Skip to main content

Name entity recognition

Named Entity Recognition (NER) in AI is a subtask of information extraction that focuses on identifying and classifying named entities mentioned in unstructured text into predefined categories such as the names of persons, organizations, locations, dates, and more. NER is essential for various natural language processing (NLP) applications, including question answering, document summarization, and sentiment analysis.

The process of Named Entity Recognition typically involves the following steps:

1. Tokenization
The text is divided into individual words or tokens.

2. Part-of-Speech (POS) Tagging
 Each token is tagged with its part of speech (e.g., noun, verb, etc.), which helps in identifying named entities based on their syntactic context.

3. Named Entity Classification
Using machine learning algorithms, each token is classified into a predefined category (e.g., person, organization, location, etc.) based on features such as the token itself, its context, and its part of speech.

4. Post-processing
 Post-processing steps may be applied to refine the extracted entities and handle cases where the initial classification was incorrect or ambiguous.

NER models are typically trained on annotated datasets that contain text with manually labeled named entities. These models can be rule-based, statistical, or based on deep learning techniques such as recurrent neural networks (RNNs), long short-term memory (LSTM) networks, or transformer models like BERT.

Overall, Named Entity Recognition plays a crucial role in extracting structured information from unstructured text, enabling machines to better understand and process natural language.

Comments

Post a Comment

Popular posts from this blog

Introduction to AI

What is artificial intelligence? Artificial intelligence (AI) is a field of computer science and technology that focuses on creating machines, systems, or software programs capable of performing tasks that typically require human intelligence. These tasks include reasoning, problem solving, learning, perception, understanding natural language, and making decisions. AI systems are designed to simulate or replicate human cognitive functions and adapt to new information and situations. A brief history of artificial intelligence Artificial intelligence has been around for decades. In the 1950s, a computer scientist built Theseus, a remote-controlled mouse that could navigate a maze and remember the path it took.1 AI capabilities grew slowly at first. But advances in computer speed and cloud computing and the availability of large data sets led to rapid advances in the field of artificial intelligence. Now, anyone can access programs like ChatGPT, which is capable of having text-based conve...
Post a Comment
Read more

Bias and fairness in AI

BIAS Bias, in the context of artificial intelligence and data science, refers to the presence of systematic and unfair favoritism or prejudice toward certain outcomes, groups, or individuals in the data or decision-making process. Bias can manifest in various ways, and it can have significant ethical, social, and legal implications. Here are a few key aspects of bias: 1. Data Bias : Data used to train AI models may reflect or amplify existing biases in society. For example, if historical hiring data shows a bias toward one gender or ethnic group, an AI system trained on this data may perpetuate that bias when making hiring recommendations. 2. Algorithmic Bias : Algorithms or models used in AI can introduce bias based on how they process data and make decisions. This bias may arise from the design of the algorithm, the choice of features, or the training process itself. 3. Group Bias : Group bias occurs when AI systems treat different groups of people unfairly. This can include gender b...
Post a Comment
Read more

Policy gradients in AI

Policy gradients are a class of reinforcement learning algorithms used to learn the optimal policy for an agent in a given environment. Unlike value-based methods that estimate the value of different actions or states, policy gradient methods directly learn the policy function that maps states to actions. The key idea behind policy gradients is to adjust the parameters of the policy in the direction that increases the expected return (or reward) from the environment. This is typically done using gradient ascent, where the gradient of the policy's expected return with respect to its parameters is computed and used to update the policy parameters. Policy gradient methods have several advantages, including the ability to learn stochastic policies (policies that select actions probabilistically) and the ability to learn policies directly in high-dimensional or continuous action spaces. However, they can also be more sample inefficient compared to value-based methods, as they typically ...
Post a Comment
Read more