Natural language processing is a challenging field of study, and it still has a long way to go before there will be an mainstream way for it to interact with the intelligent cloud.There are a few reasons why trying to have a computer understand human language and decipher human conversations is a difficult task.
Let’s take a look at some of the challenges that programmers and computer engineers face when trying to institute NLP from a rule-based approach.
Heterogenous and Varied Human Languages
When it comes to human language, there are a variety of different idioms and dialects that are easy for human brains to learn and understand, but computers don’t yet have the level of complex ability required to decipher these things.
A human brain can use its understanding of sarcasm, metaphors, and other insinuations of language, but for a computer which is basically a really advanced calculator, these things are not yet able to be interpreted. If NLP is ever going to really take off, the challenges of addressing this kind of language use and inflection interpretation will need to be overcome.
Questions With Ambiguous Intent
Computers have not yet developed the ability to understand what meanings are implied without expressly being stated. When a question is asked in a certain way, the asker might be looking for information that has not specifically been stated within a question, or a person could be searching for a set of data that he or she isn’t fully aware exists.
NLP is not yet able to determine both what is being asked and what is not specifically being expressed. Computers need to become aware of the implied intent of questions based on what context they are placed within. For example, if someone is inquiring about a customer base, there could be numerous sorts of related statistics and data that needs to be filtered out in order for an answer to be fully relevant to the question.
The amount of processing and different algorithms behind deciphering this type of understanding is incredibly massive, yet this is also easy to forget because human beings are so adept at inferring these types of things.
Emotion or Sentiment Involved
People normally think of one of the strengths of computers is that they have no emotion. When it comes to solving problems and overcoming challenges, emotion is very often a hindrance and, therefore, computers, it seems, are better off without it. However, without a comprehensive understanding of human emotion, computers will never be able to accurately predict questions that a user could want to ask. People tend to speak differently and select different words based upon the emotion out of which they are acting. They use different key phrases and different words as well, depending on what emotion they are currently feeling, and there is extreme predictive value in these emotions.
If a computer with natural language processing can somehow gain the ability to read what emotion a person is feeling while asking a question, it could then be able to use algorithms to build an accurate context around a question, rather than merely giving a blandly uniform answer to a question that had been asked within the context of some type of emotion.
For example, if a customer is unhappy with his or her experience or about to default on financial obligations, or if a manufacturing deal is about to close, natural language processing needs to be able to understand the context of these situations if it is ever going to be useful when they occur.
Human language is complex, multi-faceted, and exponential difficult for computers to understand completely, primarily because human conversation is founded upon emotions which computers do not share. To overcome today’s cross-communicative discrepancies, technology of the future must be able to better interpret the human discourse; identifying minor inflections of tone and tacit components specific to our dialogue, sentiment, context, metaphors, idioms, and overarching heterogeneous nature of speech.
But is there an easy solution? In our rapidly evolving society, many have incorporated statistical approaches to NLP, as an easier solution to leverage rule-based systems. Machine learning (ML), for example, a subfield of AI that employs a range of statistical methods, is an emerging solution associated with NLP. Recently, the intertwining of both applications has proved a successful possibility; accelerating processors and increasing data availability in both tagged and untagged document collections. Ultimately, however, leveraging NLP necessitates its supporting ecosystem to be technologically accurate, innovative, and efficient. While NLP employs eventual promise, it still faces some tough challenges in the upcoming years.
Thankfully, it’s more than likely an inevitability that computers will eventually come up to speed, thanks to continued advances in technology. The turning point will most likely be when computers get to where they can teach themselves and use feedback from humans and big data to continue to improve. This will lead to huge advances in manufacturing and other fields.
The challenges might seem daunting right now, but it’s likely that eventually computers will be able to analyze the intelligent cloud and communicate with human beings as effectively as can the most intelligent people on earth.