452 research outputs found
Identifying high-impact sub-structures for convolution kernels in document-level sentiment classification
Convolution kernels support the modeling of complex syntactic information in machine-learning tasks. However, such models are highly sensitive to the type and size of syntactic structure used. It is therefore an important challenge to automatically identify high impact sub-structures relevant to a given task. In this paper we present a systematic study investigating (combinations of) sequence and convolution kernels using different types of substructures in document-level sentiment classification. We show that minimal sub-structures extracted from constituency and dependency trees guided by a polarity lexicon show 1.45 point absolute improvement in accuracy over a bag-of-words classifier on a widely used sentiment corpus
XML Schema Clustering with Semantic and Hierarchical Similarity Measures
With the growing popularity of XML as the data representation language, collections of the XML data are exploded in numbers. The methods are required to manage and discover the useful information from them for the improved document handling. We present a schema clustering process by organising the heterogeneous XML schemas into various groups. The methodology considers not only the linguistic and the context of the elements but also the hierarchical structural similarity. We support our findings with experiments and analysis
Data Mining : Masa Lalu, Sekarang, dan Masa Mendatang
Data mining telah menjadi disiplin ilmu yang dibangun dalam domain kecerdasan buatan (AI), dan rekayasa pengetahuan (KE). Data mining berakar pada machine learning dan statistika, tetapi merambah bidang lain dalam ilmu komputer dan ilmu lainnya seperti biologi, lingkungan, finansial, jaringan dan sebagainya. Data mining telah mendapatkan begitu besar perhatian pada dekade terakhir sehubungan dengan perkembangan hardware yang menyediakan kemampuan komputasi luar biasa yang memungkinkan pengolahan data besar. Tidak seperti kajian lain dalam AI dan KE, data mining dapat diperdebatkan sebagai sebuah aplikasi dibandingkan dengan sebuah teknologi, dengan demikian diharapkan akan menjadi topik yang hangat dibahas di masa mendatang, mengingat pertumbuhan data yang bersifat eksponensial. Paper ini memberikan kilas Balik perjalanan sejarah data mining, keadaan saat ini dan beberapa pandangan dan perkembangan ke depan
Semi-supervised co-clustering on attributed heterogeneous information networks
trueThe embargo period should be 2 years -- not sure why under the drop down I can only select one year. Please validate.</p
Fast Distributed PageRank Computation
Over the last decade, PageRank has gained importance in a wide range of
applications and domains, ever since it first proved to be effective in
determining node importance in large graphs (and was a pioneering idea behind
Google's search engine). In distributed computing alone, PageRank vector, or
more generally random walk based quantities have been used for several
different applications ranging from determining important nodes, load
balancing, search, and identifying connectivity structures. Surprisingly,
however, there has been little work towards designing provably efficient
fully-distributed algorithms for computing PageRank. The difficulty is that
traditional matrix-vector multiplication style iterative methods may not always
adapt well to the distributed setting owing to communication bandwidth
restrictions and convergence rates.
In this paper, we present fast random walk-based distributed algorithms for
computing PageRanks in general graphs and prove strong bounds on the round
complexity. We first present a distributed algorithm that takes O\big(\log
n/\eps \big) rounds with high probability on any graph (directed or
undirected), where is the network size and \eps is the reset probability
used in the PageRank computation (typically \eps is a fixed constant). We
then present a faster algorithm that takes O\big(\sqrt{\log n}/\eps \big)
rounds in undirected graphs. Both of the above algorithms are scalable, as each
node sends only small (\polylog n) number of bits over each edge per round.
To the best of our knowledge, these are the first fully distributed algorithms
for computing PageRank vector with provably efficient running time.Comment: 14 page
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