Graph vs. bag representation models for the topic classification of web documents
Text classification constitutes a popular task in Web research with various applications that
range from spam filtering to sentiment analysis. In this paper, we argue that its performance …
range from spam filtering to sentiment analysis. In this paper, we argue that its performance …
Representation models for text classification: a comparative analysis over three web document types
G Giannakopoulos, P Mavridi, G Paliouras… - Proceedings of the 2nd …, 2012 - dl.acm.org
Text classification constitutes a popular task in Web research with various applications that
range from spam filtering to sentiment analysis. To address it, patterns of co-occurring words …
range from spam filtering to sentiment analysis. To address it, patterns of co-occurring words …
Fusing document, collection and label graph-based representations with word embeddings for text classification
K Skianis, F Malliaros… - … -HLT Workshop on …, 2018 - centralesupelec.hal.science
Contrary to the traditional Bag-of-Words approach, we consider the Graph-of-Words (GoW)
model in which each document is represented by a graph that encodes relationships …
model in which each document is represented by a graph that encodes relationships …
Graph-based techniques for topic classification of tweets in Spanish
Topic classification of texts is one of the most interesting challenges in Natural Language
Processing (NLP). Topic classifiers commonly use a bag-of-words approach, in which the …
Processing (NLP). Topic classifiers commonly use a bag-of-words approach, in which the …
An improved classification strategy for filtering relevant tweets using bag-of-word classifiers
In this paper we have presented a classification framework for classifying tweets relevant to
some specific target sectors. Due to the imposed length restriction on an individual tweet …
some specific target sectors. Due to the imposed length restriction on an individual tweet …
Graph-based term weighting for text categorization
FD Malliaros, K Skianis - Proceedings of the 2015 IEEE/ACM …, 2015 - dl.acm.org
Text categorization is an important task with plenty of applications, ranging from sentiment
analysis to automated news classification. In this paper, we introduce a novel graph-based …
analysis to automated news classification. In this paper, we introduce a novel graph-based …
Wikipedia-based hybrid document representation for textual news classification
The sheer amount of news items that are published every day makes worth the task of
automating their classification. The common approach consists in representing news items …
automating their classification. The common approach consists in representing news items …
Bag of textual graphs (BoTG): A general graph‐based text representation model
Text representation models are the fundamental basis for information retrieval and text
mining tasks. Although different text models have been proposed, they typically target …
mining tasks. Although different text models have been proposed, they typically target …
A thorough evaluation of distance-based meta-features for automated text classification
We address the problem of automatically learning to classify texts by exploiting information
derived from meta-features, ie, features derived from the original bag-of-words …
derived from meta-features, ie, features derived from the original bag-of-words …
Raising the baseline for high-precision text classifiers
A Kolcz, W Yih - Proceedings of the 13th ACM SIGKDD international …, 2007 - dl.acm.org
Many important application areas of text classifiers demand high precision andit is common
to compare prospective solutions to the performance of Naive Bayes. This baseline is …
to compare prospective solutions to the performance of Naive Bayes. This baseline is …