Information Extraction in Bengali || সার্চ থেকে বিজ্ঞান || বাংলা

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Information Extraction (IE): Concepts, Methodology, and Neural Network Approach

This document provides a detailed briefing on Information Extraction (IE), contrasting it with Information Retrieval (IR), and outlining its application using Neural Networks. It reviews the core concepts, methodology, and key characteristics as presented in the provided sources.

1. Definition and Goal of Information Extraction (IE)

Information Extraction (IE) is defined as “any method for filtering information from large volumes of text.” Its primary goal is “to transform text into a structured format and deduce information within a document into a tabular structure.”

In essence, IE converts unstructured or semi-structured data into structured knowledge. This process involves:

Extracting pre-specified features from documents.

Representing them in structured or tabular form.

Using Natural Language Processing (NLP) and semantic analysis to derive meaning.

IE is fact-focused: instead of returning whole documents, it extracts the key knowledge units and relationships hidden in them.

2. Distinction from Information Retrieval (IR)
Aspect Information Retrieval (IR) Information Extraction (IE)
Goal Finds documents relevant to a query (“document retrieval”) Extracts facts/features from text (“feature retrieval”)
Methodology Keyword/document matching, classification-style approach NLP + semantic analysis, understanding relationships
Depth Shallow (does not “understand” text) Deep (semantic roles, meaning, relations)
Output Ranked list of documents Structured facts in tabular/network form
3. Information Extraction using Neural Networks: Methodology Overview

The process of IE via Neural Networks follows a multi-step pipeline:

Input Document → Raw text data provided.

Sentence Analysis → Parse sentences grammatically.

Assign Deep Case → Identify semantic roles (Agent, Action, Place, Date, etc.).

Network Creation → Represent knowledge as a connected network of entities and relations.

Question Analysis → Parse a user’s query to detect intent and keywords.

Search in Neural Network → Query knowledge graph.

Retrieve Knowledge Units → Match relevant nodes/edges in the network.

Output Answer → Present a concise, structured fact.

4. Detailed Example Walkthrough (Albert Einstein)

Let us consider the example sentence:

“Albert Einstein was awarded the Nobel Prize in Physics in 1921.”

Step 1: Input Text

“Albert Einstein was awarded the Nobel Prize in Physics in 1921.”

Step 2: Tokenisation and IDs

ID1: Albert Einstein

ID2: Awarded

ID3: Nobel Prize in Physics

ID4: 1921

Step 3: Extract Knowledge Units

K1: “Albert Einstein was awarded the Nobel Prize in Physics.”

K2: “Albert Einstein was awarded in 1921.”

Step 4: Assign Word Types

Albert Einstein → Who

Awarded → What

Nobel Prize in Physics → What/Recognition

1921 → When

Step 5: Assign Deep Cases (Semantic Roles)

Albert Einstein → Agent

Awarded → Action

Nobel Prize in Physics → Object/Theme

1921 → Date

Step 6: Define Relationships

ID1 (Einstein) links to ID2 (Awarded).

ID2 connects to ID3 (Prize) and ID4 (Date).

Step 7: Build Neural Network

Nodes: {Einstein, Awarded, Nobel Prize in Physics, 1921}

Edges: Agent–Action–Object–Date relations.

Step 8: Process Query Example

Q1: “What prize did Albert Einstein win?”

Q2: “When was Einstein awarded the Nobel Prize?”

Step 9: Search the Network

Q1 maps to K1.

Q2 maps to K2.

Step 10: Output Answer

A1: “Albert Einstein won the Nobel Prize in Physics.”

A2: “Einstein was awarded in 1921.”

5. Key Concepts and Terms

Unstructured Data → Text without fixed schema (e.g., articles, books).

Structured Data → Data in predefined fields (e.g., tables, databases).

Semi-structured Data → Text with markers but flexible schema (e.g., XML, JSON).

NLP (Natural Language Processing) → AI methods to analyse and interpret human language.

Semantic Analysis → Understanding the meaning and relationships in text.

Deep Case / Semantic Role Labelling → Assigning roles like Agent, Action, Object, Time, Place.

Knowledge Unit → A discrete extracted fact, stored in the network.

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