2,223 research outputs found
CD-CNN: A Partially Supervised Cross-Domain Deep Learning Model for Urban Resident Recognition
Driven by the wave of urbanization in recent decades, the research topic
about migrant behavior analysis draws great attention from both academia and
the government. Nevertheless, subject to the cost of data collection and the
lack of modeling methods, most of existing studies use only questionnaire
surveys with sparse samples and non-individual level statistical data to
achieve coarse-grained studies of migrant behaviors. In this paper, a partially
supervised cross-domain deep learning model named CD-CNN is proposed for
migrant/native recognition using mobile phone signaling data as behavioral
features and questionnaire survey data as incomplete labels. Specifically,
CD-CNN features in decomposing the mobile data into location domain and
communication domain, and adopts a joint learning framework that combines two
convolutional neural networks with a feature balancing scheme. Moreover, CD-CNN
employs a three-step algorithm for training, in which the co-training step is
of great value to partially supervised cross-domain learning. Comparative
experiments on the city Wuxi demonstrate the high predictive power of CD-CNN.
Two interesting applications further highlight the ability of CD-CNN for
in-depth migrant behavioral analysis.Comment: 8 pages, 5 figures, conferenc
Denoising Distantly Supervised Named Entity Recognition via a Hypergeometric Probabilistic Model
Denoising is the essential step for distant supervision based named entity
recognition. Previous denoising methods are mostly based on instance-level
confidence statistics, which ignore the variety of the underlying noise
distribution on different datasets and entity types. This makes them difficult
to be adapted to high noise rate settings. In this paper, we propose
Hypergeometric Learning (HGL), a denoising algorithm for distantly supervised
NER that takes both noise distribution and instance-level confidence into
consideration. Specifically, during neural network training, we naturally model
the noise samples in each batch following a hypergeometric distribution
parameterized by the noise-rate. Then each instance in the batch is regarded as
either correct or noisy one according to its label confidence derived from
previous training step, as well as the noise distribution in this sampled
batch. Experiments show that HGL can effectively denoise the weakly-labeled
data retrieved from distant supervision, and therefore results in significant
improvements on the trained models.Comment: Accepted to AAAI202
Efficient Document-level Event Extraction via Pseudo-Trigger-aware Pruned Complete Graph
Most previous studies of document-level event extraction mainly focus on
building argument chains in an autoregressive way, which achieves a certain
success but is inefficient in both training and inference. In contrast to the
previous studies, we propose a fast and lightweight model named as PTPCG. In
our model, we design a novel strategy for event argument combination together
with a non-autoregressive decoding algorithm via pruned complete graphs, which
are constructed under the guidance of the automatically selected pseudo
triggers. Compared to the previous systems, our system achieves competitive
results with 19.8\% of parameters and much lower resource consumption, taking
only 3.8\% GPU hours for training and up to 8.5 times faster for inference.
Besides, our model shows superior compatibility for the datasets with (or
without) triggers and the pseudo triggers can be the supplements for annotated
triggers to make further improvements. Codes are available at
https://github.com/Spico197/DocEE .Comment: Accepted to IJCAI'202
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
Learning Profitable NFT Image Diffusions via Multiple Visual-Policy Guided Reinforcement Learning
We study the task of generating profitable Non-Fungible Token (NFT) images
from user-input texts. Recent advances in diffusion models have shown great
potential for image generation. However, existing works can fall short in
generating visually-pleasing and highly-profitable NFT images, mainly due to
the lack of 1) plentiful and fine-grained visual attribute prompts for an NFT
image, and 2) effective optimization metrics for generating high-quality NFT
images. To solve these challenges, we propose a Diffusion-based generation
framework with Multiple Visual-Policies as rewards (i.e., Diffusion-MVP) for
NFT images. The proposed framework consists of a large language model (LLM), a
diffusion-based image generator, and a series of visual rewards by design.
First, the LLM enhances a basic human input (such as "panda") by generating
more comprehensive NFT-style prompts that include specific visual attributes,
such as "panda with Ninja style and green background." Second, the
diffusion-based image generator is fine-tuned using a large-scale NFT dataset
to capture fine-grained image styles and accessory compositions of popular NFT
elements. Third, we further propose to utilize multiple visual-policies as
optimization goals, including visual rarity levels, visual aesthetic scores,
and CLIP-based text-image relevances. This design ensures that our proposed
Diffusion-MVP is capable of minting NFT images with high visual quality and
market value. To facilitate this research, we have collected the largest
publicly available NFT image dataset to date, consisting of 1.5 million
high-quality images with corresponding texts and market values. Extensive
experiments including objective evaluations and user studies demonstrate that
our framework can generate NFT images showing more visually engaging elements
and higher market value, compared with SOTA approaches
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