3 research outputs found
Community Detection and Growth Potential Prediction Using the Stochastic Block Model and the Long Short-term Memory from Patent Citation Networks
Scoring patent documents is very useful for technology management. However,
conventional methods are based on static models and, thus, do not reflect the
growth potential of the technology cluster of the patent. Because even if the
cluster of a patent has no hope of growing, we recognize the patent is
important if PageRank or other ranking score is high. Therefore, there arises a
necessity of developing citation network clustering and prediction of future
citations. In our research, clustering of patent citation networks by
Stochastic Block Model was done with the aim of enabling corporate managers and
investors to evaluate the scale and life cycle of technology. As a result, we
confirmed nested SBM is appropriate for graph clustering of patent citation
networks. Also, a high MAPE value was obtained and the direction accuracy
achieved a value greater than 50% when predicting growth potential for each
cluster by using LSTM.Comment: arXiv admin note: substantial text overlap with arXiv:1904.1204
Community Detection and Growth Potential Prediction from Patent Citation Networks
The scoring of patents is useful for technology management analysis.
Therefore, a necessity of developing citation network clustering and prediction
of future citations for practical patent scoring arises. In this paper, we
propose a community detection method using the Node2vec. And in order to
analyze growth potential we compare three ''time series analysis methods'', the
Long Short-Term Memory (LSTM), ARIMA model, and Hawkes Process. The results of
our experiments, we could find common technical points from those clusters by
Node2vec. Furthermore, we found that the prediction accuracy of the ARIMA model
was higher than that of other models.Comment: arXiv admin note: text overlap with arXiv:1607.00653 by other author
Why Guided Dialog Policy Learning performs well? Understanding the role of adversarial learning and its alternative
Dialog policies, which determine a system's action based on the current state
at each dialog turn, are crucial to the success of the dialog. In recent years,
reinforcement learning (RL) has emerged as a promising option for dialog policy
learning (DPL). In RL-based DPL, dialog policies are updated according to
rewards. The manual construction of fine-grained rewards, such as
state-action-based ones, to effectively guide the dialog policy is challenging
in multi-domain task-oriented dialog scenarios with numerous state-action pair
combinations. One way to estimate rewards from collected data is to train the
reward estimator and dialog policy simultaneously using adversarial learning
(AL). Although this method has demonstrated superior performance
experimentally, it is fraught with the inherent problems of AL, such as mode
collapse. This paper first identifies the role of AL in DPL through detailed
analyses of the objective functions of dialog policy and reward estimator.
Next, based on these analyses, we propose a method that eliminates AL from
reward estimation and DPL while retaining its advantages. We evaluate our
method using MultiWOZ, a multi-domain task-oriented dialog corpus