Abstract:
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Comunicació presentada a: The 4th International Conference, INSCI 2017, celebrat a Thessaloniki, Grècia, del 22 al 24 de novembre de 2017. |
Abstract:
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Nowadays, a large amount of text documents are produced
on a daily basis, so we need e cient and e ective access to their con-
tent. News articles, blogs and technical reports are often lengthy, so the
reader needs a quick overview of the underlying content. To that end we
present graph-based models for keyword extraction, in order to compare
the Bag of Words model with the Graph of Words model in the key-
word extraction problem. We compare their performance in two publicly
available datasets using the evaluation measures Precision@10, mean Av-
erage Precision and Jaccard coe cient. The methods we have selected
for comparison are grouped into two main categories. On the one hand,
centrality measures on the formulated Graph-of-Words (GoW) are able
to rank all words in a document from the most central to the less central,
according to their score in the GoW representation. On the other hand,
community detection algorithms on the GoW provide the largest commu-
nity that contains the key nodes (words) in the GoW. We selected these
methods as the most prominent methods to identify central nodes in a
GoW model. We conclude that term-frequency scores (BoW model) are
useful only in the case of less structured text, while in more structured
text documents, the order of words plays a key role and graph-based
models are superior to the term-frequency scores per document. |