What is Natural Language Processing?
Posted: Thu Jan 23, 2025 7:06 am
Natural Language Processing refers to the processing of natural language by computers. It includes the translation of one language into another, but also the recognition of spoken language or automatic responses to certain questions. In this type of task, computers have certain problems understanding, since they focus, above all, on the meaning of each individual word. Thus, it is difficult for translation programs to detect differences between terms caused by factors such as ambiguity and polysemy, among others.
Through the discipline of Natural Language Processing, computers learn to understand the meaning of related sections of text, such as idioms or phrases, and to use them appropriately. This is not only useful for translations or conversational chats, but also when executing voice commands or generating spoken language to communicate with, for example, blind people. Likewise, when summarizing long texts or extracting certain information from large amounts of text, computers need to understand the realtor email database existing linguistic relationships.
How does Natural Language Processing work?
Whether it is machine translation or interaction with a chatbot, the common aspect of all natural language processing methods is that they take into account the hierarchies that define the relationships between words. This is somewhat complicated because many words are polysemous, meaning they have two or more meanings, such as capital (the most important city in a country or heritage), cura (a priest or medical treatment) or sierra (a tool or rock formation). These kinds of difficulties are responsible for making natural language processing one of the most complex fields in computer science. Language is often loaded with double meanings and understanding it requires extensive knowledge of the context in which it is used. Many users are familiar with the uneven communication that occurs with conversational chats, which are increasingly used as substitutes for online chats in customer service. Despite all this, computers are understanding human language better and better. To teach them the language, computational linguists draw on different areas of linguistics:
Morphology deals with the composition of words and their relationships with other words .
Syntax defines how words are put together to form sentences .
Semantics refers to both the meaning of words and groups of words.
Pragmatics echoes the context in which linguistic expressions are carried out .
Phonology deals with the phonetic structure of spoken language and is important for speech recognition.
Grammatical Tagging or Part-Of-Speech Tagging (POST)
The first step in Natural Language Processing is related to morphology and consists of determining the function of each individual word . Most people are familiar with a simplified form of this process from school, where they learn that words can be classified as nouns, verbs or adjectives. However, determining the function of each word is not an easy task for computers, as words can change function depending on the sentence in which they are placed.
Various methods can be used to break down this ambiguity : the oldest are based on extensive text corpora such as the Brown Corpus or the British National Corpus. These are made up of millions of tagged words and from which learning rules for word tagging can be deduced. Using the Brown Corpus, for example, it was possible to create the rule that verbs cease to have a predicate function when they are preceded by an article.
New tagging programs use self-learning algorithms , meaning that they automatically understand the rules of existing text corpora and use them to define other word functions. One of the most popular examples of tagging methods based on such algorithms is Brill Tagger , a method that first defines the most frequent function in the sentence and then, using rules, deduces the rest of the word functions. One rule might be: if the first word in a sentence is a proper noun, the second word is probably a verb. In a sentence such as “Jan has bought a book,” the word “has” might be classified as a verb.
Through the discipline of Natural Language Processing, computers learn to understand the meaning of related sections of text, such as idioms or phrases, and to use them appropriately. This is not only useful for translations or conversational chats, but also when executing voice commands or generating spoken language to communicate with, for example, blind people. Likewise, when summarizing long texts or extracting certain information from large amounts of text, computers need to understand the realtor email database existing linguistic relationships.
How does Natural Language Processing work?
Whether it is machine translation or interaction with a chatbot, the common aspect of all natural language processing methods is that they take into account the hierarchies that define the relationships between words. This is somewhat complicated because many words are polysemous, meaning they have two or more meanings, such as capital (the most important city in a country or heritage), cura (a priest or medical treatment) or sierra (a tool or rock formation). These kinds of difficulties are responsible for making natural language processing one of the most complex fields in computer science. Language is often loaded with double meanings and understanding it requires extensive knowledge of the context in which it is used. Many users are familiar with the uneven communication that occurs with conversational chats, which are increasingly used as substitutes for online chats in customer service. Despite all this, computers are understanding human language better and better. To teach them the language, computational linguists draw on different areas of linguistics:
Morphology deals with the composition of words and their relationships with other words .
Syntax defines how words are put together to form sentences .
Semantics refers to both the meaning of words and groups of words.
Pragmatics echoes the context in which linguistic expressions are carried out .
Phonology deals with the phonetic structure of spoken language and is important for speech recognition.
Grammatical Tagging or Part-Of-Speech Tagging (POST)
The first step in Natural Language Processing is related to morphology and consists of determining the function of each individual word . Most people are familiar with a simplified form of this process from school, where they learn that words can be classified as nouns, verbs or adjectives. However, determining the function of each word is not an easy task for computers, as words can change function depending on the sentence in which they are placed.
Various methods can be used to break down this ambiguity : the oldest are based on extensive text corpora such as the Brown Corpus or the British National Corpus. These are made up of millions of tagged words and from which learning rules for word tagging can be deduced. Using the Brown Corpus, for example, it was possible to create the rule that verbs cease to have a predicate function when they are preceded by an article.
New tagging programs use self-learning algorithms , meaning that they automatically understand the rules of existing text corpora and use them to define other word functions. One of the most popular examples of tagging methods based on such algorithms is Brill Tagger , a method that first defines the most frequent function in the sentence and then, using rules, deduces the rest of the word functions. One rule might be: if the first word in a sentence is a proper noun, the second word is probably a verb. In a sentence such as “Jan has bought a book,” the word “has” might be classified as a verb.