2,292 research outputs found
Discovering items with potential popularity on social media
Predicting the future popularity of online content is highly important in
many applications. Preferential attachment phenomena is encountered in scale
free networks.Under it's influece popular items get more popular thereby
resulting in long tailed distribution problem. Consequently, new items which
can be popular (potential ones), are suppressed by the already popular items.
This paper proposes a novel model which is able to identify potential items. It
identifies the potentially popular items by considering the number of links or
ratings it has recieved in recent past along with it's popularity decay. For
obtaining an effecient model we consider only temporal features of the content,
avoiding the cost of extracting other features. We have found that people
follow recent behaviours of their peers. In presence of fit or quality items
already popular items lose it's popularity. Prediction accuracy is measured on
three industrial datasets namely Movielens, Netflix and Facebook wall post.
Experimental results show that compare to state-of-the-art model our model have
better prediction accuracy.Comment: 7 pages in ACM style.7 figures and 1 tabl
Paraphrase Generation with Deep Reinforcement Learning
Automatic generation of paraphrases from a given sentence is an important yet
challenging task in natural language processing (NLP), and plays a key role in
a number of applications such as question answering, search, and dialogue. In
this paper, we present a deep reinforcement learning approach to paraphrase
generation. Specifically, we propose a new framework for the task, which
consists of a \textit{generator} and an \textit{evaluator}, both of which are
learned from data. The generator, built as a sequence-to-sequence learning
model, can produce paraphrases given a sentence. The evaluator, constructed as
a deep matching model, can judge whether two sentences are paraphrases of each
other. The generator is first trained by deep learning and then further
fine-tuned by reinforcement learning in which the reward is given by the
evaluator. For the learning of the evaluator, we propose two methods based on
supervised learning and inverse reinforcement learning respectively, depending
on the type of available training data. Empirical study shows that the learned
evaluator can guide the generator to produce more accurate paraphrases.
Experimental results demonstrate the proposed models (the generators)
outperform the state-of-the-art methods in paraphrase generation in both
automatic evaluation and human evaluation.Comment: EMNLP 201
Topological gauge theory, symmetry fractionalization, and classification of symmetry-enriched topological phases in three dimensions
Symmetry plays a crucial role in enriching topological phases of matter.
Phases with intrinsic topological order that are symmetric are called
symmetry-enriched topological phases (SET). In this paper, we focus on SETs in
three spatial dimensions, where the intrinsic topological orders are described
by Abelian gauge theory and the symmetry groups are also Abelian. As a series
work of our previous research [Phys. Rev. B 94, 245120 (2016);
(arXiv:1609.00985)], we study these topological phases described by twisted
gauge theories with global symmetry and consider all possible topologically
inequivalent "charge matrices". Within each equivalence class, there is a
unique pattern of symmetry fractionalization on both point-like and string-like
topological excitations. In this way, we classify Abelian topological order
enriched by Abelian symmetry within our field-theoretic approach. To
illustrate, we concretely calculate many representative examples of SETs and
discuss future directions
Neural Generative Question Answering
This paper presents an end-to-end neural network model, named Neural
Generative Question Answering (GENQA), that can generate answers to simple
factoid questions, based on the facts in a knowledge-base. More specifically,
the model is built on the encoder-decoder framework for sequence-to-sequence
learning, while equipped with the ability to enquire the knowledge-base, and is
trained on a corpus of question-answer pairs, with their associated triples in
the knowledge-base. Empirical study shows the proposed model can effectively
deal with the variations of questions and answers, and generate right and
natural answers by referring to the facts in the knowledge-base. The experiment
on question answering demonstrates that the proposed model can outperform an
embedding-based QA model as well as a neural dialogue model trained on the same
data.Comment: Accepted by IJCAI 201
Retraction notice to ‘The Building and Optimization of Quantum Database’ Physics Procedia, Volume 25, 2012, Pages 1602–1609
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