Natural Language Processing
(Natural Language Processing, NLP for short) is the process of interpreting and understanding human language. It is the first step in making sense of text data. NLP represents the structure of sentences using various methods such as phrase structure trees (syntax trees) and dependency structures (relation diagrams). Here is an example of a syntax tree:
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Although there can be multiple structures for a given sentence, humans generally agree on a single interpretation. The challenge is how to replicate this human judgment using computers. How can we reconcile the discrete nature of “structure” with the continuous nature of “meaning”?
One approach is the Shift-Reduce method. It involves examining pairs of words in a sentence from the beginning and deciding whether to combine them or not. If they are combined, they are treated as a single word. This decision is made using machine learning techniques, such as binary classification, with the help of a corpus. However, a challenge arises when we need to make decisions without knowing the words to the right of the current position. To address this, techniques like beam search are used, where both combining and not combining the words are explored up to a certain point.
This approach seems similar to the concept of Recurrent Neural Networks (RNN).