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https://github.com/textvec/textvec

Text vectorization tool to outperform TFIDF for classification tasks
https://github.com/textvec/textvec

machine-learning natural-language-processing nlp python text-analysis text-classification text-processing tf-idf

Last synced: about 5 hours ago
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Text vectorization tool to outperform TFIDF for classification tasks

Host: GitHub
URL: https://github.com/textvec/textvec
Owner: textvec
License: mit
Created: 2018-04-12T14:03:53.000Z (almost 7 years ago)
Default Branch: master
Last Pushed: 2024-06-17T22:44:04.000Z (7 months ago)
Last Synced: 2025-01-05T18:25:16.809Z (7 days ago)
Topics: machine-learning, natural-language-processing, nlp, python, text-analysis, text-classification, text-processing, tf-idf
Language: Python
Homepage:
Size: 799 KB
Stars: 194
Watchers: 8
Forks: 26
Open Issues: 5
Metadata Files:
- Readme: README.md
- License: LICENSE

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awesome-python-machine-learning-resources - GitHub - 33% open · ⏱️ 05.07.2022): (文本数据和NLP)

README

        ![textvec logo](/examples/images/logo.png?raw=true)

## 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](https://github.com/scikit-learn/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__.

------------------

## 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.

![Binary comparison](/examples/images/imdb_bin.png?raw=true)

For more dataset benchmarks (rotten tomatoes, airline sentiment) see [Binary classification quality comparison](/examples/binary_comparison.ipynb)

------------------

## Install:

Usage:

```

pip install textvec

```

Source code:

```

git clone https://github.com/textvec/textvec

cd textvec

pip install .

```

------------------

## HOW: Examples

The usage is similar to scikit-learn:

``` python

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](/examples/basic_usage.ipynb) and other notebooks in [Examples](/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!

------------------

## TODO

- [ ] Docs

------------------

## REFERENCE

- [1] [Deqing Wang and Hui Zhang] [Inverse-Category-Frequency based Supervised Term Weighting Schemes for Text Categorization](https://arxiv.org/pdf/1012.2609.pdf)

- [2] [M. Lan, C. L. Tan, J. Su, and Y. Lu] [Supervised and traditional term weighting methods for automatic text categorization](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.151.3665&rep=rep1&type=pdf)

- [3] [Sanjeev Arora, Yingyu Liang and Tengyu Ma] [A Simple But Tough-To-Beat Baseline For Sentence Embeddings](https://openreview.net/pdf?id=SyK00v5xx)

- [4] Thanks [aysent](https://aysent.github.io/2015/10/21/supervised-term-weighting.html#motivation-for-text-classification-tasks) for an inspiration