What is natural language processing? That is the question.
Natural language processing (NLP) aims to mimic human ability to understand natural language and accomplish certain tasks, for example translation, summarization, information extraction, categorization and sentiment analysis.
With NLP, text is typically first broken down text into linguistic units such as words, grammatical group of words like noun phrases, or semantic group of words like concepts or predicates. These units are then processed by algorithms that can be divided into two main categories: statistical and symbolic.
Statistical approaches and more specifically, statistical machine learning, infers patterns through iterative approximation using mathematical formulas. These techniques are now the mainstream approach and are used to perform a wide variety of NLP tasks. The symbolic approach requires that someone handcraft rules modeling knowledge acquired from domain experts. This can be effective for specific tasks, but is less flexible and more time consuming.
What is natural language processing: Conversational vs narrative
So what is natural language processing? Let’s look at it from the perespective of conversational vs narrative formats. State of the art information extraction has mostly been focused on mining information from narrative texts such as newspaper articles or scientific publications. These texts are usually characterized by grammatical sentences with punctuation and correct word spelling.
Conversational texts are far more present on the web. They can be chats, emails, forum discussions or survey responses. Common characteristics of these types of texts are a significant presence of ungrammatical sentences, lack of punctuation, spelling errors, and a far more important role played by the context (what has been said before) to understand and interpret correctly what is being said. Context is crucial, for example, to interpret the answer to open-ended questions such as “Was your car service issue dealt with effectively?”
Differences between narrative texts and conversational texts have a significant impact on the NLP techniques used to mine information. Conversational texts are typically less structured and represent a more challenging problem for NLP systems. Inferring semantics from narrative texts is therefore less challenging than inferring semantics from conversational texts.
As a result, new algorithms specifically designed for conversational texts are needed. That’s where Keatext comes in.
What is natural language processing: Start with Keatext
Keatext is specifically designed for today’s data analyst who want to learn from customer comments and other unstructured text on the web and elsewhere. This is a big job, with lots of data and conversational text with all its flaws and limitations. Making sense quickly and reliably of these valuable sources of customer insight requires a tool with Keatext’s advanced characteristics:
- Intuitive user interface that lets the analyst control the system;
- Automated learning capabilities allow SIFT to improve results based on the analyst’s commands and actions;
- Advanced algorithms specifically designed to handle conversational text;
- Advanced text analytics functions in an easy-to-use application;
- SIFT learns from your texts and your interaction—no need for upfront dictionaries or hard-coded logic;
- Authorized Salesforce Appexchange partner. Learn more about our Salesforce connector; and
- Powerful cloud technology that can scale to process thousands of text comments in minutes. No need for complex IT infrastructure and the inherent delays in installing older on-premise systems.
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