NLP application problem $-$ Task A : NER Application $(6$ Marks$)$

Implement an application that displays visualization of Named Entities as shown below: image.png

Implementation Guidelines:

You can use Spacy library for the implementation.

The Application takes user input text file and displays the input text file with named entities as shown in the sample.

The Application should also download a table with named entities identified as ORDINAL $-$ "first", "second", etc. $($make sure your input file has few examples of this entity$).$

Table format: Table name: Entity Table, Columns: entity, words from text. Feel free to trim this table if it is huge

Task B $-$ Document Retrieval System Using TF$-$IDF and Cosine Similarity $(4$ Marks$)$

Use corpus $=$ corpus $=[$ "Quantum computing promises to revolutionize technology.", "Genetic engineering has made significant advancements in recent years.", "Renewable energy is the key to sustainable development.", "The Internet of Things $($IoT$)$ connects everyday devices to the internet.", "Blockchain technology ensures secure and transparent transactions." $]$

Query $=$ "advancements in technology"

Implement the preprocessing steps as required and convert corpus and query to TF$-$IDF vectors.

Compute and display cosine similarity of each corpus document with query.

Retrieve Top $2$ Documents

Task C: Literature Survey $(5$ Marks$)$

Investigate the historical evolution of Information Extraction techniques. How has the field evolved over the years, and what are the key milestones in the development of information extraction systems?

Deliverables:

Task $-$ A

A well$-$documented report with code $($Python and frontend$)$ for the NER application.

Instructions for running the application locally.

A set screenshots that explains the entire flow of the application to be included in the report.

attach the file containing the named entities identified in the input text file, you can choose a suitable file format $($doc$,$pdf$,$txt$...)$

Task $$ B

html or pdf format of Jupyter notebook or other IDE implementing the function with output.

Task $-$ C

Word document with your response on Task C$.$