Henning Wachsmuth's Text Analysis Pipelines: Towards Ad-hoc Large-Scale Text PDF

By Henning Wachsmuth

ISBN-10: 3319257404

ISBN-13: 9783319257402

ISBN-10: 3319257412

ISBN-13: 9783319257419

This monograph proposes a entire and completely computerized method of designing textual content research pipelines for arbitrary info wishes which are optimum when it comes to run-time potency and that robustly mine correct info from textual content of any style. in keeping with state of the art concepts from desktop studying and different components of man-made intelligence, novel pipeline building and execution algorithms are built and applied in prototypical software program. Formal analyses of the algorithms and wide empirical experiments underline that the proposed strategy represents a necessary step in the direction of the ad-hoc use of textual content mining in net seek and massive facts analytics.
Both internet seek and massive info analytics goal to meet peoples’ wishes for info in an adhoc demeanour. the data looked for is usually hidden in quite a lot of typical language textual content. rather than easily returning hyperlinks to very likely appropriate texts, major seek and analytics engines have began to without delay mine proper info from the texts. To this finish, they execute textual content research pipelines which can encompass numerous complicated information-extraction and text-classification phases. as a result of useful standards of potency and robustness, although, using textual content mining has to date been restricted to expected info wishes that may be fulfilled with really uncomplicated, manually built pipelines.

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Get Text Analysis Pipelines: Towards Ad-hoc Large-Scale Text PDF

This monograph proposes a accomplished and completely automated method of designing textual content research pipelines for arbitrary details wishes which are optimum when it comes to run-time potency and that robustly mine proper info from textual content of any style. in response to state of the art concepts from laptop studying and different components of man-made intelligence, novel pipeline development and execution algorithms are built and carried out in prototypical software program.

Additional resources for Text Analysis Pipelines: Towards Ad-hoc Large-Scale Text Mining

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Experiments. In corpus linguistics, the general method to develop and evaluate both rule-based and statistical text analysis approaches is to perform experiments using a split of a corpus into different datasets. 12 We realize the process underlying this method in the following two ways in this book, both of which are very common in statistical evaluation (Witten and Frank 2005). 12 The development of statistical approaches benefits from a balanced dataset (see above). This can be achieved through either undersampling minority classes or oversampling majority classes.

1 Foundations of Text Mining In this section, we explain all general foundations of text mining the book at hand builds upon. After a brief outline of text mining, we organize the foundations along the three main research fields related to text mining. The goal is not to provide a formal and comprehensive introduction to these fields, but rather to give exactly the information that is necessary to follow our discussion. At the end, we describe how to develop and evaluate approaches to text analysis.

In both cases, they are seen as ground truth annotations (Manning et al. 2008). 11 Besides effectiveness and efficiency, we also investigate the robustness and intelligibility of text analysis in Chap. 5. Further details are given there. 1 Foundations of Text Mining true negatives (TN) all information 31 false negatives (FN) true positives (TP) ground truth information false positives (FP) output information Fig. 5 Venn diagram showing the four sets that can be derived from the ground truth information of some type in a collection of input texts and the output information of that type inferred from the input texts by a text analysis approach.

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Text Analysis Pipelines: Towards Ad-hoc Large-Scale Text Mining by Henning Wachsmuth


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