Abstract
This work proposes a novel approach to visually interact with semantic networks constructed via natural language processing techniques. The proposed web interface, WINS, allows the user to select a textual document to be analyzed, choose the algorithm to construct the semantic network, and visualize the network with its metrics. Unlike previous works, which are typically based on co-occurrence matrix for constructing the text network, the proposed interface embeds an additional approach based on the combination of network science with distributed representations of words and phrases.
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Notes
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- 2.
Chunking, n-gramming are text pre-processing phases to segment raw text into these units [2].
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Proximity can be quantified with any spatial proximity measurement; for example, using euclidean distance, or cosine distance.
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The term “card” refers to the HTML division class used for the aesthetic layout design.
- 5.
Dimensionality of vectors has been reduced to two components. This can be achieved with different techniques with some limitations as discussed by the authors of [14].
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Acknowledgements
This material is based upon work supported, in whole or in part, by the U.S. Department of Defense through the Office of the Assistant Secretary of Defense for Research and Engineering (ASD(R&E)) under Contract [HQ0034-19-D- 0003, TO#0150].
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Borrelli, D. et al. (2020). WINS: Web Interface for Network Science via Natural Language Distributed Representations. In: Stephanidis, C., Antona, M. (eds) HCI International 2020 - Posters. HCII 2020. Communications in Computer and Information Science, vol 1224. Springer, Cham. https://doi.org/10.1007/978-3-030-50726-8_80
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