8. An Unsupervised Neural Attention Model for Aspect Extraction#
Code for the ACL2017 paper “An Unsupervised Neural Attention Model for Aspect Extraction”.
Aspect extraction is one of the key tasks in sentiment analysis. It aims to extract entity aspects on which opinions
have been expressed. For example, in the sentence “The beef was tender and melted in my mouth”, the aspect term is “beef”.
Experimental results on real-life datasets demonstrate that our approach discovers more meaningful and coherent aspects,
and substantially outperforms baseline methods on several evaluation tasks.
Aspect-Based Sentiment analysis (ABSA) can then be performed on the set of aspects in the downstream task.
NLP scientist at Alibaba DAMO Academy. Ph.D. from NUS.
7. ✍🏻 gpt2-client: Easy-to-use TensorFlow Wrapper for GPT-2 🤖 📝#
GPT-2 is a Natural Language Processing model developed by OpenAI for text generation. The model has 4 versions -
117M, 345M, 774M, and 1558M - that differ in terms of the amount of training data fed to it and the number of parameters
gpt2-client is a wrapper around the original gpt-2 repository that features the same functionality but with more
accessiblity, comprehensibility, and utilty. You can play around with all four GPT-2 models in less than five lines of code.
A Python port of SymSpell, a 1 million times faster
spelling correction & fuzzy search through Symmetric Delete spelling correction algorithm.
The Symmetric Delete spelling correction algorithm reduces the complexity of edit candidate generation and dictionary
lookup for a given Damerau-Levenshtein distance. It is six orders of magnitude faster (than the standard approach with
deletes + transposes + replaces + inserts) and language independent.
4. Textstat: Python Package to Calculate Readability Statistics of Text#
Textstat is an easy to use library to calculate statistics from text. It helps determine readability, complexity, and grade level.
It supports various statistics including: Flesch Reading Ease Score, Flesch-Kincaid Grade Level, Fog Scale (Gunning FOG Formula),
SMOG Index, Automated Readability Index, Coleman-Liau Index, Linsear Write Formula and the Dale-Chall Readability Score.
Data Scientist | Natural Language Processing + Machine Learning Enthusiast | Data Stories + Visuals | Programmer + Coder | at H2O.ai
3. Sentiment analysis on tweets using Naive Bayes, SVM, CNN, LSTM, etc.#
Sentiment classification on twitter dataset. The authors use a number of machine learning and deep learning methods to
perform sentiment analysis (Naive Bayes, SVM, CNN, LSTM, etc.). The authors finally use a majority vote ensemble method
with 5 of our best models to achieve the classification accuracy of 83.58% on kaggle public leaderboard.
A fork from the RASA (contextual AI assistant/chatbot) NLU repository. Focused on the Natural Language Understanding (NLU)
Turn a chinese natural language sentence/utterance into structured data.
1. Chinese Named Entity Recognition and Relation Extraction#
An NLP repository including state-of-art deep learning methods for various tasks in chinese/mandarin language (中文):
named entity recognition (NER/实体识别), relation extraction (RE/关系提取) and word segmentation.