quansyn 0.0.6

A package for quantitative syntax analysis

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QuanSyn Documentation (English)

QuanSyn is a Python package designed for quantitative syntax analysis. Currently, it includes three modules: depval, lingnet, and lawfitter.

• Depval provides various methods to calculate dependency-based metrics, including Mean Dependency Distance (MDD), Mean Hierarchy Distance (MHD), Dependency Direction, Dependency Distance Distribution, etc. It also offers a convertor for treebank formats such as conllu, conll, pmt, and mcdt.

• Lingnet can quickly convert a treebank into syntactic dependency networks and co-occurrence networks for language system modeling and language network analysis.

• Lawfitter can quickly fit data to a target mathematical model and detect potential structural patterns in languages.

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Table of Contents

• Terms and Concepts
• Installation
• Quick Start【For beginners】
• Usage【For experts】
• License
• Contact
• Citing

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Terms and Concepts

1. Metric Hierarchy in Depval

(This image is sourced from: Chen, R., Deng, S., & Liu, H. (2021). Syntactic Complexity of Different Text Types: From the Perspective of Dependency Distance Both Linearly and Hierarchically. Journal of Quantitative Linguistics, 29(4), 510–540. https://doi.org/10.1080/09296174.2021.2005960)

Take Dependency Distance (dd) as an example, which can be used at the word, sentence, and discourse levels.
In QuanSyn, the word-level dd refers to the linear distance between a target word and its head word when the target word is a dependent.
The sentence-level mdd refers to the mean dependency distance within a sentence.
The text-level mdd refers to the mean dependency distance in a treebank.
The "level" refers to the subject of the statistical metric, which changes the calculation at each level. The specifics are as follows:

• Word-level: dd, hd, ddir, v

  • dd: Dependency Distance, the linear distance between a target word and its governor. For example, the dd between Bush and will is 1.
  • hd: Hierarchical Distance, the Euclidean distance between a target word and its governor in a tree. For example, the hd of Bush is 1.
  • ddir: Dependency Direction, the direction of the dependency relation.
  • v: Valency, the dynamic generalized valency of a target word, referring to the number of words that link to the target word. For example, Bush only has a dependency with will, so v = 1.

• Sentence-level: mdd, mhd, mhdd, tdl, sl, mv, vk, ttw, tth, hi, hf

  • mdd: Mean Dependency Distance, the average of all dependency distances in a sentence. The MDD of the sentence in the figure is (8+1+1+3+1+1+1+1+1+2+1+1+1+2)/14 = 1.79.
    • $mdd = \frac{1}{sl-1}\sum_{i=1}^{sl-1}dd_i$
    • Liu, H. (2008). Dependency distance as a metric of language comprehension difficulty. Journal of Cognitive Science, 9(2), 159-191.
  • mhd: Mean Hierarchical Distance, the average of all hierarchical distances in a sentence. The MHD of the sentence in the figure is (2+5+4+3+4+5+2+2+3+4+4+5+6+6)/14 = 3.93.
    • $mhd = \frac{1}{sl-1}\sum_{i=1}^{sl-1}hd_i$
    • Jing, Y., & Liu, H. (2015, August). Mean hierarchical distance augmenting mean dependency distance. In Proceedings of the third international conference on dependency linguistics (Depling 2015) (pp. 161-170).
  • mhdd: Mean Hierarchy Dependency Distance, the number of dependency relationships divided by the number of hierarchical levels in the sentence. The mhdd for the sentence in the figure is 14/6 = 2.33.
    • $mhdd = \frac{sl-1}{MAXHL}$, MAXHL为句子中最大层级数。
    • Chen, R., Deng, S., & Liu, H. (2021). Syntactic Complexity of Different Text Types: From the Perspective of Dependency Distance Both Linearly and Hierarchically. Journal of Quantitative Linguistics, 29(4), 510–540.
  • tdl: Total Dependency Length, the sum of all dependency distances in a sentence. The TDL for the sentence in the figure is 25.
    • $tdl = \sum_{i=1}^{sl-1}dd_i$
    • Futrell, R., Mahowald, K., & Gibson, E. (2015). Large-scale evidence of dependency length minimization in 37 languages. Proceedings of the National Academy of Sciences, 112(33), 10336-10341.
  • sl: Sentence Length, the length of the sentence. In this example, the sentence length is 15.
  • mv: Mean Valency, the average valency for all words in a sentence.
    • $mv = \frac{1}{sl}\sum_{i=1}^{sl}v_i$
    • ：
    1. Yan, J., & Liu, H. (2021, May). Quantitative analysis of Chinese and English verb valencies based on probabilistic valency pattern theory. In Workshop on Chinese Lexical Semantics (pp. 152-162). Cham: Springer International Publishing.
    2. Čech, R., Pajas, P., & Mačutek, J. (2010). Full valency. Verb valency without distinguishing complements and adjuncts. Journal of quantitative linguistics, 17(4), 291-302.
  • vk: Variance of Dynamic Valencies, the variance of dynamic generalized valencies for the sentence.
    • $vk = \sum_{i=1}^{sl}v_i^2 - (2-\frac{2}{sl})^2$
    • Lu, Q., Lin, Y., & Liu, H. (2018). Dynamic valency and dependency distance. Quantitative analysis of dependency structures, 145-166.
  • tw: Tree Width, the width of a tree.
  • th: Tree Height, the height of a tree.
    • tw & th: Zhang, H., & Liu, H. (2018). Interrelations among dependency tree widths, heights and sentence lengths. Quantitative Analysis of Dependency Structures, 72, 31-52.
  • hi: Number of head-initial dependencies.
  • hf: Number of head-final dependencies.
    • ddir & hi & hf: Liu, H. (2010). Dependency direction as a means of word-order typology: A method based on dependency treebanks. Lingua, 120(6), 1567-1578.

• Text-level: mdd, mhd, mhdd, mtdl, msl, mv, vk, mtw, mth, hi, hf

  • mdd: Mean Dependency Distance, the average of all dependency distances across all sentences in the text.
  • mhd: Mean Hierarchical Distance, the average of all hierarchy distances across all sentences in the text.
  • mhdd: Mean Hierarchical Dependency Distance, the average of all mhdd values across all sentences in the text.
  • mtdl: Mean Total Dependency Length, the average of all tdl values across all sentences in the text.
  • msl: Mean Sentence Length, the average of all sentence lengths across the text.
  • mv: Mean Valency, the average of all valency values across all sentences in the text.
  • vk: Variance of Dynamic Valencies, the average of all VK values across all sentences in the text.
  • mtw: Mean Tree Width, the average of all tree widths across all sentences in the text.
  • mth: Mean Tree Height, the average of all tree heights across all sentences in the text.
  • hi: Proportion of head-initial dependencies in the text.
  • hf: Proportion of head-final dependencies in the text.

• Distributions: dd, hd, v, sl, tw, th, pos, deprel

  • dd: Dependency Distance Distribution.
  • hd: Hierarchical Distance Distribution.
  • v: Valency Distribution.
  • sl: Sentence Length Distribution.
  • tw: Tree Width Distribution.
  • th: Tree Height Distribution.
  • pos: Part-of-Speech Distribution.
  • deprel: Dependency Relation Distribution.

• Probabilistic Valency Patterns: pos, deprel

  • pvp: Probabilistic Valency Pattern, the probability distribution of dependency relations of a given word or word class.
  • References:
    1. Liu, H., & Feng, Z. (2007). Probabilistic Valency Pattern Theory for Natural Language Processing. Linguistic Science, 6(3), 32-41.
    2. Yan, J., & Liu, H. (2021, May). Quantitative Analysis of Chinese and English Verb Valencies Based on Probabilistic Valency Pattern Theory. In Workshop on Chinese Lexical Semantics (pp. 152-162). Cham: Springer International Publishing.
    3. Čech, R., Pajas, P., & Mačutek, J. (2010). Full Valency: Verb Valency Without Distinguishing Complements and Adjuncts. Journal of Quantitative Linguistics, 17(4), 291-302.

Note: All metric calculations ignore punctuation marks.
Please strictly adhere to the abbreviations and coding specified above.

2. Treebank Formats

Currently, only dependency treebanks are supported. The most common format for dependency treebanks is conllu. For further details on the conllu format, please refer to conllu format. However, other treebank formats are also commonly used, so it is necessary to support additional formats. The currently supported formats, in addition to conllu, include conll, pmt, and mcdt. If you want to use formats other than conllu, please first convert them to the conllu format using depval.convert() or depval.Converter().

• conll: The conllu format with the deps and misc columns removed, with no other differences.

• pmt: This refers to the treebank format used by PKUMultiViewTreebank format. In this format, each sentence spans four lines: the word form, part-of-speech tag, dependency relation type, and the linear index of the governing word.

  this	is	an	example	.
  pron	verb	num	noun	punct
  subj	root	det	obj	punct
  2	0	4	2	2

• mcdt: This refers to the treebank format used by MCDT / Modern Chinese Dependency Treebank.

  sn	id	form	pos	head	gform	gpos	deprel
  1	1	this	pron	2	is	verb	subj
  1	2	is	verb	0	.	punct	root
  1	3	an	num	4	example	noun	det
  1	4	example	noun	2	is	verb	obj
  1	6	.	punct

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Installation

You can install the QuanSyn package via pip:

pip install quansyn

This package depends on conllu.

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Quick Start

Not everyone in the world knows how to program, but when the door is closed, a window must open. We provide some convenient APIs that allow you to save all analysis results locally with just a few lines of code, and you only need to change a few arguments.

Calculating Syntactic Metrics

from quansyn.depval import analyze

# The content in quotes needs to be changed: the *folder* path where the treebank files are stored and the *folder* path where the results should be saved.
# Note: These must be folders!!!
treebanks_path = r'path/to/treebank'
output_path = r'path/to/output'

# Perform the calculation
analyze(treebanks_path, output_path)

# Check the results saved locally, then you can use software like Excel, SPSS, jamovi for statistical analysis.

Converting Treebank Formats

from quansyn.depval import convert

# The content in quotes needs to be changed: the *folder* path where the source files are stored, and the *folder* path where the output files should be saved.
# Note: These must be folders!!!
input_path = r'path/to/treebank'
output_path = r'path/to/output'

# input_format and output_format refer to the source file format and output file format, change them as needed.
# The supported formats are: 'conllu', 'conll', 'pmt', 'mcdt'. Please refer to the previous introduction for details on these four formats.
input_format = 'conll'
output_format = 'conllu'

convert(input_path, output_path, input_format, output_format)

Building a Linguistic Network

from quansyn.lingnet import load_edges

# The content in quotes needs to be changed: the *folder* path where the source files are stored and the *folder* path where the output files should be saved.
# Note: These must be folders!!!
input_path = r'path/to/treebank'
output_path = r'path/to/output'
# Select 'dependency' or 'adjacency' mode, corresponding to the dependency relations or co-occurrence relations in the treebank.
mode = 'dependency'
# Load the treebank and generate the network
load_edges(input_path, output_path, mode)

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Usage

Here, we will explain in detail the structure and usage of the QuanSyn package.

Example 1: Calculating Dependency-Level / Word-Level Metrics

You can calculate dependency-level or word-level metrics:

from quansyn.depval import DepValAnalyzer

# Load the dependency treebank
treebank_path = 'path/to/treebank.conllu'
analyzer = DepValAnalyzer(open(treebank_path, encoding='utf-8'))

# Set the set of metrics (please follow the hierarchy and encoding strictly)
# If no metrics set is provided, all metrics are calculated by default.
metrics = ['dd', 'hd', 'ddir', 'v']
dep_metrics = analyzer.calculate_dep_metrics(metrics=metrics)

# Print the results
dep_metrics

>>> {'dd': [[1,2,3,...],[2,3,5,6,...]], 'hd': [[4,1,1,...],[2,2,5,4,...]], ...}

The return format is a dictionary dict, where the key is the metric name, and the value is a list, with each element corresponding to a sentence and containing the respective metric value for the dependency or word.

Example 2: Calculating Sentence-Level Metrics

Similarly, you can calculate sentence-level metrics with a similar code structure:

from quansyn.depval import DepValAnalyzer

treebank_path = 'path/to/treebank.conllu'
analyzer = DepValAnalyzer(open(treebank_path, encoding='utf-8'))

metrics = ['mdd', 'mhd', 'mhdd', 'tdl', 'sl', 'mv', 'vk', 'tw', 'th', 'hi', 'hf']
sent_metrics = analyzer.calculate_sent_metrics(metrics=metrics)

sent_metrics    
>>> {'mdd': [1,2,3,2,3,5,6,...], 'mhd': [4,1,1,2,2,5,4,...], ...}

The return format is a dictionary dict, where the key is the metric name, and the value is a list, with each element corresponding to the respective metric value for each sentence.

Example 3: Calculating Text-Level Metrics

You can also calculate text-level metrics in a similar manner:

from quansyn.depval import DepValAnalyzer

treebank_path = 'path/to/treebank.conllu'
analyzer = DepValAnalyzer(open(treebank_path, encoding='utf-8'))

metrics = ['mdd', 'mhd', 'mhdd', 'mtdl', 'msl', 'mv', 'vk', 'mtw', 'mth', 'hi', 'hf']
text_metrics = analyzer.calculate_text_metrics(metrics=metrics)

text_metrics
>>> {'mdd': 2.32, 'mhd': 2.21, ...}

The return format is a dictionary dict, where the key is the metric name, and the value is the respective metric value for the entire text.

Example 4: Calculating Metric Distributions

from quansyn.depval import DepValAnalyzer

treebank_path = 'path/to/treebank.conllu'
analyzer = DepValAnalyzer(open(treebank_path, encoding='utf-8'))

metrics = ['dd', 'hd', 'sl', 'v', 'tw', 'th', 'deprel', 'pos']

# normalize=True means to normalize the frequency, the default is False
distributions = analyzer.calculate_distributions(metrics=metrics, normalize=True)

# Print the results
distributions
>>> {'dd': ([1,2,3,4],[0.1,0.2,0.3,0.4]), 'hd', ...}

The return format is a dictionary dict, where the key is the distribution name, and the value is a tuple containing two elements: the first being x and the second being y (frequency or frequency ratio). Both x and y are lists.

Example 5: Calculating Probabilistic Valency Patterns

from quansyn.depval import DepValAnalyzer

# Load the dependency treebank      
treebank_path = 'path/to/treebank.conllu'
analyzer = DepValAnalyzer(open(treebank_path, encoding='utf-8'))

# Calculate the pvp (probabilistic valency patterns) for a specific word class, e.g., nouns.
# The treebank uses the 'sud' annotation system.
# 'input' is the word class; if not specified, pvp is calculated for all word classes.
# 'target' specifies whether to calculate dependency relation probabilities ('deprel') or part-of-speech probabilities ('pos'). Default is 'deprel'.
pvp = analyzer.calculate_pvp(input='NOUN', target='deprel', normalize=True)

pvp
>>> {'act as dependents': {'nmod': 0.1, 'nmod:poss': 0.2, 'nmod:tmod': 0.3, ...},
'act as governors': {...}}

The return format is a dictionary dict, where the key indicates whether the word is acting as a governor or a dependent, and the value is a dictionary containing the corresponding dependency relations and their probabilities.

Example 6: Converting Treebank Formats

from quansyn.depval import Converter

# Load the treebank
treebank_path = 'path/to/treebank.conllu'
converter = Converter(open(treebank_path, encoding='utf-8'))

# Convert the format
# The first argument is the source format, and the second argument is the target format
converted_treebank = converter.style2style('conllu', 'pmt')

# Save the result
# converter.save(treebank, treebank_format, output_path)
converter.save(converted_treebank, 'pmt', 'output_path/to/treebank.txt')

Example 7: Building a Linguistic Network

from quansyn.lingnet import load_edges
import networkx as nx

# Load the treebank
treebank_path = 'path/to/treebank.conllu'
treebank = open(treebank_path, encoding='utf-8')

# Extract dependency relations as edges, or co-occurrence relations by setting mode to 'adjacency'
# edges is a list of tuples, where each tuple is (head, dependent)
edges = conllu2edges(treebank, mode='dependency')

# Build the network using NetworkX
G = nx.Graph(edges)

Example 8: Fitting Parameters

from quansyn.lawfitter import fit

# Load the data
data = ([1,2,3,4,5],[0.1,0.2,0.3,0.4,0.5])

# Fit built-in distributions
fit(data, 'zipf')

# Fit a custom distribution
def custom_dist(x, a):
    return a / (x ** 2)

fit(data, customized_law=custom_dist)

>>> {'paras': [0.123456789], 'r^2': -16.229684950920042}

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License

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

Contact

• GitHub: @YuhuYang
• Email: [e-mail]

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.}
}