davidsbatista

David S. Batista's repositories

Annotated-Semantic-Relationships-Datasets

A collections of public and free annotated datasets of relationships between entities/nominals (Portuguese and English)

NER-datasets

Datasets to train supervised classifiers for Named-Entity Recognition in different languages (Portuguese, German, Dutch, French, English)

Language:Python333 14 5

NER-Evaluation

An implementation of a full named-entity evaluation metrics based on SemEval'13 Task 9 - not at tag/token level but considering all the tokens that are part of the named-entity

Language:PythonMIT206 10 16

Snowball

Implementation with some extensions of the paper "Snowball: Extracting Relations from Large Plain-Text Collections" (Agichtein and Gravano, 2000)

Language:PythonGPL-3.0176 8 7

BREDS

"Bootstrapping Relationship Extractors with Distributional Semantics" (Batista et al., 2015) in EMNLP'15 - Python implementation

Language:PythonLGPL-3.0145 11 19

Aspect-Based-Sentiment-Analysis

Aspect-Based Sentiment Analysis Experiments

Language:Python132 7 4

text-classification

An example on how to train supervised classifiers for multi-label text classification using sklearn pipelines

Language:Jupyter Notebook110 9 4

ConvNets-for-Sentence-Classification

"Convolutional Neural Networks for Sentence Classification" (Kim 2014) - https://www.aclweb.org/anthology/D14-1181

Language:Jupyter NotebookGPL-3.054 5 1

machine-learning-notebooks

Assorted exercises and proof-of-concepts to understand and study machine learning and statistical learning theory

Language:Jupyter Notebook44 5 4

Resources developed by and for the project REACTION (Retrieval, Extraction and Aggregation Computing Technology for Integrating and Organizing News) an initiative for developing a computational journalism platform (mostly) for Portuguese.

9 50

StanfordNER-experiments

Language:Python8 30

SLANG-Sequence-LAbeliNG

Sequence LAbeliNG with Neural Networks: "Neural Architectures for Named Entity Recognition" (Lample et al., 2016) and "End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF" (Ma, 2016)

Language:Jupyter Notebook7 30