QuantLing

QuantLing：A python package for quantitative syntax analysis.

Description

QuantLing is a Python package for Quantitative Linguistics. It provides functionality to quantify linguistic structures and explore language patterns.

This package is consisted of three main parts:

• depval.py: some indicators about dependency structures and valency structures.
• lawfitter.py: a small fitter for some laws in QL.
• lingnet.py: a module for complex network construction.

Installation

You can install QuantLing via pip:

pip install quantling

nltk and conllu are required.

pip install nltk conllu

Quick Start

Here's a simple example of how to use QuantLing:

1. depval

from quantling.depval import DepValAnalyzer   
data = open(r'your_treebank.conllu',encoding='utf-8')
dv = DependencyAnalyzer(data) 

# dependency distance distribution
dv.dd_distribution()
# mean dependency distance of specific wordclasses
dv.mdd(pos='NOUN')
# mean dependency distance of specific dependency relations
dv.mdd(dependency='nsubj')
# proportion of dependency distance
dv.pdd()
# tree width and tree depth
dv.tree()
# tree width distirbution and tree depth distribution
dv.tree_distribution()

# mean valency
dv.mean_valency()
# valency distribution
dv.valency_distribution()
# probalistic valency pattern 
dv.pvp()

or:

dv = getDepFeatures(data)
print(dv)

2. lawfitter

from quantling.lawfitter import fit   
#results = fit(data,model,variant)
results = fit([[1,2,3,4,5,6],[3,4,2,6,8,15]],'zipf')
print(resluts)

3. lingnet

from quantling.lingnet import conllu2edge
import networkx as nx   
# use a conllu file to construction a network
data = open(r'your_treebank.conllu',encoding='utf-8')
edges = conllu2edge(data,mode='dependency')
# or to construct a co-occurance network 
#edges = conllu2edge(data,mode='adjacency')
G = nx.Graph()
G.add_edges_from(edges)

# to estimate the degree exponents
degree =[i[1] for i in G.degree()]
degree_exponents = fitPowerLaw(degree)
print(degree_exponents)

Documentation

For more detailed information, please refer to the video (in Chinese).

Features

• Dependency distance distribution
• Mean dependency distance of specific wordclasses
• Mean dependency distance of specific dependency relations
• Proportion of dependency distance
• Tree width and tree depth
• Tree width distribution and tree depth distribution
• Mean valency
• Valency distribution
• Probabilistic valency pattern
• Law fitter
• Complex network construction

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

• GitHub: @YuhuYang
• Email: yangmufy@163.com

Citing

If our project has been helpful to you, please give it a star and cite our articles. We would be very grateful.

@article{Yang_2022,
doi = {10.1209/0295-5075/ac8bf2},
url = {https://dx.doi.org/10.1209/0295-5075/ac8bf2},
year = {2022},
month = {sep},
publisher = {EDP Sciences, IOP Publishing and Società Italiana di Fisica},
volume = {139},
number = {6},
pages = {61002},
author = {Mu Yang and Haitao Liu},
title = {The role of syntax in the formation of scale-free language networks},
journal = {Europhysics Letters},
abstract = {The overall structure of a network is determined by its micro features, which are different in both syntactic and non-syntactic networks. However, the fact that most language networks are small-world and scale-free raises the question: does syntax play a role in forming the scale-free feature? To answer this question, we build syntactic networks and co-occurrence networks to compare the generation mechanisms of nodes, and to investigate whether syntactic and non-syntactic factors have distinct roles. The results show that frequency is the foundation of the scale-free feature, while syntax is beneficial to enhance this feature. This research introduces a microscopic approach, which may shed light on the scale-free feature of language networks.}
}