By Henning Wachsmuth
This monograph proposes a accomplished and completely computerized method of designing textual content research pipelines for arbitrary info wishes which are optimum by way of run-time potency and that robustly mine appropriate details from textual content of any sort. in response to state of the art recommendations from desktop studying and different components of synthetic intelligence, novel pipeline building and execution algorithms are constructed and applied in prototypical software program. Formal analyses of the algorithms and vast empirical experiments underline that the proposed technique represents a vital step in the direction of the ad-hoc use of textual content mining in net seek and large information analytics.
Both internet seek and large info analytics objective to satisfy peoples’ wishes for info in an adhoc demeanour. the data looked for is usually hidden in quite a lot of average language textual content. rather than easily returning hyperlinks to very likely suitable texts, best seek and analytics engines have began to at once mine suitable details from the texts. To this finish, they execute textual content research pipelines which could encompass numerous advanced information-extraction and text-classification levels. because of functional requisites of potency and robustness, in spite of the fact that, using textual content mining has to this point been constrained to expected details wishes that may be fulfilled with really basic, manually developed pipelines.
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Additional resources for Text Analysis Pipelines: Towards Ad-hoc Large-Scale Text Mining
Through optimized scheduling, we can greatly improve the run-time efficiency of traditional text analysis pipelines, which benefits large-scale text mining. Through adaptive scheduling, we maintain efficiency even on highly heterogeneous texts. 4 Contributions and Outline of This Book 13 3. Pipeline robustness. Through the overall analysis, we can significantly improve the domain robustness of text analysis pipelines for the classification of argumentative texts over traditional approaches. 6 shows how these high-level main contributions relate to the three core ideas within our overall approach.
2011). We have realized our approach to ad-hoc pipeline construction as a freely available expert system (Wachsmuth et al. 2013a). Experiments with this system in the InfexBA context and on the scientifically important biomedical extraction task Genia (Kim et al. 2011) indicate that efficient and effective pipelines can be designed in near-zero time. Open problems are largely due to automation only, such as a missing weighting of the quality criteria to be met. The use of our input control comes even without any notable drawback.
1(b). Sometimes, also an objective (or neutral) “polarity” is considered, although this class rather refers to subjectivity (Pang and Lee 2004). , ) ... input data 25 ... , ) output information generalization machine learning instances patterns Fig. 2 Illustration of a high-level view of data mining. Input data is represented as a set of instances, from which a model is derived using machine learning. The model is then generalized to infer new output information. sentiment scoring here. We employ a number of sentiment analysis algorithms in Sect.