Abstract
We started with Lewin’s aphorism, “there is nothing as practical as a good theory”. Vector semantics, the broad theory that was raised from a Firthian slogan to a computational theory by Schütze, 1993, has clearly proven its practicality on a wide range of tasks from Named Entity Recognition (see 8.1) to sentiment analysis. But the farther we move from basic labeling and classification tasks, the more indirect the impact becomes, until we reach a point where some conceptual model needs to be fitted to the text. Perhaps the best known such problem is time extraction and normalization, where our target model is the standard (Gregorian) calendar rather than the simple (naive) model we discussed in 3.2. In 9.1, based almost entirely on the work of Gábor Recski and his co-workers at TU Wien, we outline a system that probes for matches with a far more complex conceptual model, that of building codes and regulations in effect in the city of Vienna.
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Kornai, A. (2023). Applications. In: Vector Semantics. Cognitive Technologies. Springer, Singapore. https://doi.org/10.1007/978-981-19-5607-2_9
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DOI: https://doi.org/10.1007/978-981-19-5607-2_9
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