Applied Text Analysis with Python: Enabling Language Aware by Benjamin Bengfort, Rebecca Bilbro, Tony Ojeda

By Benjamin Bengfort, Rebecca Bilbro, Tony Ojeda

The programming panorama of usual language processing has replaced dramatically some time past few years. computer studying techniques now require mature instruments like Python’s scikit-learn to use types to textual content at scale. This useful consultant exhibits programmers and knowledge scientists who've an intermediate-level realizing of Python and a simple realizing of computer studying and traditional language processing find out how to develop into more adept in those intriguing parts of information science.

This publication offers a concise, targeted, and utilized method of textual content research with Python, and covers subject matters together with textual content ingestion and wrangling, simple computer studying on textual content, category for textual content research, entity solution, and textual content visualization. utilized textual content research with Python will provide help to layout and boost language-aware info products.

You’ll find out how and why computing device studying algorithms make judgements approximately language to investigate textual content; the right way to ingest, wrangle, and preprocess language info; and the way the 3 basic textual content research libraries in Python paintings in live performance. eventually, this booklet will show you how to layout and strengthen language-aware info products.

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Extra resources for Applied Text Analysis with Python: Enabling Language Aware Data Products with Machine Learning

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To name a few notable utility readers: PlaintextCorpusReader: a reader for corpora that consist of plaintext documents, where paragraphs are assumed to be split using blank lines. TaggedCorpusReader: a reader for simple part-of-speech tagged corpora, where sentences are on their own line, and tokens are delimited with their tag. BracketParseCorpusReader: a reader for corpora that consist of parenthesis-delineated parse trees. ChunkedCorpusReader: a reader for chunked (and optionally tagged) corpora formatted with parentheses.

Why is it that models trained in a specific field or domain of the language would perform better than ones trained on general language? Consider that the term “bank” is very likely to be an institution that produces fiscal and monetary tools in an economics, financial, or political domain, whereas in an aviation or vehicular domain it is more likely to be a form of motion that results in the change of direction of an aircraft. By fitting models in a narrower context, the prediction space is smaller and more specific, and therefore better able to handle the flexible aspects of language.

The addition of the WORM store to our data ingestion workflow means that we need to store data in two places: the raw corpus as well as the preprocessed corpus, and leads to the question: where should that data be stored? When we think of data management, the first thought is a database. Databases are certainly valuable tools in building language aware data products, and many provide full-text search functionality and other types of indexing. However, consider the fact that most databases are constructed to retrieve or update only a couple of rows per transaction.

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