Many may be familiar with the voice command on smartphones for its several shortcuts such as noting appointments or when making calls. We all know that the technology behind this function is the AI that has the ability similar to human to learn and analyze complex data. AI that can precisely process voice command requires Natural Language Processing (NLP). This is one of the mechanisms that helps AI to efficiently and precisely process commands. It helps AI to provide an output that is similar to the natural human language. Today, we will share insights about the Natural Language Processing (NLP).
What is NLP?
NLP is short for Natural Language Processing. It is a branch of the Artificial Intelligence or AI technology that helps computers understand the human language for the objective to communicate and analyze language data.
The story behind NLP development is that computers are actually designed to process data that are numbers or codes which are quite definite. However, this contradicts with the way human communicate, which is using language wherein such language is more complicated when compared to the codes used by computers. Thus, NLP originated to reduce the gap between human and computer communication.
NLP supports reading and listening with the use of other technology. For example, visual recognition is used for reading the text while voice recognition is used to listen along with other technology to provide output for us human. Also, they are used to transfer data to a human in forms of text or voice.
How does NLP learn languages?
The way that NLP learns languages is based on the way AI works (read more about how AI works). The difference is that it is applied to language which is more complex and abstract. Currently, NLP has 6 steps in its language learning process.
- Morphological Level - NLP will dissect words into letters to find consonants, vowels, and final consonants, then, proceed its precision to the next level.
- Lexical Level – After combining the letters, it will find the meaning of the words and prepare to understand the sentence.
- Syntactic Level – It will refer to the understanding of words and standard structure sequence that has been defined by experts or patterns that it has learned.
- Semantic Level – It will understand the meaning of the word in the sentence beyond the structure of the language standard.
- Discourse Level – It will understand the effects of the previous sentence towards the sentence it is reading. Also, it will understand the sequence of the words in the sentence that may provide different meanings.
- Pragmatic Level – It will understand the words and sentence based on the situation or background knowledge that may not be present in the content at that time. This gives the AI an ability similar to the human ability to connect new and old data all the time.
Aside from the understanding at every step, NLP has 3 other channels to learn a language which is a simulation of the language learning process of a human.
- Symbolic – This is the foundation of human’s understanding of language. Here, AI will learn the vocabulary and structure of the language. In this step, developers can input data from experts directly into the AI.
- Statistical – After learning the basics of the language, in the next step, AI will gather more data about language usage from other places. Then, they will analyze the patterns using statistical methods. An example is observing the frequency of word usage and defining commonly-used sentence sequence. Afterward, the information will be synthesized into new data. This will help the AI improve its language skills to match the current trend. Also, they will be able to understand technical terms such as in science, finance, or academic documents.
- Connectionist – AI will integrate the language learning process in the statistical and symbolic level in order to enhance communication and understanding. They will begin with previous knowledge from the symbolic stage and adopt new data from the statistical step.
NLP and its current applications.
Today, NLP in associated with various innovation both directly and indirectly. Familiar innovations that are enriched by NLP are as follow:
- Smarter Chatbot - In the past, Chatbot will respond to programmed commands. However, with NLP, Chatbot can more efficiently respond like a human.
- Enhancing Search Engine – In the past, the search engine will search words which lack the precision of meanings. With NLP, they can better search content to match the meaning, thus, the search is more precise.
- Technology for sentence translation – NLP is designed to understand language. Therefore, NLP is able to take one language and translate to another. At present, the sentence translation on websites is developed by using the NLP technology.
- Tags and automatically categorizing content – NLP is designed to understand the content. Therefore, it is able to help human categorize content according to the standard setting.
- Core mechanism for autonomous vehicles – The communication on the roads still depend on road signs with letters. Therefore, NLP can understand the signs which can make autonomous cars safer.
Computers that understand languages and new potentials in the future.
We have seen the working process and examples of NLP applications. Some may begin to question how computers that learn languages can add potential to our work in the future. Surely, NLP will play a major role in terms of technology usage and data management.
- Disrupt technology usage – Human communicate by using languages, thus, designing computers with AI that understand languages will make computer usage easier. We will be able to explain our needs via voice or text for computers to follow the command. Siri and Google Assistant are examples of the NLP technology. If this technology can flawlessly operate, it will surely disrupt today’s computer usage.
- A significant element in Big Data management – We are aware that digital data is increasing. Part of the data is language that we need to manage. AI with NLP can efficiently perform this task. Moreover, NLP can work with other technologies such as image and sound analysis, thus, it can embrace the entire language media management. Therefore, it will be another significant technology that will assist us in the data age.
Obstacles and challenges for NLP development.
Before NLP can fully affect our lives, it needs to overcome these challenges.
- Languages are constantly changing their meanings – The language we use always undergo change. This involves new vocabulary, word combination, or changing the word sequence in a sentence. Even a human that is not in the field may find it difficult to keep up. NLP will need to learn everything in order to communicate. Thus, the challenge is that it always needs to be updated while maintaining the correctness of the language.
- The language learning process – Teaching AI to learn the language is similar to teaching a newborn child. Language is not only input, but it involves an understanding that needs to be in line with the objectives. Today, although NLP has been developed to better respond to a human when compared with human’s ability, it is still far behind.
This wraps up the story about Natural Language Process. Surprisingly, it is closely associated with innovations relating to human and shall play a significant role in the future. For the Thai language, there is a team of developers named “InThai” who is one of U.REKA finalists. They are developing a Thai language NLP so Thais can explore this technology in the future. Follow us here for more advanced technology updates.
Reference: sas.com, machinelearningmastery.com, and expertsystem.com