WHAT: Supervised text vectorization tool

Textvec is a text vectorization tool, with the aim to implement all the "classic" text vectorization NLP methods in Python. The main idea of this project is to show alternatives for an excellent TFIDF method which is highly overused for supervised tasks. All interfaces are similar to scikit-learn so you should be able to test the performance of this supervised methods just with a few changes.

Textvec is compatible with: Python 2.7-3.7.

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WHY: Comparison with TFIDF

As you can read in the different articles^1,2 almost on every dataset supervised methods outperform unsupervised. But most text classification examples on the internet ignores that fact.

	IMDB_bin	RT_bin	Airlines Sentiment_bin	Airlines Sentiment_multiclass	20news_multiclass
TF	0.8984	0.7571	0.9194	0.8084	0.8206
TFIDF	0.9052	0.7717	0.9259	0.8118	0.8575
TFPF	0.8813	0.7403	0.9212	NA	NA
TFRF	0.8797	0.7412	0.9194	NA	NA
TFICF	0.8984	0.7642	0.9199	0.8125	0.8292
TFBINICF	0.8984	0.7571	0.9194	NA	NA
TFCHI2	0.8898	0.7398	0.9108	NA	NA
TFGR	0.8850	0.7065	0.8956	NA	NA
TFRRF	0.8879	0.7506	0.9194	NA	NA
TFOR	0.9092	0.7806	0.9207	NA	NA

Here is a comparison for binary classification on imdb sentiment data set. Labels sorted by accuracy score and the heatmap shows the correlation between different approaches. As you can see some methods are good for to ensemble models or perform features selection.

For more dataset benchmarks (rotten tomatoes, airline sentiment) see Binary classification quality comparison

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Install:

Usage:

pip install textvec

Source code:

git clone https://github.com/textvec/textvec
cd textvec
pip install .

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HOW: Examples

The usage is similar to scikit-learn:

from sklearn.feature_extraction.text import CountVectorizer
from textvec.vectorizers import TfBinIcfVectorizer

cvec = CountVectorizer().fit(train_data.text)

tficf_vec = TfBinIcfVectorizer(sublinear_tf=True)
tficf_vec.fit(cvec.transform(text), y)

For more detailed examples see Basic example and other notebooks in Examples

Currently implemented methods:

• TfIcfVectorizer
• TforVectorizer
• TfgrVectorizer
• TfigVectorizer
• Tfchi2Vectorizer
• TfrfVectorizer
• TfrrfVectorizer
• TfBinIcfVectorizer
• TfpfVectorizer
• SifVectorizer
• TfbnsVectorizer

Most of the vectorization techniques you can find in articles^1,2,3. If you see any method with wrong name or reference please commit!

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TODO

• Docs

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REFERENCE

• [1] [Deqing Wang and Hui Zhang] Inverse-Category-Frequency based Supervised Term Weighting Schemes for Text Categorization
• [2] [M. Lan, C. L. Tan, J. Su, and Y. Lu] Supervised and traditional term weighting methods for automatic text categorization
• [3] [Sanjeev Arora, Yingyu Liang and Tengyu Ma] A Simple But Tough-To-Beat Baseline For Sentence Embeddings
• [4] Thanks aysent for an inspiration