neosca 0.0.18

Syntactic complexity analyzer of written English language samples

NeoSCA

NeoSCA is a syntactic complexity analyzer of written English language samples. It is a rewrite of Xiaofei Lu's L2 Syntactic Complexity Analyzer, supporting Windows, macOS, and Linux.

Description

The same as L2SCA, NeoSCA takes written English language samples in plain text format as input, counts the frequency of the following 9 structures in the text:

1. words (W)
2. sentences (S)
3. verb phrases (VP)
4. clauses (C)
5. T-units (T)
6. dependent clauses (DC)
7. complex T-units (CT)
8. coordinate phrases (CP)
9. complex nominals (CN)

and computes the following 14 syntactic complexity indices of the text:

1. mean length of sentence (MLS)
2. mean length of T-unit (MLT)
3. mean length of clause (MLC)
4. clauses per sentence (C/S)
5. verb phrases per T-unit (VP/T)
6. clauses per T-unit (C/T)
7. dependent clauses per clause (DC/C)
8. dependent clauses per T-unit (DC/T)
9. T-units per sentence (T/S)
10. complex T-unit ratio (CT/T)
11. coordinate phrases per T-unit (CP/T)
12. coordinate phrases per clause (CP/C)
13. complex nominals per T-unit (CN/T)
14. complex nominals per clause (CP/C)

Comparison

L2SCA	NeoSCA
runs on macOS and Linux	runs on Windows, macOS, and Linux
single and multiple input are handled respectively by two commands	one command for both cases, making your life easier
runs only under its own home directory	runs under any directory
outputs only frequencies of the "9+14" syntactic structures	add options to reserve intermediate results, i.e. Stanford Parser's parsing results, Tregex's querying results

Usage

1. Single input:

nsca sample1.txt 
# output will be saved in result.csv
nsca sample1.txt -o sample1.csv 
# custom output file

2. Multiple input:

nsca sample1.txt sample2.txt
nsca sample*.txt 
# wildcard characters are also supported
nsca sample[1-10].txt

3. Use -p/--reserve-parsed to reserve parsed files of Stanford Parser. Use -m/--reserve-match to reserve match results of Stanford Tregex.

nsca sample1.txt -p -m

Installation

1. Install neosca

pip install neosca

2. Install Java 8 or later

3. Download latest versions of Stanford Parser and Stanford Tregex

4. Set STANFORD_PARSER_HOME and STANFORD_TREGEX_HOME

• Windows:

In the Environment Variables window (press Windows+s, type env, and press Enter):

STANFORD_PARSER_HOME=\path\to\stanford-parser-full-2020-11-17
STANFORD_TREGEX_HOME=\path\to\stanford-tregex-2020-11-17

• Linux/macOS:

export STANFORD_PARSER_HOME=/path/to/stanford-parser-full-2020-11-17
export STANFORD_TREGEX_HOME=/path/to/stanford-tregex-2020-11-17

Under the hood

NeoSCA works as a wrapper of Stanford Parser and Stanford Tregex, so does L2SCA. In case you are unfamiliar with the two dependencies, below are some quick examples. Detailed explanations can be found in the book Computational Methods for Corpus Annotation and Analysis (Lu, 2014).

• Stanford Parser

Assume you have a file named sample.txt containing one sentence:

This is an example.

This command:

java -mx1500m -cp "/path/to/stanford-parser-full-2020-11-17/*" edu.stanford.nlp.parser.lexparser.LexicalizedParser -outputFormat penn edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz /path/to/sample.txt

gives the phrase structure tree:

(ROOT
  (S
    (NP (DT This))
    (VP (VBZ is)
      (NP (DT an) (NN example)))
    (. .)))

The tree can be visualized as follows, with quotation marks eliminated.

In this tree, the starting symbol is the label "ROOT" at the root level of the tree, and the 4 terminal symbols are the 4 words in the sentence, located at the bottom of the branches of the tree. The non-terminal symbols, located between the starting symbol and the terminal symbols, include a number of labels for different clausal, phrasal, and lexical categories. For example, the non-terminal symbol "NP" indicates the phrasal category "Noun Phrase".

When parsing input files, NeoSCA runs the above command and, if you have specified the -rp option, saves phrase structure trees in files with .parsed extension.

• Stanford Tregex

Tregex queries regex-like patterns, called Tregex patterns, against phrase structure trees generated by Stanford Parser.

Assume a sample.parsed has:

(ROOT
  (S
    (NP (DT This))
    (VP (VBZ is)
      (NP (DT an) (NN example)))
    (. .)))

This command:

java -mx100m -cp "/path/to/stanford-tregex-2020-11-17/stanford-tregex.jar" edu.stanford.nlp.trees.tregex.TregexPattern "NP" sample.parsed -o

gives

Pattern string:
NP
Parsed representation:
Root NP
Reading trees from file(s) sample.parsed
(NP (DT This))

(NP (DT an) (NN example))

There were 2 matches in total.

When querying parsed files, NeoSCA runs the above command and remembers how many matches for each of the pre-specified Tregex patterns.

If you have specified the -rm option, it also saves the matches, the two NPs in our case, in files with .matches extension.

Citing

Please use the following citation if you use NeoSCA in your work:

@misc{tan2022neosca,
author = {Tan, Long},
title = {NeoSCA},
howpublished = {\url{https://github.com/tanloong/neosca}},
year = {2022}
}

Also, you need to cite Lu's article describing L2SCA:

@article{lu2010automatic,
title={Automatic analysis of syntactic complexity in second language writing},
author={Lu, Xiaofei},
journal={International journal of corpus linguistics},
volume={15},
number={4},
pages={474--496},
year={2010},
publisher={John Benjamins}
}

License

The same as L2SCA, NeoSCA is licensed under the GNU General Public License, version 2 or later.