Natural Language Processing At A Glance
What is Natural Language Processing?
Natural Language Processing (NLP) encompasses computer science, human language, and artificial intelligence in an attempt to close the gap between human communication and computer understanding.The purpose of NLP is to train machines to understand, decipher, and manipulate the human language. It primarily deals with the interaction between computers and humans using natural language. So, with the help of machine learning, NLP models can derive meaning from incoming written communications in a way that is valuable to businesses. By doing so, machines can automate processes and perform repetitive tasks such as summarization and customer support ticket classification.
NLP has two components: Natural Language Understanding (NLU) and Natural Language Generation (NLG). NLU uses syntactic and semantic analysis of text and speech to determine the meaning of a sentence. NLU refers to how the smart machine rearranges unstructured data in a way that the machine can understand it. NLG, however, refers to the process of producing human language text responses based on data inputs. Thus, NLG is the process of turning structured data into text.
Rise of NLP
NLP was not born overnight. In fact, the first NLP model was created in the 1950s. But, due to recent improvements in NLP technology, NLP models have more capabilities than ever before. According to Y Meadows, developments in NLP technology in the last 18 months have allowed it to help companies in ways that were not possible before. For example, the discovery of ‘Transformer’ language models served as a major breakthrough in the use of NLP technology. One of the most well known Transformers is Google’s creation of Bidirectional Encoder Representations from Transformers (BERT). BERT uses machine learning and NLP to process almost all of Google’s search queries and to better understand user searches in Google Search.
Today, more companies are starting to see the benefits of NLP models and recognize its potential to draw insights from large amounts of data and automate tedious and repetitive tasks. In fact, according to a 2020 NLP Survey Report, 53 percent of business leaders said their NLP budget was at least 10 percent higher in 2020 compared to 2019. Furthermore, the value of the NLP market is expected to increase considerably over the next few years. According to Mordor Intelligence, revenue from the NLP market in 2020 was valued at 10 billion U.S. dollars, while revenue from the NLP market in 2026 is projected to soar to 48 billion U.S. dollars. These increases are evidence that as NLP technology continues to evolve, its value to businesses increases. Therefore, it is crucial for businesses to utilize NLP technology in order to reap the benefits. These benefits include improved customer satisfaction, customer experience, and loyalty.
How Does NLP Work?
Every NLP project goes through a series of phases called a pipeline. In order to better understand how NLP converts sequences of text into numbers that can be quantitatively compared, you must have a basic understanding of each step in the NLP process.
1. Morphological Processing and Lexical Analysis
The first phase in NLP modeling is morphological processing and lexical analysis, which is also known as Tokenization. In this phase, data is scanned and converted into lexemes. A lexeme is a sequence of characters that matches the pattern for a token and is identified by the lexical analyzer as an instance of that token. Thus, the overall purpose of this phase is to separate pieces of text into smaller units called tokens. Tokens can consist of words, sub-words, or even characters.
2. Syntactic Analysis
The second phase involves syntactic analysis, also known as parsing. Syntax refers to the principles that govern sentence structure in any given language. Thus, syntactic analysis focuses on the proper arrangement of words in a sentence in order to derive meaning from it. In other words, the purpose of syntactic analysis is to check grammar, word arrangements, and assess the relationship between natural language and grammatical rules.
For example, a sentence like “He ball the passed” does not make any sense and would be rejected by the syntactic analyzer.
3. Semantic Analysis
The third phase in NLP modeling is semantic analysis. The purpose of semantic analysis is to draw dictionary meaning from the text. In other words, this phase is concerned with the literal meaning of words, phrases, and sentences. The semantic analyzer checks the text for meaningfulness and shows how words are associated with each other. Thus, the semantic analyzer ensures structures have assigned meaning.
For example, the sentence “He has colorless blue eyes” does not make logical sense. Since the eyes were described as colorless, the word blue contradicts the meaning of the sentence. In addition, words can often have multiple meanings. For example, the word run can be used in a variety of ways such as “The relationship has run its course”, “I need to run to the store”, “He will run in the next election” and more. So, when a sentence contains a word like “run”, it has to be analyzed in order to determine which definition of run fits the description of the sentence.
4. Discourse Integration
The next step in the pipeline is discourse integration. Discourse integration focuses on the context of the text. It deals with how a sentence’s meaning can be affected by the sentences that come before and after it. In this phase, texts are analyzed on a larger scale such as paragraphs, documents, and so on.
Sometimes complex sentences can be ambiguous which is why discourse integration focuses on the sentences prior and following the original text. For example, the word “that” in the sentence “She won’t like that” is dependent on the sentence prior to it. Thus, this phase is important because sentences are not always meant to be taken literally like in semantic analysis. It is only after analyzing context, that the real meaning of a sentence can be understood.
5. Pragmatic Analysis
Lastly, pragmatic analysis is the final phase in NLP modeling. Its purpose is to analyze how context contributes to meaning. In other words, this phase focuses on how real world knowledge, social context, and understanding can impact the meaning of a text.
In addition, it helps to discover the intent by applying a set of rules based on different characteristics of dialogues. For example, “Close the fridge” will be interpreted as a request instead of an order when applied against a cooperative rule based dialogue. Consider another example. Let’s say a mother says to her daughter “Check the status of the cake in the oven”. In this example, the mother’s intent is not for the daughter to observe but to take action based on condition. Furthermore, in information retrieval, this phase primarily engages in processing queries and understanding by incorporating the user’s history as well as the context of the query. Context may include time and location.
It is important to note that as NLP technology continues to improve its value to businesses will become paramount. Today, some of the top advantages of adopting NLP technology are the ability to:
- Perform large scale analysis;
- Garner a more objective and accurate analysis;
- Streamline processes and reduce costs;
- Improve customer experience and satisfaction;
- Better understand your customers;
- Empower your employees;
- Gain actionable insights;
Now that you know the basics of how NLP works, you can start to identify ways that implementing an NLP model can improve your business.
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