Abstract
Feature extraction from text corpora is an important step in Natural Language Processing (NLP), especially for Machine Learning (ML) techniques. Various NLP tasks have many common steps, e.g. low level act of reading a corpus and obtaining text windows from it. Some high-level processing steps might also be shared, e.g. testing for morpho-syntactic constraints between words. An integrated feature extraction framework removes wasteful redundancy and helps in rapid prototyping.
In this paper we present a flexible feature extraction framework called Fextor. We describe assumptions about the feature extraction process and provide general overview of software architecture. This is accompanied by examples of applications in hugely different NLP tasks. Namely, we show the application of Fextor in: word sense disambiguation, recognition of interchunk syntactic relations, semantic relations between named entities, as well as anaphora resolution.
This work was financed by the National Centre for Research and Development (NCBiR) project SP/I/1/77065/10.
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Broda, B., Kędzia, P., Marcińczuk, M., Radziszewski, A., Ramocki, R., Wardyński, A. (2013). Fextor: A Feature Extraction Framework for Natural Language Processing: A Case Study in Word Sense Disambiguation, Relation Recognition and Anaphora Resolution. In: Przepiórkowski, A., Piasecki, M., Jassem, K., Fuglewicz, P. (eds) Computational Linguistics. Studies in Computational Intelligence, vol 458. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34399-5_3
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