Search Results for author:
Found 11 papers, 7 papers with code
Decoupling Mixture-of-Graphs: Unseen Relational Learning for Knowledge Graph Completion by Fusing Ontology and Textual Experts
In fact, the semantics of a relation can be expressed by three kinds of graphs: factual graph, ontology graph, textual description graph, and they can complement each other.
CMQA: A Dataset of Conditional Question Answering with Multiple-Span Answers
To bridge the gap, we propose a new task: conditional question answering with hierarchical multi-span answers, where both the hierarchical relations and the conditions need to be extracted.
Cross-Lingual Contrastive Learning for Fine-Grained Entity Typing for Low-Resource Languages
In this paper, we propose a cross-lingual contrastive learning framework to learn FGET models for low-resource languages.
KLMo: Knowledge Graph Enhanced Pretrained Language Model with Fine-Grained Relationships
In this work, we propose to incorporate KG (including both entities and relations) into the language learning process to obtain KG-enhanced pretrained Language Model, namely KLMo.
TexSmart: A System for Enhanced Natural Language Understanding
This paper introduces TexSmart, a text understanding system that supports fine-grained named entity recognition (NER) and enhanced semantic analysis functionalities.
TexSmart: A Text Understanding System for Fine-Grained NER and Enhanced Semantic Analysis
This technique report introduces TexSmart, a text understanding system that supports fine-grained named entity recognition (NER) and enhanced semantic analysis functionalities.
Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme
Joint extraction of entities and relations is an important task in information extraction.
Ranked #3 on
Relation Extraction
on NYT-single
Self-Taught Convolutional Neural Networks for Short Text Clustering
Short text clustering is a challenging problem due to its sparseness of text representation.
Ranked #2 on
Short Text Clustering
on Stackoverflow
Combining Lexical and Semantic-based Features for Answer Sentence Selection
As the result of the evaluation shows, our solution provides a valuable and brief model which could be used in modelling question answering or sentence semantic relevance.
Text Classification Improved by Integrating Bidirectional LSTM with Two-dimensional Max Pooling
To integrate the features on both dimensions of the matrix, this paper explores applying 2D max pooling operation to obtain a fixed-length representation of the text.
Ranked #5 on
Text Classification
on TREC-6
Hierarchical Memory Networks for Answer Selection on Unknown Words
Recently, end-to-end memory networks have shown promising results on Question Answering task, which encode the past facts into an explicit memory and perform reasoning ability by making multiple computational steps on the memory.
