Search Results for author:
Found 15 papers, 6 papers with code
Relation Extraction with Word Graphs from N-grams
Most recent studies for relation extraction (RE) leverage the dependency tree of the input sentence to incorporate syntax-driven contextual information to improve model performance, with little attention paid to the limitation where high-quality dependency parsers in most cases unavailable, especially for in-domain scenarios.
Improving Federated Learning for Aspect-based Sentiment Analysis via Topic Memories
Aspect-based sentiment analysis (ABSA) predicts the sentiment polarity towards a particular aspect term in a sentence, which is an important task in real-world applications.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
Syntax-driven Approach for Semantic Role Labeling
To achieve a better performance in SRL, a model is always required to have a good understanding of the context information.
Ranked #2 on
Semantic Role Labeling
on CoNLL 2005
Reinforced Cross-modal Alignment for Radiology Report Generation
In detail, a shared memory is used to record the mappings between visual and textual information, and the proposed reinforced algorithm is performed to learn the signal from the reports to guide the cross-modal alignment even though such reports are not directly related to how images and texts are mapped.
ChiMST: A Chinese Medical Corpus for Word Segmentation and Medical Term Recognition
Chinese word segmentation (CWS) and named entity recognition (NER) are two important tasks in Chinese natural language processing.
Enhancing Relation Extraction via Adversarial Multi-task Learning
Relation extraction (RE) is a sub-field of information extraction, which aims to extract the relation between two given named entities (NEs) in a sentence and thus requires a good understanding of contextual information, especially the entities and their surrounding texts.
Complementary Learning of Aspect Terms for Aspect-based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) aims to predict the sentiment polarity towards a given aspect term in a sentence on the fine-grained level, which usually requires a good understanding of contextual information, especially appropriately distinguishing of a given aspect and its contexts, to achieve good performance.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
Use Image Clustering to Facilitate Technology Assisted Review
During the past decade breakthroughs in GPU hardware and deep neural networks technologies have revolutionized the field of computer vision, making image analytical potentials accessible to a range of real-world applications.
Improving Arabic Diacritization with Regularized Decoding and Adversarial Training
Arabic diacritization is a fundamental task for Arabic language processing.
Application of Deep Learning in Recognizing Bates Numbers and Confidentiality Stamping from Images
In eDiscovery, it is critical to ensure that each page produced in legal proceedings conforms with the requirements of court or government agency production requests.
Empirical Comparisons of CNN with Other Learning Algorithms for Text Classification in Legal Document Review
For each data set, classification models were trained with different training sample sizes using different learning algorithms.
Image Analytics for Legal Document Review: A Transfer Learning Approach
In this paper, we present several applications of deep learning in computer vision to Technology Assisted Review of image data in legal industry.
Using Google Analytics to Support Cybersecurity Forensics
Web traffic is a valuable data source, typically used in the marketing space to track brand awareness and advertising effectiveness.
Empirical Study of Deep Learning for Text Classification in Legal Document Review
Predictive coding has been widely used in legal matters to find relevant or privileged documents in large sets of electronically stored information.
