Natural Language Processing: Providing Structure To The Complexity Of Language.

Natuaral Language Processing

Language is the method of human communication, either spoken or written, consisting of the use of words in a structured and conventional way. When processing text or audio, computers view words as nothing more than data. In order for AI to understand these words and convert them into actions or output something understandable, it relies on natural language processing (NLP). NLP helps computers understand, interpret and manipulate human language. NLP has progressed from a laborious task where rules has to be handwritten by humans to the evolution we all benefit from today, where computers can understand how we communicate better than many of us do.

The Nuances of Language

Human language is not only inherently complex, it is highly nuanced and constantly evolving. We use language in a multitude of ways, both verbally and in writing. When we consider the hundreds of languages and dialects, that exist along with syntax rules, it’s hard to imagine how AI can resolve the ambiguity in human language. That is why Natural Language Processing is so important, it adds a numerical structure to this otherwise unstructured data, allowing for programable speech recognition or text analytics beyond conversing with Alexa or Siri.

Providing Structure to Otherwise Unstructured Data.

NLP is a multifaceted approach when interpreting human language, due to the complexities of text and voice-based data. Deploying a range of approaches including machine learning and algorithms, NLP fragments language into more palatable pieces, in an attempt to understand the relationships between the data and explore how the data works together to create meaning. Taking this raw language input and using linguistic algorithms, enriches the data in such a way that it delivers greater value.

Conditional Interpretation

Teaching a machine to analyze the nuances within language is one thing, teaching a machine to understand how context can affect tone is something else altogether. Semantic analytics help support natural language processing, interpreting human sentences logically and processing the layers of content NLP has fragmented, adding context. Using semantics, machines are able to determine emotional tone and gain an understanding of the attitudes and opinions expressed within textual mentions. While largely one-dimensional, developments within NLP and semantics means the day where machines understand skepticism, sarcasm and excitement, is only getting closer.

Natural Language Understanding: The Next Evolution in NLP

While NLP understands human language, Natural Language Understanding (NLU) is tasked with communicating with untrained individuals and understanding intent, going beyond just understanding words. Programmed with the ability to understand meaning in spite human error, such as mispronunciations or misspelled words, NLU can create chat and speech enabled bots that interact effectively with the public, without the need for supervision. The evolution of NLP towards NLU has major implications for businesses and consumers alike. Algorithms that understand the nuances of human language in its many contexts, will help us to make sense of the volumes of unstructured data that continue to grow unceasingly.

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