stark-trees 0.0.1

Parser for dependency trees

STARK: A Tool for Dependency Tree Extraction and Comparison

A bottom-up tool for discovering syntactic patterns in parsed corpora — no predefined queries needed.

STARK is a highly customizable tool designed for extracting different types of syntactic structures (trees) from parsed corpora (treebanks). It quantifies these structures with respect to frequency and provides other useful corpus-linguistic statistics, such as the strength of association between the nodes of a tree or its statistical significance in comparison to another treebank.

STARK is primarily aimed at processing treebanks based on the Universal Dependencies annotation scheme, but it also takes any other dependency treebank in the CONLL-U format as input.

For an online demonstration of the tool (with reduced set of features), please visit https://orodja.cjvt.si/stark/.

Installation and execution

Install Python 3 on your system https://www.python.org/downloads/.

Linux users

Install pip and other libraries required by the program, by running the following commands in the terminal:

sudo apt install python3-pip
cd <PATH TO PROJECT DIRECTORY>
pip3 install -r requirements.txt

Execute extraction by first moving to the project directory and executing the script with:

python3 stark.py 

Windows users

Download pip installation file (https://bootstrap.pypa.io/get-pip.py) and install it by double clicking on it.

Install other libraries necessary for running by going into program directory and double clicking on install.bat. If windows defender is preventing execution of this file you might have to unblock that file by right-clicking on .bat file -> Properties -> General -> Security -> Select Unblock -> Select Apply.

Execute extraction by running run.bat (in case it is blocked repeat the same procedure as for install.bat).

Changing the settings

By default, running the program as described above extracts trees from the sample en_ewt-ud-dev.conllu file (taken from the English EWT UD v2.14 treebank) as defined by the parameter settings in the default config.ini file. To change the settings, you can modify the config.ini file directly or create your own configuration (.ini) file, which is then passed as an argument when running the program in the terminal (example below) or specified in the run.bat file.

python3 stark.py --config_file my-settings.ini

Alternatively, you can change a specific setting by introducing it as a command line argument directly, which overrides the default setting specified in the config.ini configuration file. In the example below, the tool extracts verb-headed trees with lemmas as nodes from a treebank named my-treebank.conllu, while all other options remain the same as in the default config.ini configuration file.

python3 stark.py --input my-treebank.conllu --node_type lemma --head upos=VERB

List of main settings

The types of trees to be extracted and the associated output information can be defined through the main parameters listed below and described in more detail here.

General settings:

• input: location of the input file or directory (parsed corpus in .conllu)
• output: location of the output file (list of trees in .tsv)

Tree specification:

• node_type: node characteristic under investigation (form, lemma, upos, xpos, feats, deprel or none)
• labeled: extraction of labeled or unlabeled trees (values yes or no)
• fixed: differentiating trees by surface word order (values yes or no)

Tree restrictions:

• size: number of nodes in the tree (integer or range, e.g. 2-10)
• head: predefined characteristics of the head node (e.g. upos=NOUN)
• ignored_labels: predefined list of dependency labels to be ignored when retrieving the trees (e.g. punct)
• query: predefined tree structure based on the DepSearch query language (e.g. upos=VERB >obl upos=NOUN).

Statistics:

• association_measures: calculates the strength of association between nodes by MI, MI3, t-test, logDice, Dice and simple-LL scores (values yes or no)
• compare: calculates the keyness of a tree in comparison to another treebank by LL, BIC, log ratio, odds ratio and %DIFF scores (reference treebank in .conllu)

Additional visualization:

• example: prints a random sentence containing the tree
• grew_match: provides links to examples in Grew-match and describes the tree structure using the grew query language

For a detailed explanation of these and many other settings, see the settings documentation here.

Output

STARK produces a tab-separated (.tsv) file with a list of all the trees matching the input criteria sorted by descending frequency, as illustrated by the first few lines of the default sample output below, which show the 5-most frequent trees occurring in the sample en_ewt-ud-dev.conllu treebank.

The description of the tree is given in the first column, while subsequent columns additional information on the absolute and relative frequencies, the surface node order, the number of the nodes in the tree and the head node. For adding other types of information to the output, such as other useful statistics, examples and links to visualized trees, see the list of settings above or the detailed settings documentation here.

Tree	Abs-Freq	Rel-Freq	Order	N	Head
DET <det NOUN	320	12644.6	AB	2	NOUN
ADP <case DET <det NOUN	183	7276.6	ABC	3	NOUN
ADP <case PROPN	175	6958.5	AB	2	PROPN
ADP <case NOUN	163	6481.4	AB	2	NOUN
ADJ <amod NOUN	117	4652.3	AB	2	NOUN

Description of tree structure

The description of the trees given in the first column of the output is based on the dep_search query language (archived here), which is simple to learn and easy to read:

• Dependencies are expressed using < and > operators, which mimic the "arrows" in the dependency graph.
  • A < B means that token A is governed by token B, e.g. rainy < morning
  • A > B means that token A governs token B, e.g. read > newspapers
• Dependency labels are specified right after the dependency operator
  • A <amod B means that token A is the adjectival modifier of token B, e.g. rainy <amod morning
  • A >obj B means that token B is the direct object of token A, e.g. read >obj newspapers
• Priority is marked using parentheses:
  • A > B > C means that A governs both B and C in parallel, e.g. read > newspapers > people for 'people read newspapers'
  • A > (B > C) means that A governs B which, in turn, governs C, e.g. read > (newspapers > interesting) for 'read interesting newspapers'

Acknowledgment

This tool was developed by Luka Krsnik in collaboration with Kaja Dobrovoljc and Marko Robnik Šikonja. Financial and infrastructural support was provided by Slovenian Research and Innovation Agency, CLARIN.SI and CJVT UL as part of the research projects SPOT: A Treebank-Driven Approach to the Study of Spoken Slovenian (Z6-4617) and Language Resources and Technologies for Slovene (P6-0411), as well as through the 2019 and 2024 CLARIN.SI Resource and Service Development grants.