Natural language processing (NLP) is one of the most important tasks in the current industry that uses machine learning concepts. NLP deals with anything related to using machines to process and understand human text/speech, which we call Natural Languages.
Tasks such as translating between languages, speech recognition, text analysis, and automatic text generation all fall under the scope of NLP. Let’s define the two terms Natural Language and Natural Language Processing in a more formal way.
- Natural Language: A language that has developed naturally in humans.
- Natural Language Processing: The ability of a computer program to understand human languages as it is spoken. The ultimate objective of NLP is to read, decipher, understand, and make sense of the human languages in a valuable way.
Natural language deals with two categories of data: spoken and written data. Written data, like text, is more prevalent in NLP tasks, but raw text data is usually unusable in NLP applications. An engineer must first convert the raw text data into usable machine data. That machine data is then fed as an input for an NLP algorithm.
How does NLP work?
NLP deals with applying algorithms that extract the rules of a natural language and covert it so a computer can understand. We first provide the text, and a computer uses algorithms to extract meaning.
Many different techniques are used for this process, including:
- Lemmatization: grouping inflected forms of a word into a single form
- Stemming: Stemming follows an algorithm with steps to perform on the infected words to find the root ,which makes it faster.
- Word segmentation: separating a large piece of text into units
- Parsing: analyzing the grammar of a sentence
- Word sense disambiguation: determine meaning to word based on context
When it comes to written data, we use a text corpus and tokenization. A text corpus is essentially our vocabulary. We can use character-based or word-based vocabularies, which are more popular.
Then, we need to analyze how many times a word appears in a corpus. We do this by representing the text data as a vector of words. This process is called tokenization.
We use a tokenizer object to convert a text corpus into sequences. This is done with the ML tool TensorFlow. This tool essentially converts each vocabulary word to an integer ID based by descending frequency.