用于探索文本中的类型、标记和nlegomena关系的工具。
legomena的Python项目详细描述
法律
用于探索文本中的类型、标记和n-legomena关系的工具。基于Davis 2019[1]研究论文。在
安装
pip install legomena
数据源
这个包可以由任何数据源驱动,但是作者已经测试了两个数据源:Natural Language ToolKit和{a4}。前者是pythonnlp应用程序的黄金标准,但只有区区18本书的gutenberg语料库。后者包含完整的55000多本古腾堡语料库,已经标记和计数。注意:这两个数据集的重叠使得not在它们的确切类型/令牌计数上是一致的,它们的方法是不同的,但是这个包将类型/令牌计数作为原始数据,因此与方法无关。在
^{pr2}$基本用法:
# basic properties
corpus.tokens # list of tokens
corpus.types # list of types
corpus.fdist # word frequency distribution dataframe
corpus.WFD # alias for corpus.fdist
corpus.M # number of tokens
corpus.N # number of types
corpus.k # n-legomena vector
corpus.k[n] # n-legomena count (n=1 -> number of hapaxes)
corpus.hapax # list of hapax legomena, alias for corpus.nlegomena(1)
corpus.dis # list of dis legomena, alias for corpus.nlegomena(2)
corpus.tris # list of tris legomena, alias for corpus.nlegomena(3)
corpus.tetrakis # list of tetrakis legomena, alias for corpus.nlegomena(4)
corpus.pentakis # list of pentakis legomena, alias for corpus.nlegomena(5)
# advanced properties
corpus.options # tuple of optional settings
corpus.resolution # number of samples to take to calculate TTR curve
corpus.dimension # n-legomena vector length to pre-compute (max 6)
corpus.seed # random number seed for sampling TTR data
corpus.TTR # type-token ratio dataframe
# basic functions
corpus.nlegomena(n:int) # list of types occurring exactly n times
corpus.sample(m:int) # samples m tokens from corpus *without replacement*
corpus.sample(x:float) # samples proportion x of corpus *without replacement*
类型令牌模型
在文献中有各种各样的模型预测类型的数量作为标记的函数,其中最著名的是Heap's Law。下面是一些由Corpus
类覆盖的实现。在
# three models
model = HeapsModel() # Heap's Law
model = InfSeriesModel(corpus) # Infinite Series Model [1]
model = LogModel() # Logarithmic Model [1]
# model fitting
m_tokens = corpus.TTR.m_tokens
n_types = corpus.TTR.n_types
model.fit(m_tokens, n_types)
predictions = model.fit_predict(m_tokens, n_types)
# model parameters
model.params
# model predictions
predictions = model.predict(m_tokens)
# log model only
dim = corpus.dimension
predicted_k = model.predict_k(m_tokens, dim)
演示应用程序
查看demo app来探索一些Gutenberg项目书籍中的类型标记和n-legomena计数。在
- 项目
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