Natural Language Processing

We facilitate human-machine interaction

Applied Innovation

The evolution of "intelligent machines" that are able to understand and respond to «human» communication lies in the recognition of a user's intent and sentiments

Natural Language Processing: what it is and what it consists of

Natural Language Processing is the procedure that allows “machines” to recognize text or speech and, in turn, formulate the necessary answers or their reasoning, which will also be provided in the form of text or speech. For decades, man has been working on the construction of machines capable of understanding and responding to text or speech inputs and, therefore, of interacting in the same way as human beings. Well, technology has made great strides in this field, especially in natural language processing. Today, thanks to the evolution of the processing processes of the very complicated human language, machines are able to understand the meaning of words while also recognizing the intent and sentiment of the person speaking or writing.

What does Natural Language Processing (NLP) mean?

The exact definition of the term “Natural Language Processing” is “Natural Language Processing”.

“Natural Language Processing” can be defined as a subject or a branch of Artificial Intelligence (A.I.). Its purpose is to make human language understandable to machines and allow the latter to correctly process the text or speech data sent/received.

Consequently, they can respond and interact in the same way as we human beings, that is via text or speech. From speech recognition to sentiment analysis, over the following paragraphs we will list the various phases and main techniques of Natural Processing Language.

What are the Natural Processing Language phases

In general, there are three Natural Processing Language phases. Each of them is, in turn, made up of different specific activities and techniques used to encode and understand text and speech.

Speech
Recognition

The first phase is that of Speech Recognition, either a spoken “clip” or Optical Character Recognition (OCR) in the event of a text.
In this phase, the machine recognizes what is said to it, segments the speech and then separates it into words, before performing a vocal synthesis and further segmentation of the words (also called "tokenization").

Natural
language generation

The last phase is that of the generation of natural language.
Indeed, once the speech has been codified and understood (word for word), the sentiment has been analyzed and particular attitudes, such as sarcasm, confusion and other emotions related to the text or audio, have been detected, the machine formulates what is required to correctly respond to the inputs received. It will then respond in the same way, i.e. via text or speech.

Understanding
natural language

The second phase is that of understanding what is being said to the machine. This occurs thanks to the various analyzes that the machine performs on the individual words. In the main, it is a matter of morphological and syntactic analyzes that allow the recognition of words in their actual form.
Morphological segmentation and tagging of specific parts of speech will follow. These operations are used to identify the correct meaning of words inserted in a given context and recognize if they can serve as parts of the discourse.
For example, the machine recognizes that the word "pencil" can be a noun ("the pencil is on the table"), can be linked to a verb ("pick up a pencil") and so on.
However, the machine must be able to recognize that the same term cannot be used as an adjective. Indeed, used as an adjective, the term "pencil" will have no meaning. The human language is very complex and it is often very difficult to correctly analyze the meaning of a sentence or single words.
Without a shadow of a doubt, the main problems derive from: homonyms, metaphors, idioms, grammatical exceptions and so on.
Furthermore, the processes of stemming (reduction of inflected words to a basic form) and lemmatization (removal of inflectional endings and return of the word to the basic form) are also part of this phase.
Named Entity Recognition (NER) and Sentiment Analysis (which we will discuss in more detail in the paragraphs below) complete the list of activities included in this phase.

What is Natural Language Processing for?

Natural Language Processing can be used for several purposes, not just to create machines that can interact with us in the best way possible. Indeed, NLP is used to create software capable of facilitating the understanding of texts written in other languages, to create auto-responders or bots, as well as to perform analyzes on textual and speech data flows relating to social networks or messaging apps/software.

Examples of NLP

Automatic Translation

The most striking example of Natural Processing Language used for “innovative” purposes and as a sort of “evolution” of communication between human beings is Google Translate. Big G introduced this tool in 2006 and it is used to instantly translate words or phrases from/to different languages. Over time, this tool has also evolved and nowadays it not only provides a good translation in various languages, but is also able to read the text entered and pronounce it correctly (or almost!). All in over 100 different languages. It’s definitely not a perfect tool, but machine translation is making great progress in terms of accuracy and precision.

Chatbots and Virtual Assistants

NLP is essential for the creation of Chatbots or Virtual Assistants. The most interesting examples are Siri (assistant developed by Apple and integrated into almost all of its devices) and Alexa (developed by Amazon). In these two cases, Natural Processing Language was used to develop software capable of understanding natural language and responding with appropriate actions or useful comments (at the moment, they are elements that have been predefined by the developer, while in the future the virtual assistant could be free to answer as it pleases). Chatbots work in the same way as voice assistants and are able to automatically respond to specific requests from the user (both verbal and written).

Spam Detection

Natural Processing Language is also very useful for detecting Spam. An example is the new algorithm that Facebook is developing, which is capable of detecting vulgar, aggressive, hateful words or language, in such a way as to automatically delete posts containing this type of content. NPL is also used to scan emails for words or language that indicate the presence of spam, phishing attempts or online scams.

The system can be set to any parameter, for example to scan content and highlight parts containing financial terms, grammatically incorrect phrases, misspelled names and so on. Speaking of social media, sentiment analysis is crucial. It is able to correctly determine the language used in posts, responses, reviews and, in the future, it may also be able to understand user attitudes and emotions towards, for example, a product or a post.