Search Results for author:
Found 13 papers, 5 papers with code
CIST@CL-SciSumm 2020, LongSumm 2020: Automatic Scientific Document Summarization
In the LongSumm shared task, we integrate both the extractive and abstractive summarization ways.
Extractive Financial Narrative Summarisation based on DPPs
We participate in the FNS-Summarisation 2020 shared task to be held at FNP 2020 workshop at COLING 2020.
Enhancing Clinical Predictive Modeling through Model Complexity-Driven Class Proportion Tuning for Class Imbalanced Data: An Empirical Study on Opioid Overdose Prediction
Class imbalance problems widely exist in the medical field and heavily deteriorates performance of clinical predictive models.
Reveal the Unknown: Out-of-Knowledge-Base Mention Discovery with Entity Linking
We propose BLINKout, a new BERT-based Entity Linking (EL) method which can identify mentions that do not have corresponding KB entities by matching them to a special NIL entity.
Joint Open Knowledge Base Canonicalization and Linking
However, noun phrases (NPs) and relation phrases (RPs) in OKBs are not canonicalized and often appear in different paraphrased textual variants, which leads to redundant and ambiguous facts.
Low-resource Personal Attribute Prediction from Conversation
Previous studies often rely on a relative number of resources such as labeled utterances and external data, yet the attribute knowledge embedded in unlabeled utterances is underutilized and their performance of predicting some difficult personal attributes is still unsatisfactory.
Personal Attribute Prediction from Conversations
Personal knowledge bases (PKBs) are critical to many applications, such as Web-based chatbots and personalized recommendation.
Multi-View Clustering for Open Knowledge Base Canonicalization
In this paper, we propose CMVC, a novel unsupervised framework that leverages these two views of knowledge jointly for canonicalizing OKBs without the need of manually annotated labels.
Entity Linking Meets Deep Learning: Techniques and Solutions
Entity linking (EL) is the process of linking entity mentions appearing in web text with their corresponding entities in a knowledge base.
Subjective Bias in Abstractive Summarization
Due to the subjectivity of the summarization, it is a good practice to have more than one gold summary for each training document.
On Extending NLP Techniques from the Categorical to the Latent Space: KL Divergence, Zipf's Law, and Similarity Search
Despite the recent successes of deep learning in natural language processing (NLP), there remains widespread usage of and demand for techniques that do not rely on machine learning.
Hyperspectral Image Classification Based on Adaptive Sparse Deep Network
Sparse model is widely used in hyperspectral image classification. However, different of sparsity and regularization parameters has great influence on the classification results. In this paper, a novel adaptive sparse deep network based on deep architecture is proposed, which can construct the optimal sparse representation and regularization parameters by deep network. Firstly, a data flow graph is designed to represent each update iteration based on Alternating Direction Method of Multipliers (ADMM) algorithm. Forward network and Back-Propagation network are deduced. All parameters are updated by gradient descent in Back-Propagation. Then we proposed an Adaptive Sparse Deep Network. Comparing with several traditional classifiers or other algorithm for sparse model, experiment results indicate that our method achieves great improvement in HSI classification.
Multi-lingual Wikipedia Summarization and Title Generation On Low Resource Corpus
MultiLing 2019 Headline Generation Task on Wikipedia Corpus raised a critical and practical problem: multilingual task on low resource corpus.
