The fundamental logic behind NLP
Advancements in NLP are happening very rapidly, introducing new features that mean language understanding and sentence generation are becoming more and more accurate.
Let’s consider using some financial world example to make the explanation simpler and present the challenges of the complexities of human language clearly.
To begin with, the machine needs to understand which market. Can markets exhibit hunger and satisfy hunger with M&A? Is M&A a food? Is ‘no sign of abating’ a reverse statement of remaining intense? For a mortgage, can a repayment really save money?
So how does NLP manage in these instances?
Extraction processes follow a series of layers to extract maximum value from unstructured data. Key steps include syntax analysis used to address lexicon and grammar. Semantical analysis, which is used to provide sense and understand relationships that exist in the data. And then, pragmatical analysis, what is sometimes called contextual information that integrates information that sets a context around that information.
From the diagram above, you can imagine that human understands the meaning of a sentence through its individual words without having to think. However, a computer system would need to go through a lot of different stages to decompose this sentence into individual components, find the relationship between them, and infer meaning.
The foundational steps to fulfilling the above-given explanation, however, is technical in nature. If you are keen to find out more details about preprocessing text data, please google for terms like tokenization, acronym normalization, lemmatization, sentence and phrase boundaries. In addition, you can also search for full-text extraction terms that include named entity recognition, entity extraction, content categorization, content clustering, fact extraction, relationship extraction, summarization, and similarity for basic understanding.