@article{wrro135565,
          volume = {137},
           month = {October},
          author = {Z. Zhang and J. Petrak and D. Maynard},
            note = {{\copyright} 2018 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the SEMANTiCS 2018 ? 14th International Conference on Semantic Systems.},
       booktitle = {14th International Conference on Semantic Systems},
           title = {Adapted TextRank for Term Extraction: a generic method of improving automatic term extraction algorithms},
       publisher = {Elsevier},
            year = {2018},
         journal = {Procedia Computer Science},
           pages = {102--108},
        keywords = {Automatic term extraction; NLP; terminology; ontology engineering},
             url = {http://eprints.whiterose.ac.uk/135565/},
        abstract = {Automatic Term Extraction is a fundamental Natural Language Processing task often used in many knowledge acquisition processes. It is a challenging NLP task due to its high domain dependence: no existing methods can consistently outperform others in
all domains, and good ATE is very much an unsolved problem. We propose a generic method for improving the ranking of terms
extracted by a potentially wide range of existing ATE methods. We re-design the well-known TextRank algorithm to work at corpus
level, using easily obtainable domain resources in the form of seed words or phrases, to compute a score for a word from the target
dataset. This is used to refine a candidate term?s score computed by an existing ATE method, potentially improving the ranking of
real terms to be selected for tasks such as ontology engineering. Evaluation shows consistent improvement on 10 state of the art
ATE methods by up to 25 percentage points in average precision measured at top-ranked K candidates.
}
}