Natural Language Processing Techniques
Today, we’re seeing a renewed interest in natural language processing techniques. As people become more comfortable communicating with machines through natural language interfaces like Siri or intelligent assistants such as Alexa on Amazon Echo, demand for solutions that help computers interact with people in natural language is also growing.
Despite the growing use and appeal of these systems, the process of simulating human comprehension, which should be at the core of natural language processing techniques, is not an easy task to achieve.
Most natural language processing methods use statistical algorithms, eventually based or combined with a classical deep Learning approach. But they are superficial in their analysis; for example, they do not consider words such as articles and prepositions, even though they may play a relevant role in a sentence, and they are not able to distinguish meaning, especially for words that have the same form but different functions (bear: the verb or the noun/animal? Rock: to rock? Or rock, the noun: mineral or music?)
Unlike other systems that process text and language, semantic technology considers every word (articles and prepositions included) and implies a high level of accuracy by starting from the collection and understanding of all the structural and lexical elements. Semantics puts lexicon at the core of its ability to understand language. The challenge is how to bring lexicon to a machine so that it may process language in the same way that humans do.
Understanding language is not easy for machines
According to the Oxford English Dictionary, the 500 most used words in the English language each have an average of 23 different meanings. For people, understanding the meaning of words in context is not a problem at all. But what about for a machine?
Take the following sentences for example:
The driver of the car was injured.
I used the long driver.
The driver was installed in the computer.
Here, we understand the different meanings of driver because we know that its meaning depends on context.
Machines that lack knowledge or experience with linguistic structures are unable to address any linguistic and semantic problems.
Transferring the ability to understand language by leveraging probabilistic algorithms to a machine is not the best way to understand the meaning of words: the results can only be probable. Instead, a system that can simulate basic human knowledge is fundamental for controlling the extremely ambiguous nature of our everyday language and processing it with the highest possible precision.
To disambiguate the meaning of words, cognitive computing tools based on semantics identify all of the structural aspects of the language, consult lexicon databases or semantic networks to reveal all possible meanings, and finally, utilize all the information collected to disambiguate words in context.
Semantics and deep learning boost the implementation of natural language tools
A semantic-based natural language system has an built-in representation of knowledge that can be enriched and improved. Once the rules that organize this knowledge have been implemented, new knowledge can easily be added through automatic learning mechanisms such as deep learning.
Going forward, a mix of human-based knowledge representation and deep learning techniques will be crucial for advancing natural language processing, with innovative implications for even smarter applications such as virtual personal assistants, intuitive interfaces for the Internet of Things or devices like smartphones or those used in our homes and cars.
To learn more, take a look at how some of our customers are using natural language processing!