By Henning Wachsmuth
This monograph proposes a finished 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 appropriate info from textual content of any type. in line with cutting-edge innovations from laptop studying and different parts of synthetic intelligence, novel pipeline building and execution algorithms are constructed and carried out in prototypical software program. Formal analyses of the algorithms and huge empirical experiments underline that the proposed process represents a vital step in the direction of the ad-hoc use of textual content mining in internet seek and large information analytics.
Both internet seek and large information analytics objective to satisfy peoples’ wishes for info in an adhoc demeanour. the knowledge hunted for is usually hidden in quite a lot of normal language textual content. rather than easily returning hyperlinks to probably correct texts, best seek and analytics engines have began to at once mine proper details from the texts. To this finish, they execute textual content research pipelines that could encompass numerous complicated information-extraction and text-classification phases. because of sensible necessities of potency and robustness, notwithstanding, using textual content mining has up to now been constrained to expected info wishes that may be fulfilled with quite basic, manually built pipelines.
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Extra info for Text Analysis Pipelines: Towards Ad-hoc Large-Scale Text Mining
In the following, we look at the concepts of the three fields that are important for our discussion of text analysis. 1 Notice that, throughout this book, we assume that the reader has a more or less graduate-level background in computer science or similar. 2 Ananiadou and McNaught (2005) refer to the second step as information extraction. While we agree that information extraction is often the important part of this step, also other techniques from natural language processing play a role, as discussed later in this section.
6(b). We conduct according experiments once in Chap. 5. Comparison. The measured effectiveness and efficiency results of a text analysis approach are usually compared to alternative ways of addressing the given task in order to assess whether the results are good bad. 15 For simplicity, effectiveness is thus often measured with respect to the human-annotated ground truth. While there is no general upper-bound efficiency ceiling, we see in the subsequent chapters that optimal efficiency can mostly be determined in a given experiment setting.
2) and then address ad-hoc pipeline construction (Sect. 3). In Sect. 4, we develop an information-oriented view of text analysis, which can be operationalized to achieve an optimal pipeline execution (Sect. 5). This view provides new ways of trading efficiency for effectiveness (Sect. 6). Next, we optimize pipeline efficiency in Chap. 4, starting with a formal solution to the optimal scheduling of text analysis algorithms (Sect. 1). We analyze the impact of the distribution of relevant information in Sect.