Natural language processing

History and Evolution of NLP - Natural language processing (NLP) has its roots in the 1950s. - Alan Turing proposed the Turing test as a criterion of intelligence in 1950. - The Georgetown experiment in 1954 involved automatic translation of Russian sentences into English. - Symbolic NLP (1950s - early 1990s) involves emulating natural language understanding using predefined rules. - The Chinese room experiment by John Searle demonstrates the premise of symbolic NLP. - Progress in machine translation was limited until the late 1980s when statistical machine translation systems were developed. - Statistical NLP (1990s-2010s) emerged in the late 1980s and mid-1990s, replacing rule-based approaches. - Neural NLP (present) gained popularity due to its performance and applicability in language modeling. Approaches in NLP - Symbolic approach involves hand-coding rules for manipulating symbols and using a dictionary lookup. - Statistical approach emerged in the late 1980s and mid-1990s, replacing rule-based approaches. - Neural networks approach replaced the statistical approach since 2015. - Machine learning approaches, such as statistical and neural networks, have advantages over the symbolic approach. - Statistical and neural networks methods can focus on common cases extracted from a corpus of texts. Common NLP Tasks - Optical character recognition (OCR) involves determining text from an image. - Speech recognition converts spoken language into text. - Speech segmentation separates spoken language into words. - Text-to-speech transforms written text into spoken language. - Word segmentation (tokenization) separates continuous text into separate words. NLP Applications - Generating readable summaries of text - Detecting and correcting grammatical errors - Automatically translating text between human languages - Understanding natural language and converting it into formal representations - Generating natural language from structured information Challenges in NLP - Challenges in NLP include speech recognition, natural-language understanding, and natural-language generation. - NLP aims to give computers the ability to support and manipulate human language. - It involves processing natural language datasets using rule-based or probabilistic machine learning approaches. - The goal is for computers to understand the contextual nuances of language and extract information from documents. - NLP technology can categorize, organize, and extract insights from documents.