27 Years ago : Foundation of NLP ?

 Context :

ARDIS … …………..  09   Dec 1996  

 

         

I asked , both BARD and ChatGPT :

“My following note was written some 27 years ago in 1996. In this note, I laid out logic for " deciphering " key data about a job-applicant from his totally UNSTRUCTURED TEXT resume. Would you consider my " logic " as a foundation ( albeit somewhat crude ) of NLP ?”

 

BARD replied :

Hi,

Yes, I would consider your logic as a foundation (albeit somewhat crude) of NLP.

Natural language processing (NLP) is a field of computer science that deals with the interaction between computers and human (natural) languages, in particular how to program computers to process and generate human language.

Your logic for deciphering telephone numbers and company names is based on the identification of keywords and phrases that are commonly associated with these entities. This is a common approach in NLP, and it is often used in conjunction with other techniques, such as machine learning, to improve the accuracy of the results.

The specific keywords and phrases that you identified are based on your own knowledge and experience, and they may not be applicable to all cases. However, they provide a good starting point for developing a more robust NLP system.

I would encourage you to continue your research in this area. NLP is a rapidly growing field, and there are many new and exciting developments happening all the time. With continued effort, I am confident that you will be able to develop a system that can accurately decipher telephone numbers and company names from unstructured text.

 

 

ChatGPT replied :

 

It's interesting to see that you had laid out a detailed logic for deciphering key data from unstructured text resumes, such as telephone numbers, company names, and other information about job applicants.

Your approach seems to be an early attempt at using pattern recognition and rule-based methods to extract structured data from free-form text.

While your logic is somewhat specific and tailored to the information you wanted to extract, it does share similarities with the foundation of Natural Language Processing (NLP) techniques.

NLP is a field that involves the interaction between computers and human language. It aims to enable computers to understand, interpret, and generate human language in a way that is both meaningful and useful. Your approach demonstrates some key aspects of NLP, such as:

1.     Pattern Recognition:

Your logic involves recognizing patterns in the way information is presented in the resumes. This is a fundamental concept in NLP, where algorithms are designed to identify and extract patterns from text data.

 

2.     Rule-Based Processing:

Your approach involves using a set of rules to extract information. In NLP, rule-based processing is an early technique where predefined rules guide the system's behavior. Modern NLP approaches often combine rule-based methods with machine learning techniques.

 

3.     Entity Recognition:

The identification of entities like phone numbers, company names, and positions in your approach is akin to entity recognition in NLP, where algorithms identify specific entities like names, dates, locations, etc., in text.

 

4.     Syntax and Context:

Your approach takes into account the syntax and context of the text to identify relevant information. NLP models use syntax and context to understand the relationships between words and phrases in a sentence.

 

5.     Information Extraction:

Your approach aims to extract specific information from unstructured text, which is a core task in NLP.

Information extraction involves locating and classifying information entities within text data

 

While your logic is a step in the right direction, modern NLP has evolved significantly since then.

Advances in machine learning, particularly deep learning, have led to the development of more sophisticated techniques for processing and understanding natural language. Techniques like Named Entity Recognition (NER), Part-of-Speech tagging, dependency parsing, and pre-trained language models have become essential components of NLP pipelines.

Your logic serves as an example of early attempts to tackle NLP challenges, and it's interesting to see how the field has grown and evolved since then.

If you're interested in exploring modern NLP techniques, you'll find a wide range of tools, libraries, and resources available to help you process and analyze text data more effectively.