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Urban Dict spelling variant dataset. Source code of How to Evaluate Word Representations of Informal Domain?

Home Page: https://arxiv.org/abs/1911.04669

Python 12.44% Shell 1.10% Makefile 0.12% MATLAB 0.42% C 6.13% Jupyter Notebook 66.53% Roff 8.67% C++ 4.27% Perl 0.09% TSQL 0.22%
semi-structured-data information-extraction sequence-tagger word-representation-evaluation hashtag-prediction urban-dictionary

urbandict's Introduction

Discovering spelling variants on Urban Dictionary

Source code of the paper How to Evaluate Word Representations of Informal Domain?

Scraping data from Urban Dictionary ๐ŸŽ

  • Scraping data from webpage:
+ scrapy crawl UD
  • Scrapying data via API:
+ scrapy crawl UD_API

Bootstrapping algorithms

UD_Extractor/

self-training based CRF tagging

SeqLabeling/

Embedding pretraining with Tweets

train Word2Vec, FastText, GloVe with tweets data. `trainEmbedding/'

Twitter hashtag prediction task using pretrained embedding

Employ Twitter hashtag prediction downstream task using above pretrained informal word vectors as the extrinsic evaluation. HashtagPrediction/

Analysis

Use Mean Average Precision (MAP) as the intrinsic evaluation rate on word analogy task. Compare the correlations beween the intrinsic and extrinsic tasks. calcSim

Web interface

informal word pair search tool, written in Flask: demo/

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