Search Results for author:
Found 16 papers, 9 papers with code
Domain Adaptation with Category Attention Network for Deep Sentiment Analysis
Domain adaptation tasks such as cross-domain sentiment classification aim to utilize existing labeled data in the source domain and unlabeled or few labeled data in the target domain to improve the performance in the target domain via reducing the shift between the data distributions.
Open Hierarchical Relation Extraction
To establish the bidirectional connections between OpenRE and relation hierarchy, we propose the task of open hierarchical relation extraction and present a novel OHRE framework for the task.
Meta-Information Guided Meta-Learning for Few-Shot Relation Classification
Few-shot classification requires classifiers to adapt to new classes with only a few training instances.
Denoising Relation Extraction from Document-level Distant Supervision
Distant supervision (DS) has been widely used to generate auto-labeled data for sentence-level relation extraction (RE), which improves RE performance.
Knowledge Transfer via Pre-training for Recommendation: A Review and Prospect
Recommender systems aim to provide item recommendations for users, and are usually faced with data sparsity problem (e. g., cold start) in real-world scenarios.
Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics
Process optimization of photovoltaic devices is a time-intensive, trial-and-error endeavor, which lacks full transparency of the underlying physics and relies on user-imposed constraints that may or may not lead to a global optimum.
FAQ-based Question Answering via Knowledge Anchors
In this paper, we propose a novel Knowledge Anchor based Question Answering (KAQA) framework for FAQ-based QA to better understand questions and retrieve more appropriate answers.
Open Relation Extraction: Relational Knowledge Transfer from Supervised Data to Unsupervised Data
Open relation extraction (OpenRE) aims to extract relational facts from the open-domain corpus.
Neural Snowball for Few-Shot Relation Learning
To address new relations with few-shot instances, we propose a novel bootstrapping approach, Neural Snowball, to learn new relations by transferring semantic knowledge about existing relations.
Atom Responding Machine for Dialog Generation
To enrich the generated responses, ARM introduces a large number of molecule-mechanisms as various responding styles, which are conducted by taking different combinations from a few atom-mechanisms.
Language Modeling with Sparse Product of Sememe Experts
Most language modeling methods rely on large-scale data to statistically learn the sequential patterns of words.
Knowledge Graph Embedding with Hierarchical Relation Structure
To this end, in this paper, we extend existing KGE models TransE, TransH and DistMult, to learn knowledge representations by leveraging the information from the HRS.
Hierarchical Neural Network for Extracting Knowledgeable Snippets and Documents
Additionally, a "low-level sharing, high-level splitting" structure of CNN is designed to handle the documents from different content domains.
Incorporating Chinese Characters of Words for Lexical Sememe Prediction
However, existing methods of lexical sememe prediction typically rely on the external context of words to represent the meaning, which usually fails to deal with low-frequency and out-of-vocabulary words.
Does William Shakespeare REALLY Write Hamlet? Knowledge Representation Learning with Confidence
Experimental results demonstrate that our confidence-aware models achieve significant and consistent improvements on all tasks, which confirms the capability of CKRL modeling confidence with structural information in both KG noise detection and knowledge representation learning.
Tree-Structured Neural Machine for Linguistics-Aware Sentence Generation
Then, with a proposed tree-structured search method, the model is able to generate the most probable responses in the form of dependency trees, which are finally flattened into sequences as the system output.
