25 Result(s)
within
YuJie Zhang
Computer Science
Natural Language Processing (NLP)
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Chapter and Conference Paper
Improving Entity Linking by Encoding Type Information into Entity Embeddings
Entity Linking (EL) refers to the task of linking entity mentions in the text to the correct entities in the Knowledge Base (KB) in which entity embeddings play a vital and challenging role because of the subt...
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Chapter and Conference Paper
LRRA:A Transparent Neural-Symbolic Reasoning Framework for Real-World Visual Question Answering
The predominant approach of visual question answering (VQA) relies on encoding the image and question with a “black box" neural encoder and decoding a single token into answers such as “yes” or “no”. Despite t...
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Chapter and Conference Paper
Improving Low-Resource Named Entity Recognition via Label-Aware Data Augmentation and Curriculum Denoising
Deep neural networks have achieved state-of-the-art performances on named entity recognition (NER) with sufficient training data, while they perform poorly in low-resource scenarios due to data scarcity. To so...
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Chapter and Conference Paper
Explore Coarse-Grained Structures for Syntactically Controllable Paraphrase Generation
Syntactically controlled paraphrase generation can produce diverse paraphrases by exposing syntactic control, where both semantic preservation and syntactic variations are two important factors. Previous works...
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Chapter and Conference Paper
A Joint Model for Graph-Based Chinese Dependency Parsing
In Chinese dependency parsing, the joint model of word segmentation, POS tagging and dependency parsing has become the mainstream framework because it can eliminate error propagation and share knowledge, where...
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Article
Context-aware user preferences prediction on location-based social networks
Recently, the increasing number of mobile users in location-based social networks (LBSNs) has generated large amounts of data, which provides unprecedented opportunities to study mobile user preferences for lo...
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Chapter and Conference Paper
Improving Performance of NMT Using Semantic Concept of WordNet Synset
Neural machine translation (NMT) has shown promising progress in recent years. However, for reducing the computational complexity, NMT typically needs to limit its vocabulary scale to a fixed or relatively acc...
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Chapter and Conference Paper
Attention-Based Gated Convolutional Neural Networks for Distant Supervised Relation Extraction
Distant supervision is an effective method to generate large-scale labeled data for relation extraction without expensive manual annotation, but it inevitably suffers from the wrong labeling problem, which wou...
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Chapter and Conference Paper
Syntax-Aware Attention for Natural Language Inference with Phrase-Level Matching
Natural language inference (NLI) aims to predict whether a premise sentence can infer another hypothesis sentence. Models based on tree structures have shown promising results on this task, but the performance...
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Chapter and Conference Paper
A Top-Down Model for Character-Level Chinese Dependency Parsing
This paper proposes a novel transition-based algorithm for character-level Chinese dependency parsing that straightforwardly models the dependency tree in a top-down manner. Based on the stack-pointer parser, ...
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Chapter and Conference Paper
A Semantic Concept Based Unknown Words Processing Method in Neural Machine Translation
The problem of unknown words in neural machine translation (NMT), which not only affects the semantic integrity of the source sentences but also adversely affects the generating of the target sentences. The tr...
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Chapter and Conference Paper
Improved Character-Based Chinese Dependency Parsing by Using Stack-Tree LSTM
Almost all the state-of-the-art methods for Character-based Chinese dependency parsing ignore the complete dependency subtree information built during the parsing process, which is crucial for parsing the rest...
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Chapter and Conference Paper
Attention-Based Convolutional Neural Networks for Chinese Relation Extraction
Relation extraction is an important part of many information extraction systems that mines structured facts from texts. Recently, deep learning has achieved good results in relation extraction. Attention mecha...
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Chapter and Conference Paper
Addressing Domain Adaptation for Chinese Word Segmentation with Instances-Based Transfer Learning
Recent studies have shown effectiveness in using neural networks for Chinese Word Segmentation (CWS). However, these models, constrained by the domain and size of the training corpus, do not work well in domai...
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Chapter and Conference Paper
A Method of Unknown Words Processing for Neural Machine Translation Using HowNet
An inherent weakness of neural machine translation (NMT) systems is their inability to correctly translate unknown words. Traditional unknown words processing methods are usually based on word vectors trained ...
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Chapter and Conference Paper
Iterative Integration of Unsupervised Features for Chinese Dependency Parsing
Since Chinese dependency parsing is lack of a large amount of manually annotated dependency treebank. Some unsupervised methods of using large-scale unannotated data are proposed and inevitably introduce too m...
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Chapter and Conference Paper
Chinese Paraphrases Acquisition Based on Random Walk N Step
Conventional “pivot-based” approach of acquiring paraphrasing from bilingual corpus has limitations, where only paraphrases within two steps were considered. We propose a graph based model of acquiring paraphr...
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Chapter and Conference Paper
Chunk-Based Dependency-to-String Model with Japanese Case Frame
This paper proposes an idea to integrate Japanese case frame into chunk-based dependency-to-string model. At first, case frames are acquired from Japanese chunk-based dependency analysis results. Then case fra...
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Chapter and Conference Paper
Case Frame Constraints for Hierarchical Phrase-Based Translation: Japanese-Chinese as an Example
Hierarchical phrase-based model has two main problems. Firstly, without any semantic guidance, large numbers of redundant rules are extracted. Secondly, it cannot efficiently capture long reordering. This pape...
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Chapter and Conference Paper
Exploring Multiple Chinese Word Segmentation Results Based on Linear Model
In the process of develo** a domain-specific Chinese-English machine translation system, the accuracy of Chinese word segmentation on large amounts of training text often decreases because of unknown words. ...
