WGE also constructs another single undirected graph from relation-focused constraints, which views entities and relations as nodes.
Identifying vulnerabilities in the source code is essential to protect the software systems from cyber security attacks.
Automatic post-editing (APE) is an important remedy for reducing errors of raw translated texts that are produced by machine translation (MT) systems or software-aided translation.
We introduce a novel embedding model, named NoGE, which aims to integrate co-occurrence among entities and relations into graph neural networks to improve knowledge graph completion (i. e., link prediction).
We propose a simple yet effective embedding model to learn quaternion embeddings for entities and relations in knowledge graphs.
As demonstrated, the Quaternion space, a hyper-complex vector space, provides highly meaningful computations and analogical calculus through Hamilton product compared to the Euclidean and complex vector spaces.
Despite several signs of progress have been made recently, limited research has been conducted for an inductive setting where embeddings are required for newly unseen nodes -- a setting encountered commonly in practical applications of deep learning for graph networks.
Question answering systems aim to produce exact answers to users' questions instead of a list of related documents as used by current search engines.
This paper introduces a Vietnamese text-based conversational agent architecture on specific knowledge domain which is integrated in a question answering system.
In this paper, we focus on learning low-dimensional embeddings for nodes in graph-structured data.
Ranked #54 on Node Classification on Pubmed
The transformer self-attention network has been extensively used in research domains such as computer vision, image processing, and natural language processing.
Ranked #1 on Graph Classification on PTC
Thus, U2GAN can address the weaknesses in the existing models in order to produce plausible node embeddings whose sum is the final embedding of the whole graph.
Knowledge graph embedding methods often suffer from a limitation of memorizing valid triples to predict new ones for triple classification and search personalization problems.
In this paper, we introduce an embedding model, named CapsE, exploring a capsule network to model relationship triples (subject, relation, object).
Ranked #39 on Link Prediction on WN18RR
After that, the 3-column matrix is fed into a deep learning architecture to re-rank the search results returned by a basis ranker.
This paper describes our NIHRIO system for SemEval-2018 Task 3 "Irony detection in English tweets".
We present an easy-to-use and fast toolkit, namely VnCoreNLP---a Java NLP annotation pipeline for Vietnamese.
This 3-column matrix is then fed to a convolution layer where multiple filters are operated on the matrix to generate different feature maps.
Ranked #57 on Link Prediction on WN18RR
This paper presents an empirical comparison of two strategies for Vietnamese Part-of-Speech (POS) tagging from unsegmented text: (i) a pipeline strategy where we consider the output of a word segmenter as the input of a POS tagger, and (ii) a joint strategy where we predict a combined segmentation and POS tag for each syllable.
Our model generalizes the previous works in that it allows to induce different weights of different senses of a word.
In this paper, we propose a new approach to construct a system of transformation rules for the Part-of-Speech (POS) tagging task.