22,833 research outputs found
Continuous Representation of Location for Geolocation and Lexical Dialectology using Mixture Density Networks
We propose a method for embedding two-dimensional locations in a continuous
vector space using a neural network-based model incorporating mixtures of
Gaussian distributions, presenting two model variants for text-based
geolocation and lexical dialectology. Evaluated over Twitter data, the proposed
model outperforms conventional regression-based geolocation and provides a
better estimate of uncertainty. We also show the effectiveness of the
representation for predicting words from location in lexical dialectology, and
evaluate it using the DARE dataset.Comment: Conference on Empirical Methods in Natural Language Processing (EMNLP
2017) September 2017, Copenhagen, Denmar
Outlier Edge Detection Using Random Graph Generation Models and Applications
Outliers are samples that are generated by different mechanisms from other
normal data samples. Graphs, in particular social network graphs, may contain
nodes and edges that are made by scammers, malicious programs or mistakenly by
normal users. Detecting outlier nodes and edges is important for data mining
and graph analytics. However, previous research in the field has merely focused
on detecting outlier nodes. In this article, we study the properties of edges
and propose outlier edge detection algorithms using two random graph generation
models. We found that the edge-ego-network, which can be defined as the induced
graph that contains two end nodes of an edge, their neighboring nodes and the
edges that link these nodes, contains critical information to detect outlier
edges. We evaluated the proposed algorithms by injecting outlier edges into
some real-world graph data. Experiment results show that the proposed
algorithms can effectively detect outlier edges. In particular, the algorithm
based on the Preferential Attachment Random Graph Generation model consistently
gives good performance regardless of the test graph data. Further more, the
proposed algorithms are not limited in the area of outlier edge detection. We
demonstrate three different applications that benefit from the proposed
algorithms: 1) a preprocessing tool that improves the performance of graph
clustering algorithms; 2) an outlier node detection algorithm; and 3) a novel
noisy data clustering algorithm. These applications show the great potential of
the proposed outlier edge detection techniques.Comment: 14 pages, 5 figures, journal pape
Text Analytics for Android Project
Most advanced text analytics and text mining tasks include text classification, text clustering, building ontology, concept/entity extraction, summarization, deriving patterns within the structured data, production of granular taxonomies, sentiment and emotion analysis, document summarization, entity relation modelling, interpretation of the output. Already existing text analytics and text mining cannot develop text material alternatives (perform a multivariant design), perform multiple criteria analysis,
automatically select the most effective variant according to different aspects (citation index of papers (Scopus, ScienceDirect, Google Scholar) and authors (Scopus, ScienceDirect, Google Scholar), Top 25 papers, impact factor of journals, supporting phrases, document name and contents, density of keywords), calculate utility degree and market value. However, the Text Analytics for Android Project can perform the aforementioned functions. To the best of the knowledge herein, these functions have not been previously implemented; thus this is the first attempt to do so. The Text Analytics for Android Project is briefly described in this article
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