10 December 2018
Natural Language Processing is right at the cutting-edge of Artificial Intelligence, and the handling of data is critical to its success. Computers, as you know, thrive on standardised instructions and structured data – and natural language, with all its nuances and subtleties, is anything but that.
It has been developed by humans over thousands of years and involves a complex range of words, sayings and idioms. Data science plays a key role in transforming that unstructured data so that computers can process it. So yes, NLP and data science are absolutely related. Let’s explore NLP in more detail, starting with some common usage.
Practical applications of NLP
NLP enables computers to read text, hear speech, interpret it, determine which parts are important and then act on it. Common applications include:
• Spam filters – NLP is used by Gmail, for example, to scan the text of emails and attempt to understand the meaning – to work out if it’s spam
• Internet searches – when you type a question into Google or ask via Google voice search, NLP is used to understand the meaning of what you’re asking
• Converting speech-to-text and text-to-speech – transforming voice commands into written text and vice versa
• Machine translation – automatically translating text or speech from one language to another
• Content summarisation – pulling structured information out of text-based sources and generating synopses
• Sentiment analysis – identifying subjective opinions and moods in large amounts of text
The overarching aim of NLP is to take raw language as input data and transform it using data science, linguistic science and algorithms. Many different techniques and approaches are involved because of the huge variety of text- and voice-based input data. So how does it work?
Structuring a highly unstructured data source
If the raw data is voice-based, the first step of the process is to transform it into text using deep neural networks such as LSTM (a Long-Short-Term-Memory network). It’s a type of RNN (Recurrent Neural Network) with a gradient-based learning algorithm. Recurrent networks have feedback loops that allow them to model temporal dependencies, and LSTMs enhance that by being able to memorise information. The result is a technology that can map spectrogram feeds into words.
Once in text form, the process continues by breaking down the language into shorter elemental pieces, and trying to identify semantic relationships between them. Basic NLP tasks include Word Tokenization, Parsing, Tagging, Lemmatization and Stemming – which can all be done using Python NLP libraries.
These underlying tasks can then be combined with higher-level tasks, depending on the actual NLP application you’re working on. So for example, Sentiment Analysis is the NLP task for identifying sentiment in a customer review, or by judging mood in a voice analysis. And Text Summarisation is the technique for identifying important points in a body of text, and summarising it without altering the overall meaning.
NLP is a hot topic at the moment, and Data Scientists with Python expertise are right at the heart of it.
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