Text Analysis Pipelines

• Published in: Text Analysis Pipelines Vol. 9383; pp. 19 - 53
• Main Author: Wachsmuth, Henning
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 01.01.2015; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Analysis Step; Artificial intelligence; Information Extraction; Information retrieval; Machine Learning; Mathematical theory of computation; Sentiment Analysis; Text Analysis
• ISBN: 9783319257402; 3319257404
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-25741-9_2

The understanding Pipeline, text analysis natural language is one of the primary abilities that provide the basis for human intelligence. Since the invention of computers, people have thought about how to operationalize this ability in software applications (Jurafsky and Martin 2009). The rise of the internet in the 1990s then made explicit the practical need for automatically processing natural language in order to access relevant Relevancerelevant information. Search engine, as a solution, have revolutionalized the way we can find such information Ad-hoc in large amounts of text (Manning et al. 2008). Until today, however, search Search engine excel in finding relevant Relevancerelevant text rather than in understanding what information is Relevancerelevant information in the texts. Chapter 10.1007/978-3-319-25741-9_1 has proposed text Text mining as a means to achieve progress towards the latter, thereby making information search more intelligent. At the heart of every text Text miningapplication application lies the analysis of text, mostly realized in the form of text analysis Pipeline, text analysis. In this chapter, we present the basics required to follow the approaches of this book to improve such pipelines for enabling text Text miningad-hoc large-scale text mining ad-hoc on large amounts of text as well as the state of the art in this respect. Text mining combines techniques from information Information retrieval, natural language Natural language processing, and data Data mining. In Sect. 2.1, we first provide a focused overview of those techniques referred to in this book. Then, we define the text analysis Text analysisprocess and Pipeline, text analysis that we consider in our proposed approaches (Sect. 2.2). We evaluate the different approaches based on texts and pipelines from a number of case studies introduced in Sect. 2.3. Finally, Sect. 2.4 surveys and discusses related existing work in the broad context of ad-hoc large-scale text Text miningad-hoc large-scale text mining.