Yet Another Ranking Function for Automatic Multiword Term Extraction

Juan Antonio Lossio-Ventura 1, 2, * Clement Jonquet 2, 3 Mathieu Roche 4, 1 Maguelonne Teisseire 4, 1
* Corresponding author
1 ADVANSE - ADVanced Analytics for data SciencE
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
2 SMILE - Système Multi-agent, Interaction, Langage, Evolution
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Term extraction is an essential task in domain knowledge acquisition. We propose two new measures to extract multiword terms from a domain-specific text. The first measure is both linguistic and statistical based. The second measure is graph-based, allowing assessment of the importance of a multiword term of a domain. Existing measures often solve some problems related (but not completely) to term extraction, e.g., noise, silence, low frequency, large-corpora, complexity of the multiword term extraction process. Instead, we focus on managing the entire set of problems, e.g., detecting rare terms and overcoming the low frequency issue. We show that the two proposed measures outperform precision results previously reported for automatic multiword extraction by comparing them with the state-of-the-art reference measures.
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01068556
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Juan Antonio Lossio-Ventura, Clement Jonquet, Mathieu Roche, Maguelonne Teisseire. Yet Another Ranking Function for Automatic Multiword Term Extraction. PolTAL: Natural Language Processing, Sep 2014, Warsaw, Poland. pp.52-64, ⟨10.1007/978-3-319-10888-9_6⟩. ⟨lirmm-01068556⟩

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