![]() The target of named-entity recognition is the identification of references to people, organizations, locations, etc., in the text. Lemmatization maps inflected words to their uninflected root, the lemma (e.g., âareâ â âbeâ). Part-of-speech (POS) tagging is the process of determining the word class, whether itâs a noun, a verb, an article, etc. Here, tokenization splits a document into a list of separate tokens like words and punctuation characters. Now the text is clean enough to start linguistic processing. Finally, we can mask or remove identifiers like URLs or email addresses if they are not relevant for the analysis or if there are privacy issues. During character normalization, special characters such as accents and hyphens are transformed into a standard representation. We start by identifying and removing noise in text like HTML tags and nonprintable characters. The first major block of operations in our pipeline is data cleaning. A pipeline with typical preprocessing steps for textual data. But frequent words carrying little meaning, the so-called stop words, introduce noise into machine learning and data analysis because they make it harder to detect patterns.įigure 4-1. The raw data may include HTML tags or special characters that should be removed in most cases. ![]() When working with text, noise comes in different flavors. Whatâs noise and what isnât always depends on the analysis you are going to perform. Correctly identifying such word sequences as compound structures requires sophisticated linguistic processing.ĭata preparation or data preprocessing in general involves not only the transformation of data into a form that can serve as the basis for analysis but also the removal of disturbing noise. Think of the word sequence New York, which should be treated as a single named-entity. To build models on the content, we need to transform a text into a sequence of words or, more generally, meaningful sequences of characters called tokens. Technically, any text document is just a sequence of characters. Preparing Textual Data for Statistics and Machine Learning
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