The concept of artificial intelligence is becoming more common, not only in the technological field but in daily life. It basically refers to the ability to simulate processes of human intelligence developed by computer software, acquiring the abilities to reason, learn and self-correct. All this that may seem futuristic we can see represented in our daily technology, as is the case of chatbots with which we can communicate so easily in our smartphones.
On the other hand, we find natural language that is nothing more than the language that we humans use, it can be spoken, written or gestural. This language, which may sound the most basic, requires many neural connections and brain and body processes to understand others and express ourselves. Natural language is complex and spontaneous. Unlike this natural language, we have the so-called formal language that is used by sciences such as mathematics or computing, and that is based on the union of previously specified symbols.
What is the relationship of the NLP with Artificial Intelligence?
By now you will have guessed, the Natural Language Processing is one of the branches of Artificial Intelligence. Artificial Intelligence relies on this processing to be able to give effective responses and hold conversations getting closer and closer to human language.
Are there flaws in Natural Language Processing?
These technologies are very recent and they usually need improvement, especially in the ability to reproduce human-like natural language. The lack of cadence of the conversations or the errors when understanding certain forms to speak is small failures that progressively are improving. Remember that Artificial Intelligence has the ability to learn so that they are rectifying their errors.
Little by little, we are surprised with these advances, who was going to say 20 years ago that we would now be talking to our computer devices? And even more important, how far will we go?
NLP, or Natural Language Processing, focuses on the way artificial intelligence has to understand and imitate the natural language of human beings.
Achieving this has been a challenge for those who have carried it out but have finally found several ways to develop the NLP:
- Probabilistic model: to carry out this model, data is first collected and the frequency at which certain linguistic units appear in a context is calculated, so that the next time that context occurs, it will be possible to predict which unit would be adequate.
- Logical model: the reverse of the previous model, in this case, the ones that define the patterns previously are the linguists, in such a way that by combining them with the stored dictionary information, the response patterns will be configured.
Processing natural language requires different techniques, following the models described above and applying different algorithms in such a way that the NLP allows artificial bits of intelligence to perform certain tasks:
- Language detection: one of the most basic tasks that artificial intelligence has when processing natural language.
- Relationship identification: to know what you have to answer next.
- Categorization of the content: in such a way that they summarize all the information based on the natural language facilitating its search and indexing.
- Syntactic analysis: to respond correctly.
- Lemmatization: which consists of the automatic elimination of prefixes and suffixes to keep the root word, which facilitates word searches and helps a faster response.
- Contextualization: structuring the information based on the context that has been previously defined.
- Sentiment analysis: identifies the mood of the interlocutor based on languages that have been used
- Documentation Summary: capable of automatically summarizing large amounts of text.
- Translation: translating into several languages.
- From voice to text and vice versa: transforming spoken language into written text and on the contrary almost immediately.
In general, these tasks fragment the messages into elementary pieces to explore how these pieces together have new meanings, using both linguistics and complicated computer algorithms to enrich the language learned and add value to communications.
By merging AI and NLP, we are moving towards very realistic conversations and interactions, now you may well be engaging with chatbots and voice bots without even knowing you are speaking with a nonhuman. This technological field is young but advancing rapidly. NLP & AI is and will continue to be even more a part of our daily lives in years to come.