase

AutoPhrase: Automated Phrase Mining from Massive Text Corpora

xhuang28/BERT-NER

Use Google's BERT for named entity recognition （CoNLL-2003 as the dataset）.

xhuang28/biobert

BioBERT: a pre-trained biomedical language representation model for biomedical text mining

xhuang28/blockchain_bootcamp

xhuang28/commonsenseqa

xhuang28/CoreNLP

Stanford CoreNLP: A Java suite of core NLP tools.

xhuang28/flair

A very simple framework for state-of-the-art Natural Language Processing (NLP)

xhuang28/GCDT

Code for the paper: GCDT: A Global Context Enhanced Deep Transition Architecture for Sequence Labeling

xhuang28/giza-pp

GIZA++ is a statistical machine translation toolkit that is used to train IBM Models 1-5 and an HMM word alignment model. This package also contains the source for the mkcls tool which generates the word classes necessary for training some of the alignment models.

xhuang28/iclr2016

Python code for training all models in the ICLR paper, "Towards Universal Paraphrastic Sentence Embeddings". These models achieve strong performance on semantic similarity tasks without any training or tuning on the training data for those tasks. They also can produce features that are at least as discriminative as skip-thought vectors for semantic similarity tasks at a minimum. Moreover, this code can achieve state-of-the-art results on entailment and sentiment tasks.

xhuang28/is-xhuang1994

Distinguish Bots from Humans on Twitter

xhuang28/LM-LSTM-CRF

Empower Sequence Labeling with Task-Aware Language Model

xhuang28/MACROSCORE

MACROSCORE project at ISI - Micro Feature Extraction direction

xhuang28/mmner

Massively Multilingual Transfer for NER

xhuang28/mrc-for-flat-nested-ner

The code for "A Unified MRC Framework for Named Entity Recognition"

xhuang28/NER-GRN

Code for our AAAI2019 paper "GRN: Gated Relation Network to Enhance Convolutional Neural Network for Named Entity Recognition"

xhuang28/nlproc-cookbook

xhuang28/nltk_contrib

NLTK Contrib

xhuang28/OntoNotes-5.0-NER-BIO

A BIO formatted Named Entity Recognition data set extracted from the OntoNotes 5.0 release.

xhuang28/para-nmt-50m

Pre-trained models and code and data to train and use models from "Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations"

xhuang28/python-wordsegment

English word segmentation, written in pure-Python, and based on a trillion-word corpus.

xhuang28/scpn

syntactically controlled paraphrase networks

xhuang28/semi-supervised-baselines

Code for "Strong Baselines for Neural Semi-supervised Learning under Domain Shift" (Ruder & Plank, 2018 ACL)

xhuang28/Vanilla_NER

Vanilla Sequence Labeling w. Char-LSTM-CRF