Welcome to the Google AI blog, where we share the latest research from Google on artificial intelligence and machine learning. Google Search is one of the most widely used products in the world, helping billions of people find information and answers every day. But how does Google Search understand what people are looking for and provide relevant results? In this blog post, we will explain how we use natural language understanding (NLU) techniques to improve Google Search and share some of the recent advances we have made in this field.
NLU is a branch of artificial intelligence that deals with analyzing and generating natural language, such as text or speech. NLU enables machines to understand the meaning and intent of human language, and to respond appropriately. For example, NLU can help a machine answer a question like “Who is the president of France?” by extracting the relevant information from a large corpus of documents or generate a summary of a news article by condensing the main points.
One of the key challenges of NLU is that natural language is very diverse and complex, and often contains ambiguity, slang, idioms, metaphors, and other nuances that are hard for machines to interpret. To overcome this challenge, we use various NLU techniques, such as:
- Syntax analysis: This involves parsing the structure and grammar of a sentence, such as identifying the subject, verb, object, modifiers, etc. Syntax analysis helps us understand how words relate to each other in a sentence and can help us resolve ambiguity. For example, in the sentence “I saw a man with a telescope”, syntax analysis can help us determine whether the speaker saw a man who had a telescope or used a telescope to see a man.
- Semantic analysis: This involves understanding the meaning and context of words and phrases in a sentence, such as identifying entities, relations, attributes, etc. Semantic analysis helps us extract relevant information from text and can help us answer factual questions. For example, in the sentence “Emmanuel Macron is the president of France”, semantic analysis can help us identify that Emmanuel Macron is an entity of type person, president is an attribute of Emmanuel Macron, and France is an entity of type country.