Natural Language Processing (NLP)
By: Anat Katzenelnbogen

What is Natural Language Processing?

Understanding human language is a complex subject, since we express ourselves in various ways both orally and in writing. There are hundreds of languages, each with its own syntax, grammar, slang, etc. Furthermore, when writing, we tend to use abbreviating and drop punctuation. Other challenges we might face when analyzing speech include regional accents, mumbling, and terms acquired from other languages.

Natural Language Processing is a branch of Artificial Intelligence that assists computers in understanding, analyzing and interpreting human Language. This branch allows computers to communicate with humans in their own language and assess unstructured data related to language. For example, NLP enables computers to read text, hear speech, interpret it, assess emotion and define which parts of the text is more important.

 

What types of data can be analyzed and reviewed?

Structured data- data organized in orderly format. For example, data in SQL tables with columns and rows. They feature a relation index and can be easily charted.

Semi-structured data- data which isn't stored in a structured database yet features some organizational properties that simplify the analysis. A certain processing can arrange them in a certain context. For example, XML data.

Unstructured data- data which is not organized in a predefined manner or is not stored in a predefined database, from texts excerpted from medical records to texts published on social media channels.

Nowadays, computers analyze more language-based data than humans, considering the vast amount of unstructured data generated on a daily basis.

Automation is critical for text and speech data analysis.

 

What is NLP used for?

NLP technology plays a great role in Artificial intelligence applications, some of which implement machine learning combined with NLP for risk analysis, customer behavior review, Chatbots and fraud detection.

One of the most common uses of NLP is aimed at the various search apps, both audio and textual (for further details, click here). The search engine uses NLP to analyze the query and retrieve the most relevant answers. Users used to have to search by keywords. Nowadays, you can simply state complex sentences that then serve as the basis for your search. When searching by voice, the search questions are said, then recorded and processed by an interface which converts speech into text while filtering background noises, then analyzed for the different phonemes, adapted to words then finally converted to a sentence which becomes query. This query is sent to the system, which in turn sends the appropriate answer. In an age in which people can speak to Siri and Alexa as freely as they speak to one another, many people can benefit greatly from these apps.

These abilities can be used for other applications, too. For example, Facebook uses a tool titled DeepText to analyze posts published on the social network. DeepText is a text analysis engine that can detect positive and negative emotions expressed in written posts, automatically tag and retrieve themes from texts. The objective is to reach insights/conclusions from a text and in some cases even convert it to actual action. An example of this implementation is Chatbots response to a spoken question).

 

Google has its own NLP tools, including:

 

API Natural Language Cloud- this tool enables exposing the structure and meaning of the text. It allows retrieving information on people, locations and events to understand the information transferred through social media and client conversations. This tool allows classifying files by 700 predefined categories.

AutoML Natural Language- a machine learning NLP-oriented tool which enables creating personally adapted models for organizations to categorize, retrieve, and detect different elements in a text.

Here’s how it works:

  1. The user loads the various data into the computer (the more varied the examples, the more accurate the process)
  2. Runs the module adapted to their needs
  3. Assesses the received results. In case the received insights are insufficiently precise, update the module, then review the results again.

Source: https://cloud.google.com/natural-language/#how-automl-natural-language-works 

 

NLP and Knowledge Management

Both AI tools (such as Natural Language Processing) and Knowledge Management tools center around treating knowledge and information as a central component. Both are essentially intertwined. Without reliable knowledge and initial charting of the required templates, the machines would not be able to elaborate, create or implement knowledge optimally. Furthermore, the field of KM has generated the strategic processes that allow organizations to make the knowledge created with their automatic tools optimally accessible to all relevant parties.

 

References
https://machinelearning.co.il/172/deeptext/ 
https://www.sas.com/en_us/insights/analytics/what-is-natural-language-processing-nlp.html 
https://cloud.google.com/natural-language/#how-automl-natural-language-works 
https://curatti.com/artificial-intelligence-knowledge-management/ 

 

 

 
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