2,592 research outputs found
Knowledge-Enhanced Personalized Review Generation with Capsule Graph Neural Network
Personalized review generation (PRG) aims to automatically produce review
text reflecting user preference, which is a challenging natural language
generation task. Most of previous studies do not explicitly model factual
description of products, tending to generate uninformative content. Moreover,
they mainly focus on word-level generation, but cannot accurately reflect more
abstractive user preference in multiple aspects. To address the above issues,
we propose a novel knowledge-enhanced PRG model based on capsule graph neural
network~(Caps-GNN). We first construct a heterogeneous knowledge graph (HKG)
for utilizing rich item attributes. We adopt Caps-GNN to learn graph capsules
for encoding underlying characteristics from the HKG. Our generation process
contains two major steps, namely aspect sequence generation and sentence
generation. First, based on graph capsules, we adaptively learn aspect capsules
for inferring the aspect sequence. Then, conditioned on the inferred aspect
label, we design a graph-based copy mechanism to generate sentences by
incorporating related entities or words from HKG. To our knowledge, we are the
first to utilize knowledge graph for the PRG task. The incorporated KG
information is able to enhance user preference at both aspect and word levels.
Extensive experiments on three real-world datasets have demonstrated the
effectiveness of our model on the PRG task.Comment: Accepted by CIKM 2020 (Long Paper
Multi-Zone Unit for Recurrent Neural Networks
Recurrent neural networks (RNNs) have been widely used to deal with sequence
learning problems. The input-dependent transition function, which folds new
observations into hidden states to sequentially construct fixed-length
representations of arbitrary-length sequences, plays a critical role in RNNs.
Based on single space composition, transition functions in existing RNNs often
have difficulty in capturing complicated long-range dependencies. In this
paper, we introduce a new Multi-zone Unit (MZU) for RNNs. The key idea is to
design a transition function that is capable of modeling multiple space
composition. The MZU consists of three components: zone generation, zone
composition, and zone aggregation. Experimental results on multiple datasets of
the character-level language modeling task and the aspect-based sentiment
analysis task demonstrate the superiority of the MZU.Comment: Accepted at AAAI 202
Presenting an approach based on weighted CapsuleNet networks for Arabic and Persian multi-domain sentiment analysis
Sentiment classification is a fundamental task in natural language
processing, assigning one of the three classes, positive, negative, or neutral,
to free texts. However, sentiment classification models are highly domain
dependent; the classifier may perform classification with reasonable accuracy
in one domain but not in another due to the Semantic multiplicity of words
getting poor accuracy. This article presents a new Persian/Arabic multi-domain
sentiment analysis method using the cumulative weighted capsule networks
approach. Weighted capsule ensemble consists of training separate capsule
networks for each domain and a weighting measure called domain belonging degree
(DBD). This criterion consists of TF and IDF, which calculates the dependency
of each document for each domain separately; this value is multiplied by the
possible output that each capsule creates. In the end, the sum of these
multiplications is the title of the final output, and is used to determine the
polarity. And the most dependent domain is considered the final output for each
domain. The proposed method was evaluated using the Digikala dataset and
obtained acceptable accuracy compared to the existing approaches. It achieved
an accuracy of 0.89 on detecting the domain of belonging and 0.99 on detecting
the polarity. Also, for the problem of dealing with unbalanced classes, a
cost-sensitive function was used. This function was able to achieve 0.0162
improvements in accuracy for sentiment classification. This approach on Amazon
Arabic data can achieve 0.9695 accuracies in domain classification
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