The project aims to develop a natural language processing (NLP) system to automate the process of parsing resumes and filling templates for job postings. The proposed system uses machine learning techniques to analyze the content of resumes and extract relevant information, such as name,email,companies worked at,location, education, work experience, and skills. NLP techniques, such as named entity recognition and keyword extraction, have been used to automate the process of extracting key information from applicant’s resume.
In the context of this project, "resume parsing" refers to the process of extracting structured information from resumes, such as contact details, education history, work experience, skills, and other relevant sections.
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Resume Parsing: Implement a resume parser capable of extracting key information from resumes in text format.
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Information Extraction: Utilize NLP techniques, such as named entity recognition (NER) to extract relevant information from resumes. This may include name, education details, work experience, skills, email, and other sections.
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Data Normalization: Standardize and normalize the extracted information to ensure consistency and accuracy. For example, convert date formats to a common format, normalize skill names, or validate email addresses and phone numbers.
- Programming Language: Python
- Natural Language Processing (NLP) Libraries: NLTK, spaCy, tqdm, json
- Document Processing Libraries: regex
- Clone the project repository from GitHub.
- Install the required dependencies and libraries specified in the project's requirements file.
- Run the code block by block
- Implement advanced language processing techniques, such as entity resolution and relationship extraction, to improve the accuracy of information extraction.
- Building the user interface with additional features like batch processing, multi-file upload, and collaboration capabilities.
- Develop an API to allow seamless integration with other applications or systems.