Word2Vec Model Training and Elasticsearch Indexing

This code demonstrates how to create an Elasticsearch index, train a Word2Vec model on text documents, and search for documents using an English analyzer. Follow the steps below to use this code:

Pre-requirements:

Make sure you have the following Python packages installed:

->elasticsearch

->gensim

Ensure that Elasticsearch is running locally on http://localhost:9200/ or adjust the URL accordingly in the code.

Overview

This code presents a step-by-step guide for setting up Elasticsearch indexing and training a Word2Vec model to facilitate document retrieval. It encompasses the following key tasks:

1. Elasticsearch Index Creation: The code starts by creating an Elasticsearch index configured with an English text analysis pipeline. This pipeline involves tokenization, stop word removal, stemming, and other linguistic processing steps to enhance text search accuracy.

2. Document Indexing: After defining the index, the code indexes a collection of text documents. It extracts content from these documents, assigns unique identifiers, and stores them in the Elasticsearch index. This step prepares the corpus for efficient retrieval.

3. Word2Vec Model Training: Next, the code trains a Word2Vec model using the indexed documents. The model captures semantic relationships between words, which can later be used to expand search queries and find documents with similar content.

4. Search Implementation: The code performs document searches using a combination of Elasticsearch and the trained Word2Vec model. It analyzes user-provided search queries, expands them by finding similar words in the model's vocabulary, and then uses the Elasticsearch index to retrieve relevant documents based on the expanded queries.

Workflow

1. Index Creation: The code sets up an Elasticsearch index with the desired text analysis settings and mappings. This index defines how text will be processed and stored for efficient retrieval.

2. Document Indexing: Text documents are read from a specified file, divided into separate documents, and indexed in Elasticsearch. Each document is associated with a unique identifier and undergoes linguistic processing.

3. Word2Vec Model Training: The code trains a Word2Vec model on the indexed documents. This model learns to represent words as vectors in a continuous space, enabling semantic similarity calculations.

4. Search Expansion: When a user provides a search query, the code analyzes and expands the query by finding similar words in the Word2Vec model. This expansion broadens the search to include related terms.

5. Elasticsearch Query: The expanded query is then used to perform a search in the Elasticsearch index. The code retrieves documents that match the query based on the indexed content.5

6. Results Output: Search results are saved to an output file, including document IDs, scores, and other relevant information. These results can be used for document retrieval or further analysis.