The Power of NoisyText

Noise is a stark reality in real life data. Indeed, noise is a prevalent issue in real-life data across various domains, including text analytics. Noise can manifest in different forms, such as spelling errors, grammatical mistakes, irrelevant information, and inconsistencies. Managing noise is crucial in text analytics because it directly affects the quality and reliability of the insights derived from the data. Noisy unstructured text is common in informal settings such as on-line chat, SMS, email, newsgroups and blogs, automatically transcribed text from speech data, and automatically recognized text from printed or handwritten material. Gigabytes of such data is being generated every day on the Internet, in contact centers, and on mobile phones. Researchers have looked at various text mining issues such as pre-processing and cleaning noisy text, information extraction, rule learning, and classification for noisy text. Noisy Text Analytics is defined as a process of information extraction whose goal is to automatically extract structured or semi structured information from noisy unstructured text data.