So far in this course, most of the focus has been on processing words and sentences at the level of forms. We have learned how to clean text, build language models and assign part of speech tags. The next step…
Language is not just a bag of words. The way words are ordered and grouped gives sentences their structure and meaning. Natural Language Processing systems must therefore understand more than isolated tokens. they need at least a partial grasp of…
Many Natural Language Processing tasks can be described as sequence labelling. There is an input sequence of words and for every word we want to predict a label. Part of speech tagging is one of the most important examples. For…
Language modeling are at the heart of many Natural Language Processing systems. Whenever a system needs to guess the next word in a sequence, evaluate how natural a sentence sounds or choose between alternative outputs, it relies on some estimate…
Natural Language Processing systems almost never receive perfectly clean and error free input. Users type quickly on mobile phones. Optical character recognition introduces mistakes when scanning documents. Search queries are full of missing letters, extra letters and swapped characters. Any…
Natural Language Processing systems rarely work directly on raw, messy text. Before any model can classify, translate, summarise or retrieve documents, the text must pass through a careful cleaning and structuring phase. In addition, modern NLP research and development depends…