Metadata-Version: 2.1
Name: SNgramExtractor
Version: 0.0.2
Summary: Implementation of syntactic n-grams (sn-gram) extraction
Home-page: https://github.com/StatguyUser/SNgramExtractor
Author: StatguyUser
License: UNKNOWN
Download-URL: https://github.com/StatguyUser/SNgramExtractor.git
Description: What is it?
        ===========
        
        SNgramExtractor module helps extract Syntactic relations (SR tags) as elements of sn-grams. 
        
        We follow the path marked by the arrows in the dependencies and obtain sngrams.[1]
        
        The advantage of syntactic n-grams (SN-grams), i.e., n-grams that are constructed using paths in syntactic trees, is that they are less arbitrary than traditional n-grams. Thus, their number is less than the number of traditional n-grams. Besides, they can be interpreted as linguistic phenomenon, while traditional n-grams have no plausible linguistic interpretation they are merely statistical artifact. [1]
        
        SN-gram has usability across many natural language processing application areas, such as classification tasks in machine learning[2], information extraction[3], query understanding[4], machine translation[5], question answering systems[6]
        
        Input parameters
        ================
        
          - **text** input text as a single sentence.
          - **meta_tag** Resultant bigram and trigram should be concatenated with part of speech tag('pos') or dependency tag('dep') or original SN-gram('original')
          - **trigram_flag** if we need to include trigrams derived from SN-grams as well ('yes') or not ('no'). Default is 'yes'
        
        Output
        ================
        
        Dictionary object with key value pairs for bigram and trigram derived from SN-gram.
        
          - **SNBigram** dictionary key for bigram derived from SN-gram
          - **SNTrigram** dictionary key for trigram derived from SN-gram
        
        How to use is it?
        =================
        
        ```python
        
        from SNgramExtractor import SNgramExtractor
        
        text='Economic news have little effect on financial markets'
        SNgram_obj=SNgramExtractor(text,meta_tag='original',trigram_flag='yes')
        output=SNgram_obj.get_SNgram()    
        print(text)
        print('SNGram bigram:',output['SNBigram'])
        print('SNGram trigram:',output['SNTrigram'])
            
        print('-----------------------------------')
        text='every cloud has a silver lining'
        SNgram_obj=SNgramExtractor(text,meta_tag='original',trigram_flag='yes')
        output=SNgram_obj.get_SNgram()
        print(text)
        print('SNGram bigram:',output['SNBigram'])
        print('SNGram trigram:',output['SNTrigram'])
        
        ```
        
        Where to get it?
        ================
        
        `pip install SNgramExtractor`
        
        Dependencies
        ============
        
         - [spacy](https://spacy.io/)
         - [spacy model en_core_web_sm](https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.0/en_core_web_sm-2.2.0.tar.gz)
        
        References
        ============
        
        1. [Syntactic Dependency-Based N-grams as Classification Features](http://www.icsd.aegean.gr/lecturers/stamatatos/papers/MICAI2012.pdf) by Grigori Sidorov , Francisco Velasquez, Efstathios Stamatatos, Alexander Gelbukh and Liliana Chanona-Hernández
        2. [Syntactic N-grams as Machine Learning Features for Natural Language Processing](http://www.cic.ipn.mx/~sidorov/Synt_n_grams_ESWA_FINAL.pdf) by Grigori Sidorov , Francisco Velasquez, Efstathios Stamatatos, Alexander Gelbukh and Liliana Chanona-Hernández
        3. [Dependency-Based Open Information Extraction](http://www.anthology.aclweb.org/W/W12/W12-0702.pdf) by Pablo Gamallo, Marcos Garcia and Santiago Fernandez-Lanza
        4. [Query Understanding Enhanced By Hierarchical Parsing Structures](https://groups.csail.mit.edu/sls/publications/2013/Liu_ASRU_2013.pdf) by Jingjing Liu, Panupong Pasupat, Yining Wang, Scott Cyphers, and Jim Glass
        5. [Dependency Structure Trees in Syntax Based Machine Translation](http://www.cs.cmu.edu/~vamshi/publications/DependencyMT_report.pdf) by Vamshi Ambati
        6. [Question Answering Passage Retrieval Using Dependency Relations](https://www.comp.nus.edu.sg/~kanmy/papers/f66-cui.pdf) by Hang Cui, Renxu Sun, Keya Li, Min-Yen Kan and Tat-Seng Chua
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
