LFExtractor 1.0.1
A corpus-linguistic tool to extract and search for linguistic features
License: MIT License
Requires Python: >=3.6
Keywords: Computational linguistics,Corpus-linguistic tool,NLP,Natural Language Processing

# ling_feature_extractor
## Description
- A corpus-linguistic tool to extract and search for linguistic features in a text or a corpus.
- There are 95 built-in linguistic features in the main version versus 98 features in the Thesis_Project version. Deleted features are words per utterance, number of utterances, and number of overlaps, which are not deemed as generally accessible in a normal corpus.
- Over 2/3 of these features come from Biber et al.(2006) with 42 features also present in Biber(1988). These features are generally known as part of the Multi-Dimensional (MD) analysis framework.
- The program is mainly tested on two online accessible corpora, namely [British Academic Spoken Corpus](http://www.reading.ac.uk/AcaDepts/ll/base_corpus/) and [Michigan Corpus of Academic Englush](https://quod.lib.umich.edu/cgi/c/corpus/corpus?page=home;c=micase;cc=micase), but due to copyright concerns, here it is tested on the [test_sample](https://github.com/jaaack-wang/ling_feature_extractor/tree/main/test_sample). 

## Prerequisites

- `Computer Langauges`: 
   - Python 3.6+: check with cmd: `python --version` or `python3 --version` ([Download Page](https://www.python.org/downloads/)); 
   - Java 1.8+: check with cmd: 'java --version' ([Download Page](https://www.java.com/en/download/)). 
- `Python packages`

| Package | Description | Pip download | 
| :---: | :---: | :---: |
| [stanfordcorenlp](https://github.com/Lynten/stanford-corenlp) | A Python wrapper for StanforeCoreNLP | `pip/pip3 install stanfordcorenlp` |
| [pandas](https://pandas.pydata.org) | Used for storing extracted feature frequencies  | `pip/pip3 install pandas` |

Besides, built-in packages are heavily employed in the program, especially the built-in `re` package for Regular Expression.

## Installation
- Directly download from this page and cd to the project folder.
- By pip: `pip/pip3 install LFExtractor`

## Usage
### path to StanfordCoreNLP
**Please specify _the directory to StanfordCoreNLP_ in the text_processor.py under LFE folder when first using the program.**
- [X] `nlp = StanfordCoreNLP("/path/to/StanfordCoreNLP/")` 

Example: nlp = StanfordCoreNLP("/Users/wzx/p_package/stanford-corenlp-4.1.0")

### Dealing with a corpus of files
```python 
from LFE.extractor import CorpusLFE

lfe = CorpusLFE('/directory/to/the/corpus/under/analysis/')
# get frequency data and tagged corpus and extracted features by default
lfe.corpus_feature_fre_extraction() lfe.corpus_feature_fre_extraction()    # lfe.corpus_feature_fre_extraction(normalized_rate=100, save_tagged_corpus=True, save_extracted_features=True, left=0, right=0). 
# change the normalized_rate, trun off tagged text and leave extracted text with specified context to display
lfe.corpus_feature_fre_extraction(1000, False, True, 2, 3) # extract frequency data only, and the data are normalized at 1000 words.  

# get frequency data only
lfe.corpus_feature_fre_extraction(save_tagged_corpus=False, save_extracted_features=False)
# get tagged corpus only
lfe.save_tagged_corpus()
# get extracted feature only
lfe.save_corpus_extracted_features()   # lfe.save_corpus_extracted_features(left=0, right=0)
# set how many words to display besides the target pattern
lfe.save_corpus_extracted_features(2, 3)

# extract and save specific linguistic feature by feature name
# to see the built-in features' names, use `show_feature_names()`
from LFE.extractor import *
print(show_feature_names())   # Six letter words and longer, Contraction, Agentless passive, By passive...
# specify which feature to extract and save
lfe.save_corpus_one_extracted_feature_by_name('Six letter words and longer')

# extract and save specific linguistic feature by feature regex, for example, 'you know' 
lfe.save_corpus_one_extracted_feature_by_regex(r'you_\S+ know_\S+', 2, 2, feature_name='You Know')  # Extract phrase 'you know' along with 2 words spanning around. Also remember the '_\S+' at the end of each word since the corpus will be automatically POS tagged.
# for more complex structure, the features_set.py can be ultilized, for example, to extract "article + adj + noun" structure
from LFE import features_set as fs
ART = fs.ART
ADJ = fs.ADJ
NOUN = fs.NOUN
lfe.save_corpus_one_extracted_feature_by_regex(rf'{ART} {ADJ} {NOUN}', 2, 2, 'Noun phrase')
# result example (use test_sample): away_RB by_IN	【 the_DT whole_JJ thing_NN 】	In_IN fact_NN 
```

### Dealing with a text
```python
from LFE import extractor as ex

# check the functionalities contained in ex by dir(ex)
# show built-in feature names
print(ex.show_feature_names())   # Six letter words and longer, Contraction, Agentless passive, By passive...
# get built-in features' regex by its name
print(ex.get_feature_regex_by_name('Contraction'))  # (n't| '\S\S?)_[^P]\S+
# get built-in features' names by regex
print(ex.get_feature_name_by_regex(r"(n't| '\S\S?)_[^P]\S+"))  # Contraction

# text processing
# tagged file
ex.save_single_tagged_text('/path/to/the/file')
# cleaned file
ex.save_single_cleaned_text('/path/to/the/file')

# display extracted feature by name
res = ex.display_extracted_feature_by_name('/path/to/the/file', 'Contraction', left=0, right=0)
print(res)  #  's_VBZ, n't_NEG, 've_VBP...
# save the result
ex.save_extracted_feature_by_name('/path/to/the/file', 'Contraction', left=0, right=0)

# display extracted feature by regex, for example, noun phrase
from LFE import features_set as fs

ART = fs.ART
ADJ = fs.ADJ
NOUN = fs.NOUN
res = ex.display_extracted_feature_by_regex(rf'{ART} {ADJ} {NOUN}', 2, 2, 'Noun phrase')
print(res)  # One_CD is_VBZ	【 the_DT extraordinary_JJ evidence_NN 】	of_IN human_JJ
# save the result
ex.save_extracted_feature_by_regex(rf'{ART} {ADJ} {NOUN}', 2, 2, 'Noun phrase')

# get the frequency data of all the linguistic features for a file 
res = ex.get_single_file_feature_fre(file_path, normalized_rate=100, save_tagged_file=True, save_extracted_features=True, left=0, right=0)
print(res)
```

### Dealing with a part of a corpus
```python
from LFE.extractor import *

lfe = CorpusLFE('/directory/to/the/corpus/under/analysis/')
# get_filepath_list and select the files you want to examine and construct a list
fp_list = lfe.get_filepath_list()   
# loop through the list and use the functionalities mentioned above to get the results you want
```