limteng-rpi/neural_name_tagging
Code for "Reliability-aware Dynamic Feature Composition for Name Tagging" (ACL2019)
This project helps researchers and developers working with natural language understand and extract named entities (like people, places, or organizations) from text more accurately. It takes pre-processed text data, typically in CoNLL format, along with word embeddings, and outputs models that can identify these entities. The intended users are primarily NLP researchers, computational linguists, or data scientists focused on information extraction tasks.
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Use this if you are a researcher or NLP practitioner developing or evaluating named entity recognition (NER) systems and want to experiment with advanced feature composition techniques for improved reliability across different text genres.
Not ideal if you are a business user looking for an out-of-the-box solution for tagging names without needing to understand the underlying model architecture or training process.
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Python
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Last pushed
Nov 06, 2019
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