Text Analysis Pipelines - Towards Ad-hoc Large-scale Text Mining - Wachsmuth, Henning

Pipelines for Ad-hoc Large-scale Text Mining - Wachsmuth, Henning
Today's web search and big data analytics applications aim to address information needs~(typically given in the form of search queries) ad-hoc on large numbers of texts. In order to directly return relevant information instead of only returning potentially relevant texts, these applications have begun to employ text mining. The term text mining covers tasks that deal with the inference of structured high-quality information from collections and streams of unstructured input texts. Text mining requires task-specific text analysis processes that may consist of several interdependent steps. These processes are realized with sequences of algorithms from information extraction, text classification, and natural language processing. However, the use of such text analysis pipelines is still restricted to addressing a few predefined information needs. We argue that the reasons behind are three-fold: First, text analysis pipelines are usually made manually in respect of the given information need and input texts, because their design requires expert knowledge about the algorithms to be employed. When information needs have to be addressed that are unknown beforehand, text mining hence cannot be performed ad-hoc. Second, text analysis pipelines tend to be inefficient in terms of run-time, because their execution often includes analyzing texts with computationally expensive algorithms. When information needs have to be addressed ad-hoc, text mining hence cannot be performed in the large. And third, text analysis pipelines tend not to robustly achieve high effectiveness on all texts, because their results are often inferred by algorithms that rely on domain-dependent features of texts. Hence, text mining currently cannot guarantee to infer high-quality information. In this thesis, we contribute to the question of how to address information needs from text mining ad-hoc in an efficient and domain-robust manner. We observe that knowledge about a text analysis process and information obtained within the process help to improve the design, the execution, and the results of the pipeline that realizes the process. To this end, we apply different techniques from classical and statistical artificial intelligence. In particular, we first develop knowledge-based approaches for an ad-hoc pipeline construction and for an optimal execution of a pipeline on its input. Then, we show theoretically and practically how to optimize and adapt the schedule of the algorithms in a pipeline based on information in the analyzed input texts in order to maximize execution efficiency. Finally, we learn patterns in the argumentation structures of texts statistically that remain strongly invariant across domains and that, thereby, allow for more robust analysis results in a restricted set of tasks. We formally analyze all developed approaches and we implement them as open-source software applications. Based on these applications, we evaluate the approaches on established and on newly created collections of texts for scientifically and industrially important text analysis tasks, such as financial event extraction and fine-grained sentiment analysis. Our findings show that text analysis pipelines can be designed automatically, which process only portions of text that are relevant for the information need at hand. Through scheduling, the run-time efficiency of pipelines can be improved by up to more than one order of magnitude while maintaining effectiveness. Moreover, we provide evidence that a pipeline's domain robustness substantially benefits from focusing on argumentation structure in tasks like sentiment analysis. We conclude that our approaches denote essential building blocks of enabling ad-hoc large-scale text mining in web search and big data analytics applications.

Embarrassed or Awkward? Ranking Emotion Synonyms for ESL Learners’ Appropriate Wording - Chen, Wei-Fan; Chen, MeiHua; Ku, Lun-Wei (Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications)

Mining Supportive and Unsupportive Evidence from Facebook Using Anti-reconstruction of the Nuclear Power Plant as an Example - Chen, Wei-Fan; Ku, Lun-Wei; Lee, Yann-Hui (2015 AAAI Spring Symposium Series)

Topic-based Stance Mining for Social Media Texts - Chen, Wei-Fan; Lee, Yann-Hui; Ku, Lun-Wei (International Conference on HCI in Business)

A Computer-assistance Learning System for Emotional Wording - Chen, Wei-Fan; Chen, Mei-Hua; Chen, Ming-Lung; Ku, Lun-Wei (IEEE Transactions on Knowledge and Data Engineering)

Technology Enhanced Emotion Expression Learning - Chen, Mei-Hua; Chen, Wei-Fan; Ku, Lun-Wei (Proceedings of the sixth joint Foreign Language Education and Technology Conference (FLEAT VI))

A Review Corpus for Argumentation Analysis - Wachsmuth, Henning; Trenkmann, Martin; Stein, Benno; Engels, Gregor; Palakarska, Tsvetomira (Proceedings of the 15th International Conference on Intelligent Text Processing and Computational Linguistics)

PBlaman: performance blame analysis based on Palladio contracts - Brüseke, Frank; Wachsmuth, Henning; Engels, Gregor; Becker, Steffen (Proceedings of the 4th International Symposium on Autonomous Minirobots for Research and Edutainment)

Modeling Review Argumentation for Robust Sentiment Analysis - Wachsmuth, Henning; Trenkmann, Martin; Stein, Benno; Engels, Gregor (Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers)

iSoNTRE: The Social Network Transformer into Recommendation Engine - Abu Quba Rana, Chamsi; Hassas, Salima; Usama, Fayyad; Alshomary, Milad; Gertosio, Christine (2014 IEEE/ACS 11th International Conference on Computer Systems and Applications (AICCSA))

RESOLVE: An Emotion Word Suggestion System Facilitates Language Learners’ Emotional Expressions - Chen, Mei-Hua; Chen, Wei-Fan; Ku, Lun-Wei (Proceedings of the AsiaCALL 2014)

Learning Efficient Information Extraction on Heterogeneous Texts - Wachsmuth, Henning; Stein, Benno; Engels, Gregor (Proceedings of the Sixth International Joint Conference on Natural Language Processing)

Information Extraction as a Filtering Task - Wachsmuth, Henning; Stein, Benno; Engels, Gregor (Proceedings of the 22nd ACM International Conference on Conference on Information & Knowledge Management)

Automatic Pipeline Construction for Real-Time Annotation - Wachsmuth, Henning; Rose, Mirko; Engels, Gregor (14th International Conference on Intelligent Text Processing and Computational Linguistics)

Optimal Scheduling of Information Extraction Algorithms - Wachsmuth, Henning; Stein, Benno (Proceedings of COLING 2012: Posters)

A Syllable-based Prosody Modeling for L1 and L2 English Speeches - Chen, Wei-Fan; Kuo, Chin-Kuan; Wang, Yih-Ru; Chen, Sin-Horng (Proceedings of the 8th International Symposium on Chinese Spoken Language Processing)

Back to the Roots of Genres: Text Classification by Language Function - Wachsmuth, Henning; Bujna, Kathrin (Proceedings of 5th International Joint Conference on Natural Language Processing)

Constructing Efficient Information Extraction Pipelines - Wachsmuth, Henning; Stein, Benno; Engels, Gregor (20th ACM International Conference on Information and Knowledge Management)

Efficient Statement Identification for Automatic Market Forecasting - Wachsmuth, Henning; Prettenhofer, Peter; Stein, Benno (Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010))

Smart Teams: Simulating Large Robotic Swarms in Vast Environments - Arens, Stephan; Buss, Alexander; Deck, Helena; Dynia, Miroslaw; Fischer, Matthias; Hagedorn, Holger; Isaak, Peter; Kutylowski, Jaroslaw; Meyer auf der Heide, Friedhelm; Nesterow, Viktor; Ogiermann, Adrian; Stobbe, Boris; et al. (Proceedings of the 4th International Symposium on Autonomous Minirobots for Research and Edutainment)
We consider the problem of exploring an unknown environment using a swarm of autonomous robots with collective behavior emerging from their local rules. Each robot has only a very restricted view on the environment which makes cooperation difficult. We introduce a software system which is capable of simulating a large number of such robots (e.g. 1000) on highly complex terrains with millions of obstacles. Its main purpose is to easily integrate and evaluate any kind of algorithm for controlling the robot behavior. The simulation may be observed in real-time via a visualization that displays both the individual and the collective progress of the robots. We present the system design, its main features and underlying concepts.