In order to impart the barriers in terms of language spoken across the globe, Natural Language Processing (NLP) has become a potential arm of Artificial Intelligence. Tech giants like Facebook, Google and others are competing to hone the possible virtues of the technology.
Breaking Down NLP
Natural Language Processing is a branch of Artificial Intelligence that provides the automatic manipulation of human language or human-generated text.
Marco Varone, chief technology officer for Expert Systems, an NLP company said – “Natural language processing helps computers read and respond by simulating the human ability to understand the everyday language that people use to communicate. Without natural language processing, artificial intelligence only can understand the meaning of language and answer simple questions, but it is not able to understand the meaning of words in context.”
According to 2017 Deloitte survey, a major count of companies who are dealing in disruptive technologies said that they have adopted NLP for analytics podium.
Semantics; The Crux of NLP
Semantics being the heart of Natural Language Processing breaks down the human speech into a simpler form and then fetches for the meaningful essence of the potential word frame.
MIT Technology Review reported in a study on machine learning-driven analytics – “Instead of users telling the software what they are looking to find, autonomous capabilities serve up insights based on identified correlations and patterns. The result will be simplified and more personalized insights that anticipate requirements and make recommendations using predictive analytics.”
Usage of Semantics+NLP
• The analysis derived from semantic breakdown can be further used in the decision-making process and performing tasks or form conclusions based on limited data.
• Organizations are exploring the possible capabilities of NLP in context to business applications (tailored customer service and market intelligence) at a high
• California based tech company Facebook has scrutinized the potential applications of NLP and semantics in the commercial
• Facebook has experimented with the potential applications in different sectors for natural language processing.
Casey Newton, a writer for Verge asserted – “It’s possible to imagine a world where M was more successful at commerce and was able to take a cut of revenue, defraying some of the costs of maintaining an around-the-clock service. But bot-based commerce has been slow to take off, as most people continue to prefer native apps and the web over sending text messages.” Casey Newton had given access to an earlier version of M that offered various shopping suggestions among its services.
Adoption of NLP in Med-World and Expected Hurdles
The medical sector is also conceiving the boons of NLP by helping decoding doctor’s notes and using the outcome data for essential research information on various diseases including breast and other cancers.
Additionally, NLP in collaboration with machine learning, big data and IoT services contribute to below activities in the healthcare sector.
• Upgrading provider interaction with patients with the EHR (Electronic Health Record)
• Develop patient health literacy
• Subsidizing to a higher quality of care
• Diagnose patients in need of improved care coordination.
Tejal Patel, quoted in an article for American Cancer Society “A key challenge to mining electronic health records for mammography research is the preponderance of unstructured narrative text, which strikingly limits usable output. In the era of EHR systems, big data, and machine learning algorithms, natural language processing has emerged as a possible solution with which to overcome the limitations of manual data abstraction.”
Despite the far-fetched approach and mastery of Natural Language Processing, it still lacks the quality of natural language understanding which is similar to the ability of humans to integrate and adjust to new information and details. This symbolizes the next big challenge for NLP algorithms.
Macro Lagi, an MIT researcher in ML said – “Most of the methods employed in NLP are statistical in nature, and statistics can only go so far without context or semantics. The algorithms behind the applications described above simulate human understanding and can do that at scale, but they are still brittle in that they can’t simulate a behavior they haven’t seen before.”