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LG-Starship: A Framework for Text Analysis

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Web, Artificial Intelligence and Network Applications (WAINA 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1150))

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Abstract

In this work we present a new framework for the analysis of Italian texts that could help linguists to perform rapid text analysis. The framework, that performs both statistical and rule-based analysis, is called LG-Starship. The idea is to built a modular software that includes the basic algorithms to perform different kinds of analysis. The framework will include a Preprocessing Module a POS Tagging and Lemmatization module, a Statistic Module, a Semantic Module based on Distributional Analysis algorithms, and a Syntactic Module, which analyze syntax structures of a selected sentence and tag the verbs and its arguments with semantic labels. The objective of the Framework is to build an “all-in-one” platform for NLP which allows any kind of users to perform basic and advanced text analysis.

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Authors and Affiliations

  1. University of Salerno, via Giovanni Paolo II, 132, 84084, Fisciano, SA, Italy

    Alessandro Maisto

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  1. Alessandro Maisto
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Correspondence to Alessandro Maisto .

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Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Department of Electrical Engineering and Information Technology, University of Naples “Frederico II”, Naples, Italy

    Flora Amato

  3. Department of Political Science, University of Campania Luigi Vanvitelli, Caserta, Italy

    Francesco Moscato

  4. Faculty of Business Administration, Rissho University, Tokyo, Japan

    Tomoya Enokido

  5. Department of Advanced Sciences, Hosei University, Tokyo, Japan

    Makoto Takizawa

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Maisto, A. (2020). LG-Starship: A Framework for Text Analysis. In: Barolli, L., Amato, F., Moscato, F., Enokido, T., Takizawa, M. (eds) Web, Artificial Intelligence and Network Applications. WAINA 2020. Advances in Intelligent Systems and Computing, vol 1150. Springer, Cham. https://doi.org/10.1007/978-3-030-44038-1_38

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