The Quick Wins of DevSecOps

Hello and welcome to my DevSecOps post. Here in Germany, it’s winter right now, and the forests are quiet. The snow slows down everything, and it’s a beautiful time to move undisturbed through the woods.

Here you can pursue your thoughts, and I had to think about a subject that customers or participants at conferences ask me repeatedly.

The question is almost always:

What are the quick wins or low hanging fruits if you want to deal more with the topic of security in software development?

And I want to answer this question right now!

Let’s start with the definition of a phrase that is often used in the business world:

Make or Buy

Even as a software developer, you will often hear this phrase during meetings with the company’s management and sales part.

The phrase is called; “Make or Buy.” Typically, we have to decide if we want to do something ourselves or spend money to buy the requested functionality. It could be less or more functionality or different so that we have to adjust ourselves to use it in our context.

But as a software developer, we have to deal with the same question every day. I am talking about dependencies. Should we write the source code by ourselves or just adding the next dependencies? Who will be responsible for removing bugs, and what is the total cost of this decision? But first, let’s take a look at the make-or-buy association inside the full tech-stack.

#java #security #kotlin

What is GEEK

Buddha Community

Jenny Jabde

1621251999

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Mia King

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What Is DevSecOps? and Ideas Of DevSecOps

What is DevSecOps? As teams adopt Continuous Delivery, DevOps, CI/CD for software development, being able to create systems that are safe and secure at speed, with great feedback and with high-quality becomes ever more important.

Using software engineering disciplines like Continuous Delivery to help improve software design is centred around creating a reliable and repeatable approach to delivering change. If you have security concerns that define the releasability of your systems, how can you employ these proven techniques that allow us to create “Better Software, Faster” to ensure that the security of your systems is not only not compromised, but improved.

In this episode Dave Farley leads us through an introduction to the ideas of DevSecOps, a kind of DevSecOps for beginners, and positions it in the broader context of Continuous Delivery, to help even experts see how to position DevSecOps and where to focus.

#devsecops #devops #cd

Rylan Becker

1663769400

Libraries for Working with Human Languages in Popular Python

In this Python article, let's learn about Natural Language Processing: Libraries for Working with Human Languages in Popular Python

General
- gensim - Topic Modeling for Humans.
- langid.py - Stand-alone language identification system.
- nltk - A leading platform for building Python programs to work with human language data.
- pattern - A web mining module.
- polyglot - Natural language pipeline supporting hundreds of languages.
- pytext - A natural language modeling framework based on PyTorch.
- PyTorch-NLP - A toolkit enabling rapid deep learning NLP prototyping for research.
- spacy - A library for industrial-strength natural language processing in Python and Cython.
- Stanza - The Stanford NLP Group's official Python library, supporting 60+ languages.
Chinese
- funNLP - A collection of tools and datasets for Chinese NLP.
- jieba - The most popular Chinese text segmentation library.
- pkuseg-python - A toolkit for Chinese word segmentation in various domains.
- snownlp - A library for processing Chinese text.

What is Python?

Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Its high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together. Python's simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance. Python supports modules and packages, which encourages program modularity and code reuse. The Python interpreter and the extensive standard library are available in source or binary form without charge for all major platforms, and can be freely distributed.

what is Natural Language Processing in python?

Natural language processing (NLP) is a field that focuses on making natural human language usable by computer programs. NLTK, or Natural Language Toolkit, is a Python package that you can use for NLP. A lot of the data that you could be analyzing is unstructured data and contains human-readable text.

Libraries for Working with Human Languages in Popular Python

gensim

Gensim is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.

Installation

This software depends on NumPy and Scipy, two Python packages for scientific computing. You must have them installed prior to installing gensim.

It is also recommended you install a fast BLAS library before installing NumPy. This is optional, but using an optimized BLAS such as MKL, ATLAS or OpenBLAS is known to improve performance by as much as an order of magnitude. On OSX, NumPy picks up its vecLib BLAS automatically, so you don’t need to do anything special.

Install the latest version of gensim:

    pip install --upgrade gensim

Or, if you have instead downloaded and unzipped the source tar.gz package:

    python setup.py install

For alternative modes of installation, see the documentation.

Gensim is being continuously tested under all supported Python versions. Support for Python 2.7 was dropped in gensim 4.0.0 – install gensim 3.8.3 if you must use Python 2.7.

View on GitHub

2. Langid.py

langid.py is a standalone Language Identification (LangID) tool.

The design principles are as follows:

Fast
Pre-trained over a large number of languages (currently 97)
Not sensitive to domain-specific features (e.g. HTML/XML markup)
Single .py file with minimal dependencies
Deployable as a web service

All that is required to run langid.py is >= Python 2.7 and numpy. The main script langid/langid.py is cross-compatible with both Python2 and Python3, but the accompanying training tools are still Python2-only.

langid.py is WSGI-compliant. langid.py will use fapws3 as a web server if available, and default to wsgiref.simple_server otherwise.

langid.py comes pre-trained on 97 languages (ISO 639-1 codes given):

af, am, an, ar, as, az, be, bg, bn, br, bs, ca, cs, cy, da, de, dz, el, en, eo, es, et, eu, fa, fi, fo, fr, ga, gl, gu, he, hi, hr, ht, hu, hy, id, is, it, ja, jv, ka, kk, km, kn, ko, ku, ky, la, lb, lo, lt, lv, mg, mk, ml, mn, mr, ms, mt, nb, ne, nl, nn, no, oc, or, pa, pl, ps, pt, qu, ro, ru, rw, se, si, sk, sl, sq, sr, sv, sw, ta, te, th, tl, tr, ug, uk, ur, vi, vo, wa, xh, zh, zu

The training data was drawn from 5 different sources:

JRC-Acquis
ClueWeb 09
Wikipedia
Reuters RCV2
Debian i18n

View on GitHub

3. NLTK

NLTK -- the Natural Language Toolkit -- is a suite of open source Python modules, data sets, and tutorials supporting research and development in Natural Language Processing. NLTK requires Python version 3.7, 3.8, 3.9 or 3.10.

For documentation, please visit nltk.org.

Contributing

Do you want to contribute to NLTK development? Great! Please read CONTRIBUTING.md for more details.

Donate

Have you found the toolkit helpful? Please support NLTK development by donating to the project via PayPal, using the link on the NLTK homepage.

Citing

If you publish work that uses NLTK, please cite the NLTK book, as follows:

Bird, Steven, Edward Loper and Ewan Klein (2009).
Natural Language Processing with Python.  O'Reilly Media Inc.

View on GitHub

4. Pattern

Pattern is a web mining module for Python. It has tools for:

Data Mining: web services (Google, Twitter, Wikipedia), web crawler, HTML DOM parser
Natural Language Processing: part-of-speech taggers, n-gram search, sentiment analysis, WordNet
Machine Learning: vector space model, clustering, classification (KNN, SVM, Perceptron)
Network Analysis: graph centrality and visualization.

It is well documented, thoroughly tested with 350+ unit tests and comes bundled with 50+ examples. The source code is licensed under BSD.

Example workflow

Example

This example trains a classifier on adjectives mined from Twitter using Python 3. First, tweets that contain hashtag #win or #fail are collected. For example: "$20 tip off a sweet little old lady today #win". The word part-of-speech tags are then parsed, keeping only adjectives. Each tweet is transformed to a vector, a dictionary of adjective → count items, labeled WIN or FAIL. The classifier uses the vectors to learn which other tweets look more like WIN or more like FAIL.

from pattern.web import Twitter
from pattern.en import tag
from pattern.vector import KNN, count

twitter, knn = Twitter(), KNN()

for i in range(1, 3):
    for tweet in twitter.search('#win OR #fail', start=i, count=100):
        s = tweet.text.lower()
        p = '#win' in s and 'WIN' or 'FAIL'
        v = tag(s)
        v = [word for word, pos in v if pos == 'JJ'] # JJ = adjective
        v = count(v) # {'sweet': 1}
        if v:
            knn.train(v, type=p)

print(knn.classify('sweet potato burger'))
print(knn.classify('stupid autocorrect'))

View on GitHub

5. polyglot

Polyglot is a natural language pipeline that supports massive multilingual applications.

Features

Tokenization (165 Languages)
Language detection (196 Languages)
Named Entity Recognition (40 Languages)
Part of Speech Tagging (16 Languages)
Sentiment Analysis (136 Languages)
Word Embeddings (137 Languages)
Morphological analysis (135 Languages)
Transliteration (69 Languages)

Developer

Rami Al-Rfou @ rmyeid gmail com

Quick Tutorial

import polyglot
from polyglot.text import Text, Word

Language Detection

text = Text("Bonjour, Mesdames.")
print("Language Detected: Code={}, Name={}\n".format(text.language.code, text.language.name))

Language Detected: Code=fr, Name=French

Tokenization

zen = Text("Beautiful is better than ugly. "
           "Explicit is better than implicit. "
           "Simple is better than complex.")
print(zen.words)

[u'Beautiful', u'is', u'better', u'than', u'ugly', u'.', u'Explicit', u'is', u'better', u'than', u'implicit', u'.', u'Simple', u'is', u'better', u'than', u'complex', u'.']

print(zen.sentences)

[Sentence("Beautiful is better than ugly."), Sentence("Explicit is better than implicit."), Sentence("Simple i

View on GitHub

6. PyText

PyText is a deep-learning based NLP modeling framework built on PyTorch. PyText addresses the often-conflicting requirements of enabling rapid experimentation and of serving models at scale. It achieves this by providing simple and extensible interfaces and abstractions for model components, and by using PyTorch’s capabilities of exporting models for inference via the optimized Caffe2 execution engine. We are using PyText in Facebook to iterate quickly on new modeling ideas and then seamlessly ship them at scale.

Installing PyText

PyText requires Python 3.6.1 or above.

To get started on a Cloud VM, check out our guide.

Get the source code:

  $ git clone https://github.com/facebookresearch/pytext
  $ cd pytext

Create a virtualenv and install PyText:

  $ python3 -m venv pytext_venv
  $ source pytext_venv/bin/activate
  (pytext_venv) $ pip install pytext-nlp

Detailed instructions and more installation options can be found in our Documentation. If you encounter issues with missing dependencies during installation, please refer to OS Dependencies.

View on GitHub

7. PyTorch-NLP

Basic Utilities for PyTorch Natural Language Processing (NLP)
PyTorch-NLP, or torchnlp for short, is a library of basic utilities for PyTorch NLP. torchnlp extends PyTorch to provide you with basic text data processing functions.

Installation 🐾

Make sure you have Python 3.6+ and PyTorch 1.0+. You can then install pytorch-nlp using pip:

pip install pytorch-nlp

Or to install the latest code via:

pip install git+https://github.com/PetrochukM/PyTorch-NLP.git

Docs

The complete documentation for PyTorch-NLP is available via our ReadTheDocs website.

Get Started

Within an NLP data pipeline, you'll want to implement these basic steps:

1. Load your Data 🐿

Load the IMDB dataset, for example:

from torchnlp.datasets import imdb_dataset

# Load the imdb training dataset
train = imdb_dataset(train=True)
train[0]  # RETURNS: {'text': 'For a movie that gets..', 'sentiment': 'pos'}

Load a custom dataset, for example:

from pathlib import Path

from torchnlp.download import download_file_maybe_extract

directory_path = Path('data/')
train_file_path = Path('trees/train.txt')

download_file_maybe_extract(
    url='http://nlp.stanford.edu/sentiment/trainDevTestTrees_PTB.zip',
    directory=directory_path,
    check_files=[train_file_path])

open(directory_path / train_file_path)

Don't worry we'll handle caching for you!

View on GitHub

8. spaCy

spaCy is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products.

spaCy comes with pretrained pipelines and currently supports tokenization and training for 60+ languages. It features state-of-the-art speed and neural network models for tagging, parsing, named entity recognition, text classification and more, multi-task learning with pretrained transformers like BERT, as well as a production-ready training system and easy model packaging, deployment and workflow management. spaCy is commercial open-source software, released under the MIT license.

Features

Support for 60+ languages
Trained pipelines for different languages and tasks
Multi-task learning with pretrained transformers like BERT
Support for pretrained word vectors and embeddings
State-of-the-art speed
Production-ready training system
Linguistically-motivated tokenization
Components for named entity recognition, part-of-speech-tagging, dependency parsing, sentence segmentation, text classification, lemmatization, morphological analysis, entity linking and more
Easily extensible with custom components and attributes
Support for custom models in PyTorch, TensorFlow and other frameworks
Built in visualizers for syntax and NER
Easy model packaging, deployment and workflow management
Robust, rigorously evaluated accuracy

📖 For more details, see the facts, figures and benchmarks.

View on GitHub | View on source

9. Stanza

Official Stanford NLP Python Library for Many Human Languages

The Stanford NLP Group's official Python NLP library. It contains support for running various accurate natural language processing tools on 60+ languages and for accessing the Java Stanford CoreNLP software from Python. For detailed information please visit our official website.

🔥 A new collection of biomedical and clinical English model packages are now available, offering seamless experience for syntactic analysis and named entity recognition (NER) from biomedical literature text and clinical notes. For more information, check out our Biomedical models documentation page.

References

If you use this library in your research, please kindly cite our ACL2020 Stanza system demo paper:

@inproceedings{qi2020stanza,
    title={Stanza: A {Python} Natural Language Processing Toolkit for Many Human Languages},
    author={Qi, Peng and Zhang, Yuhao and Zhang, Yuhui and Bolton, Jason and Manning, Christopher D.},
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    year={2020}
}

If you use our biomedical and clinical models, please also cite our Stanza Biomedical Models description paper:

@article{zhang2021biomedical,
    author = {Zhang, Yuhao and Zhang, Yuhui and Qi, Peng and Manning, Christopher D and Langlotz, Curtis P},
    title = {Biomedical and clinical {E}nglish model packages for the {S}tanza {P}ython {NLP} library},
    journal = {Journal of the American Medical Informatics Association},
    year = {2021},
    month = {06},
    issn = {1527-974X}
}

The PyTorch implementation of the neural pipeline in this repository is due to Peng Qi (@qipeng), Yuhao Zhang (@yuhaozhang), and Yuhui Zhang (@yuhui-zh15), with help from Jason Bolton (@j38), Tim Dozat (@tdozat) and John Bauer (@AngledLuffa). Maintenance of this repo is currently led by John Bauer.

If you use the CoreNLP software through Stanza, please cite the CoreNLP software package and the respective modules as described here ("Citing Stanford CoreNLP in papers"). The CoreNLP client is mostly written by Arun Chaganty, and Jason Bolton spearheaded merging the two projects together.

View on GitHub

10. funNLP

The Most Powerful NLP-Weapon Arsenal

NLP民工的乐园: 几乎最全的中文NLP资源库

在入门到熟悉NLP的过程中，用到了很多github上的包，遂整理了一下，分享在这里。

很多包非常有趣，值得收藏，满足大家的收集癖！如果觉得有用，请分享并star⭐，谢谢！

长期不定时更新，欢迎watch和fork！❤️❤️❤️❤️❤️

View on GitHub

11. jieba

“结巴”中文分词：做最好的 Python 中文分词组件

"Jieba" (Chinese for "to stutter") Chinese text segmentation: built to be the best Python Chinese word segmentation module.

Scroll down for English documentation.

特点

支持四种分词模式：
- 精确模式，试图将句子最精确地切开，适合文本分析；
- 全模式，把句子中所有的可以成词的词语都扫描出来, 速度非常快，但是不能解决歧义；
- 搜索引擎模式，在精确模式的基础上，对长词再次切分，提高召回率，适合用于搜索引擎分词。
- paddle模式，利用PaddlePaddle深度学习框架，训练序列标注（双向GRU）网络模型实现分词。同时支持词性标注。paddle模式使用需安装paddlepaddle-tiny，pip install paddlepaddle-tiny==1.6.1。目前paddle模式支持jieba v0.40及以上版本。jieba v0.40以下版本，请升级jieba，pip install jieba --upgrade 。PaddlePaddle官网
支持繁体分词
支持自定义词典
MIT 授权协议

安装说明

代码对 Python 2/3 均兼容

全自动安装：easy_install jieba 或者 pip install jieba / pip3 install jieba
半自动安装：先下载 http://pypi.python.org/pypi/jieba/ ，解压后运行 python setup.py install
手动安装：将 jieba 目录放置于当前目录或者 site-packages 目录
通过 import jieba 来引用
如果需要使用paddle模式下的分词和词性标注功能，请先安装paddlepaddle-tiny，pip install paddlepaddle-tiny==1.6.1。

View on GitHub

12. pkuseg-python

pkuseg 是基于论文[Luo et. al, 2019]的工具包。其简单易用，支持细分领域分词，有效提升了分词准确度。

主要亮点

pkuseg具有如下几个特点：

多领域分词。不同于以往的通用中文分词工具，此工具包同时致力于为不同领域的数据提供个性化的预训练模型。根据待分词文本的领域特点，用户可以自由地选择不同的模型。我们目前支持了新闻领域，网络领域，医药领域，旅游领域，以及混合领域的分词预训练模型。在使用中，如果用户明确待分词的领域，可加载对应的模型进行分词。如果用户无法确定具体领域，推荐使用在混合领域上训练的通用模型。各领域分词样例可参考 example.txt。
更高的分词准确率。相比于其他的分词工具包，当使用相同的训练数据和测试数据，pkuseg可以取得更高的分词准确率。
支持用户自训练模型。支持用户使用全新的标注数据进行训练。
支持词性标注。

View on GitHub

13. SnowNLP

Python library for processing Chinese text

SnowNLP是一个python写的类库，可以方便的处理中文文本内容，是受到了TextBlob的启发而写的，由于现在大部分的自然语言处理库基本都是针对英文的，于是写了一个方便处理中文的类库，并且和TextBlob不同的是，这里没有用NLTK，所有的算法都是自己实现的，并且自带了一些训练好的字典。注意本程序都是处理的unicode编码，所以使用时请自行decode成unicode。

from snownlp import SnowNLP

s = SnowNLP(u'这个东西真心很赞')

s.words         # [u'这个', u'东西', u'真心',
                #  u'很', u'赞']

s.tags          # [(u'这个', u'r'), (u'东西', u'n'),
                #  (u'真心', u'd'), (u'很', u'd'),
                #  (u'赞', u'Vg')]

s.sentiments    # 0.9769663402895832 positive的概率

s.pinyin        # [u'zhe', u'ge', u'dong', u'xi',
                #  u'zhen', u'xin', u'hen', u'zan']

s = SnowNLP(u'「繁體字」「繁體中文」的叫法在臺灣亦很常見。')

s.han           # u'「繁体字」「繁体中文」的叫法
                # 在台湾亦很常见。'

text = u'''
自然语言处理是计算机科学领域与人工智能领域中的一个重要方向。
它研究能实现人与计算机之间用自然语言进行有效通信的各种理论和方法。
自然语言处理是一门融语言学、计算机科学、数学于一体的科学。
因此，这一领域的研究将涉及自然语言，即人们日常使用的语言，
所以它与语言学的研究有着密切的联系，但又有重要的区别。
自然语言处理并不是一般地研究自然语言，
而在于研制能有效地实现自然语言通信的计算机系统，
特别是其中的软件系统。因而它是计算机科学的一部分。
'''

s = SnowNLP(text)

s.keywords(3)	# [u'语言', u'自然', u'计算机']

s.summary(3)	# [u'因而它是计算机科学的一部分',
                #  u'自然语言处理是一门融语言学、计算机科学、
				#	 数学于一体的科学',
				#  u'自然语言处理是计算机科学领域与人工智能
				#	 领域中的一个重要方向']
s.sentences

s = SnowNLP([[u'这篇', u'文章'],
             [u'那篇', u'论文'],
             [u'这个']])
s.tf
s.idf
s.sim([u'文章'])# [0.3756070762985226, 0, 0]

View on GitHub

FAQ About Natural Language Processing in Python

What is natural language processing with example?

Natural language processing (NLP) describes the interaction between human language and computers. It's a technology that many people use daily and has been around for years, but is often taken for granted. A few examples of NLP that people use every day are: Spell check. Autocomplete.

What is NLTK used for in Python?

The Natural Language Toolkit (NLTK) is a platform used for building Python programs that work with human language data for applying in statistical natural language processing (NLP). It contains text processing libraries for tokenization, parsing, classification, stemming, tagging and semantic reasoning.

Which Python library is used for natural language processing?

NLTK is an essential library supports tasks such as classification, stemming, tagging, parsing, semantic reasoning, and tokenization in Python. It's basically your main tool for natural language processing and machine learning.

Why Python is used for NLP?

Natural language processing (NLP) is a field that focuses on making natural human language usable by computer programs. NLTK, or Natural Language Toolkit, is a Python package that you can use for NLP.

Pattern: A Web Mining Module for Python

Pattern

Pattern is a web mining module for Python. It has tools for:

Data Mining: web services (Google, Twitter, Wikipedia), web crawler, HTML DOM parser
Natural Language Processing: part-of-speech taggers, n-gram search, sentiment analysis, WordNet
Machine Learning: vector space model, clustering, classification (KNN, SVM, Perceptron)
Network Analysis: graph centrality and visualization.

It is well documented, thoroughly tested with 350+ unit tests and comes bundled with 50+ examples. The source code is licensed under BSD.

Example workflow

Example

from pattern.web import Twitter
from pattern.en import tag
from pattern.vector import KNN, count

twitter, knn = Twitter(), KNN()

for i in range(1, 3):
    for tweet in twitter.search('#win OR #fail', start=i, count=100):
        s = tweet.text.lower()
        p = '#win' in s and 'WIN' or 'FAIL'
        v = tag(s)
        v = [word for word, pos in v if pos == 'JJ'] # JJ = adjective
        v = count(v) # {'sweet': 1}
        if v:
            knn.train(v, type=p)

print(knn.classify('sweet potato burger'))
print(knn.classify('stupid autocorrect'))

Installation

Pattern supports Python 2.7 and Python 3.6. To install Pattern so that it is available in all your scripts, unzip the download and from the command line do:

cd pattern-3.6
python setup.py install

If you have pip, you can automatically download and install from the PyPI repository:

pip install pattern

If none of the above works, you can make Python aware of the module in three ways:

Put the pattern folder in the same folder as your script.
Put the pattern folder in the standard location for modules so it is available to all scripts:
- c:\python36\Lib\site-packages\ (Windows),
- /Library/Python/3.6/site-packages/ (Mac OS X),
- /usr/lib/python3.6/site-packages/ (Unix).
Add the location of the module to sys.path in your script, before importing it:

MODULE = '/users/tom/desktop/pattern'
import sys; if MODULE not in sys.path: sys.path.append(MODULE)
from pattern.en import parsetree

Documentation

For documentation and examples see the user documentation.

Version

3.6

License

BSD, see LICENSE.txt for further details.

Reference

De Smedt, T., Daelemans, W. (2012). Pattern for Python. Journal of Machine Learning Research, 13, 2031–2035.

Contribute

The source code is hosted on GitHub and contributions or donations are welcomed.

Bundled dependencies

Pattern is bundled with the following data sets, algorithms and Python packages:

Brill tagger, Eric Brill
Brill tagger for Dutch, Jeroen Geertzen
Brill tagger for German, Gerold Schneider & Martin Volk
Brill tagger for Spanish, trained on Wikicorpus (Samuel Reese & Gemma Boleda et al.)
Brill tagger for French, trained on Lefff (Benoît Sagot & Lionel Clément et al.)
Brill tagger for Italian, mined from Wiktionary
English pluralization, Damian Conway
Spanish verb inflection, Fred Jehle
French verb inflection, Bob Salita
Graph JavaScript framework, Aslak Hellesoy & Dave Hoover
LIBSVM, Chih-Chung Chang & Chih-Jen Lin
LIBLINEAR, Rong-En Fan et al.
NetworkX centrality, Aric Hagberg, Dan Schult & Pieter Swart
spelling corrector, Peter Norvig

Acknowledgements

Authors:

Tom De Smedt (tom@organisms.be)
Walter Daelemans (walter.daelemans@ua.ac.be)

Contributors (chronological):

Frederik De Bleser
Jason Wiener
Daniel Friesen
Jeroen Geertzen
Thomas Crombez
Ken Williams
Peteris Erins
Rajesh Nair
F. De Smedt
Radim Řehůřek
Tom Loredo
John DeBovis
Thomas Sileo
Gerold Schneider
Martin Volk
Samuel Joseph
Shubhanshu Mishra
Robert Elwell
Fred Jehle
Antoine Mazières + fabelier.org
Rémi de Zoeten + closealert.nl
Kenneth Koch
Jens Grivolla
Fabio Marfia
Steven Loria
Colin Molter + tevizz.com
Peter Bull
Maurizio Sambati
Dan Fu
Salvatore Di Dio
Vincent Van Asch
Frederik Elwert

Author: clips
Source Code: https://github.com/clips/pattern
License: BSD-3-Clause License

#python

Katrine Gorczany

1651826520

Pattern: A Web Mining Module for Python

Pattern

Pattern is a web mining module for Python. It has tools for:

Data Mining: web services (Google, Twitter, Wikipedia), web crawler, HTML DOM parser
Natural Language Processing: part-of-speech taggers, n-gram search, sentiment analysis, WordNet
Machine Learning: vector space model, clustering, classification (KNN, SVM, Perceptron)
Network Analysis: graph centrality and visualization.

It is well documented, thoroughly tested with 350+ unit tests and comes bundled with 50+ examples. The source code is licensed under BSD.

Example workflow

Example

from pattern.web import Twitter
from pattern.en import tag
from pattern.vector import KNN, count

twitter, knn = Twitter(), KNN()

for i in range(1, 3):
    for tweet in twitter.search('#win OR #fail', start=i, count=100):
        s = tweet.text.lower()
        p = '#win' in s and 'WIN' or 'FAIL'
        v = tag(s)
        v = [word for word, pos in v if pos == 'JJ'] # JJ = adjective
        v = count(v) # {'sweet': 1}
        if v:
            knn.train(v, type=p)

print(knn.classify('sweet potato burger'))
print(knn.classify('stupid autocorrect'))

Installation

Pattern supports Python 2.7 and Python 3.6. To install Pattern so that it is available in all your scripts, unzip the download and from the command line do:

cd pattern-3.6
python setup.py install

If you have pip, you can automatically download and install from the PyPI repository:

pip install pattern

If none of the above works, you can make Python aware of the module in three ways:

Put the pattern folder in the same folder as your script.
Put the pattern folder in the standard location for modules so it is available to all scripts:
- c:\python36\Lib\site-packages\ (Windows),
- /Library/Python/3.6/site-packages/ (Mac OS X),
- /usr/lib/python3.6/site-packages/ (Unix).
Add the location of the module to sys.path in your script, before importing it:

MODULE = '/users/tom/desktop/pattern'
import sys; if MODULE not in sys.path: sys.path.append(MODULE)
from pattern.en import parsetree

Documentation

For documentation and examples see the user documentation.

Version

3.6

License

BSD, see LICENSE.txt for further details.

Reference

De Smedt, T., Daelemans, W. (2012). Pattern for Python. Journal of Machine Learning Research, 13, 2031–2035.

Contribute

The source code is hosted on GitHub and contributions or donations are welcomed.

Bundled dependencies

Pattern is bundled with the following data sets, algorithms and Python packages:

Brill tagger, Eric Brill
Brill tagger for Dutch, Jeroen Geertzen
Brill tagger for German, Gerold Schneider & Martin Volk
Brill tagger for Spanish, trained on Wikicorpus (Samuel Reese & Gemma Boleda et al.)
Brill tagger for French, trained on Lefff (Benoît Sagot & Lionel Clément et al.)
Brill tagger for Italian, mined from Wiktionary
English pluralization, Damian Conway
Spanish verb inflection, Fred Jehle
French verb inflection, Bob Salita
Graph JavaScript framework, Aslak Hellesoy & Dave Hoover
LIBSVM, Chih-Chung Chang & Chih-Jen Lin
LIBLINEAR, Rong-En Fan et al.
NetworkX centrality, Aric Hagberg, Dan Schult & Pieter Swart
spelling corrector, Peter Norvig

Acknowledgements

Authors:

Tom De Smedt (tom@organisms.be)
Walter Daelemans (walter.daelemans@ua.ac.be)

Contributors (chronological):

Frederik De Bleser
Jason Wiener
Daniel Friesen
Jeroen Geertzen
Thomas Crombez
Ken Williams
Peteris Erins
Rajesh Nair
F. De Smedt
Radim Řehůřek
Tom Loredo
John DeBovis
Thomas Sileo
Gerold Schneider
Martin Volk
Samuel Joseph
Shubhanshu Mishra
Robert Elwell
Fred Jehle
Antoine Mazières + fabelier.org
Rémi de Zoeten + closealert.nl
Kenneth Koch
Jens Grivolla
Fabio Marfia
Steven Loria
Colin Molter + tevizz.com
Peter Bull
Maurizio Sambati
Dan Fu
Salvatore Di Dio
Vincent Van Asch
Frederik Elwert

Author: clips
Source Code: https://github.com/clips/pattern
License: BSD-3-Clause License

#python #pattern

The Quick Wins of DevSecOps

Make or Buy

Table of contents:

What is Python?

what is Natural Language Processing in python?

Libraries for Working with Human Languages in Popular Python

Installation

2. Langid.py

3. NLTK

Contributing

Donate

Citing

4. Pattern

Example

5. polyglot

Features

Developer

Quick Tutorial

Language Detection

Tokenization

6. PyText

PyText requires Python 3.6.1 or above.

7. PyTorch-NLP

Installation 🐾

Docs

Get Started

1. Load your Data 🐿

8. spaCy

Features

View on GitHub | View on source

9. Stanza

References

10. funNLP

The Most Powerful NLP-Weapon Arsenal

NLP民工的乐园: 几乎最全的中文NLP资源库

11. jieba

12. pkuseg-python

主要亮点

13. SnowNLP

FAQ About Natural Language Processing in Python

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Pattern

Example

Installation

Documentation

Version

License

Reference

Contribute

Bundled dependencies

Acknowledgements

Example

Installation

Documentation

Version

License

Reference

Contribute

Bundled dependencies

Acknowledgements