Search Results for author:
Found 67 papers, 17 papers with code
An Effective and Efficient Entity Alignment Decoding Algorithm via Third-Order Tensor Isomorphism
Entity alignment (EA) aims to discover the equivalent entity pairs between KGs, which is a crucial step for integrating multi-source KGs. For a long time, most researchers have regarded EA as a pure graph representation learning task and focused on improving graph encoders while paying little attention to the decoding process. In this paper, we propose an effective and efficient EA Decoding Algorithm via Third-order Tensor Isomorphism (DATTI). Specifically, we derive two sets of isomorphism equations: (1) Adjacency tensor isomorphism equations and (2) Gramian tensor isomorphism equations. By combining these equations, DATTI could effectively utilize the adjacency and inner correlation isomorphisms of KGs to enhance the decoding process of EA. Extensive experiments on public datasets indicate that our decoding algorithm can deliver significant performance improvements even on the most advanced EA methods, while the extra required time is less than 3 seconds.
Understanding Gender Bias in Knowledge Base Embeddings
To exemplify the potential applications of our study, we also present two strategies (by adding and removing KB triples) to mitigate gender biases in KB embeddings.
LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Models
This paper presents a comprehensive survey of the current status and opportunities for Large Language Models (LLMs) in strategic reasoning, a sophisticated form of reasoning that necessitates understanding and predicting adversary actions in multi-agent settings while adjusting strategies accordingly.
Temporal Knowledge Graph Completion with Time-sensitive Relations in Hypercomplex Space
Temporal knowledge graph completion (TKGC) aims to fill in missing facts within a given temporal knowledge graph at a specific time.
Knowledge Graph Completion
Temporal Knowledge Graph Completion
A Survey on Temporal Knowledge Graph: Representation Learning and Applications
Knowledge graph representation learning aims to learn low-dimensional vector embeddings for entities and relations in a knowledge graph.
K-Level Reasoning with Large Language Models
While Large Language Models (LLMs) have demonstrated their proficiency in complex reasoning tasks, their performance in dynamic, interactive, and competitive scenarios - such as business strategy and stock market analysis - remains underexplored.
BIBench: Benchmarking Data Analysis Knowledge of Large Language Models
To bridge this gap, we introduce BIBench, a comprehensive benchmark designed to evaluate the data analysis capabilities of LLMs within the context of Business Intelligence (BI).
An Effective and Efficient Time-aware Entity Alignment Framework via Two-aspect Three-view Label Propagation
Entity alignment (EA) aims to find the equivalent entity pairs between different knowledge graphs (KGs), which is crucial to promote knowledge fusion.
LightEA: A Scalable, Robust, and Interpretable Entity Alignment Framework via Three-view Label Propagation
Entity Alignment (EA) aims to find equivalent entity pairs between KGs, which is the core step of bridging and integrating multi-source KGs.
Ranked #1 on
Entity Alignment
on DBP1M DE-EN
Prompt-based Connective Prediction Method for Fine-grained Implicit Discourse Relation Recognition
Due to the absence of connectives, implicit discourse relation recognition (IDRR) is still a challenging and crucial task in discourse analysis.
A Simple Temporal Information Matching Mechanism for Entity Alignment Between Temporal Knowledge Graphs
However, we believe that it is not necessary to learn the embeddings of temporal information in KGs since most TKGs have uniform temporal representations.
Few Clean Instances Help Denoising Distant Supervision
Existing distantly supervised relation extractors usually rely on noisy data for both model training and evaluation, which may lead to garbage-in-garbage-out systems.
A Dual-Attention Neural Network for Pun Location and Using Pun-Gloss Pairs for Interpretation
Pun location is to identify the punning word (usually a word or a phrase that makes the text ambiguous) in a given short text, and pun interpretation is to find out two different meanings of the punning word.
From Alignment to Assignment: Frustratingly Simple Unsupervised Entity Alignment
Cross-lingual entity alignment (EA) aims to find the equivalent entities between crosslingual KGs, which is a crucial step for integrating KGs.
Ranked #4 on
Entity Alignment
on dbp15k fr-en
(using extra training data)
Are Negative Samples Necessary in Entity Alignment? An Approach with High Performance, Scalability and Robustness
Entity alignment (EA) aims to find the equivalent entities in different KGs, which is a crucial step in integrating multiple KGs.
Ranked #6 on
Entity Alignment
on dbp15k ja-en
(using extra training data)
Boosting the Speed of Entity Alignment 10*: Dual Attention Matching Network with Normalized Hard Sample Mining
Seeking the equivalent entities among multi-source Knowledge Graphs (KGs) is the pivotal step to KGs integration, also known as \emph{entity alignment} (EA).
Ranked #2 on
Entity Alignment
on YAGO-WIKI50K
Generating CCG Categories
Previous CCG supertaggers usually predict categories using multi-class classification.
In-Order Chart-Based Constituent Parsing
We propose a novel in-order chart-based model for constituent parsing.
ECNU at SemEval-2020 Task 7: Assessing Humor in Edited News Headlines Using BiLSTM with Attention
In this paper we describe our system submitted to SemEval 2020 Task 7: {``}Assessing Humor in Edited News Headlines{''}.
Relational Reflection Entity Alignment
Entity alignment aims to identify equivalent entity pairs from different Knowledge Graphs (KGs), which is essential in integrating multi-source KGs.
Ranked #3 on
Entity Alignment
on DICEWS-1K
A Span-based Linearization for Constituent Trees
We propose a novel linearization of a constituent tree, together with a new locally normalized model.
MRAEA: An Efficient and Robust Entity Alignment Approach for Cross-lingual Knowledge Graph
To tackle these challenges, we propose a novel Meta Relation Aware Entity Alignment (MRAEA) to directly model cross-lingual entity embeddings by attending over the node's incoming and outgoing neighbors and its connected relations' meta semantics.
Ranked #17 on
Entity Alignment
on DBP15k zh-en
Exploring Human Gender Stereotypes with Word Association Test
Specifically, we use random walk on word association graph to derive bias scores for a large amount of words.
Graph-based Dependency Parsing with Graph Neural Networks
We investigate the problem of efficiently incorporating high-order features into neural graph-based dependency parsing.
Ranked #12 on
Dependency Parsing
on Penn Treebank
Joint Type Inference on Entities and Relations via Graph Convolutional Networks
We develop a new paradigm for the task of joint entity relation extraction.
Ranked #1 on
Relation Extraction
on ACE 2005
(Sentence Encoder metric)
Scaling up Open Tagging from Tens to Thousands: Comprehension Empowered Attribute Value Extraction from Product Title
Supplementing product information by extracting attribute values from title is a crucial task in e-Commerce domain.
Towards a One-stop Solution to Both Aspect Extraction and Sentiment Analysis Tasks with Neural Multi-task Learning
Previous studies usually divided aspect-based sentiment analysis into several subtasks in pipeline, i. e., first aspect term and/or opinion term extraction, then aspect-based sentiment prediction, resulting in error propagation and external resources dependency.
AntNLP at CoNLL 2018 Shared Task: A Graph-Based Parser for Universal Dependency Parsing
We describe the graph-based dependency parser in our system (AntNLP) submitted to the CoNLL 2018 UD Shared Task.
Extracting Entities and Relations with Joint Minimum Risk Training
We investigate the task of joint entity relation extraction.
Ranked #1 on
Relation Extraction
on ACE 2005
(Sentence Encoder metric)
ECNU at SemEval-2018 Task 1: Emotion Intensity Prediction Using Effective Features and Machine Learning Models
This paper describes our submissions to SemEval 2018 task 1.
ECNU at SemEval-2018 Task 2: Leverage Traditional NLP Features and Neural Networks Methods to Address Twitter Emoji Prediction Task
This paper describes our submissions to Task 2 in SemEval 2018, i. e., Multilingual Emoji Prediction.
ECNU at SemEval-2018 Task 11: Using Deep Learning Method to Address Machine Comprehension Task
This paper describes the system we submitted to the Task 11 in SemEval 2018, i. e., Machine Comprehension using Commonsense Knowledge.
ECNU at SemEval-2018 Task 10: Evaluating Simple but Effective Features on Machine Learning Methods for Semantic Difference Detection
This paper describes the system we submitted to Task 10 (Capturing Discriminative Attributes) in SemEval 2018.
ECNU at SemEval-2018 Task 3: Exploration on Irony Detection from Tweets via Machine Learning and Deep Learning Methods
The paper describes our submissions to task 3 in SemEval-2018.
ECNU at SemEval-2018 Task 12: An End-to-End Attention-based Neural Network for the Argument Reasoning Comprehension Task
This paper presents our submissions to SemEval 2018 Task 12: the Argument Reasoning Comprehension Task.
A Multi-task Learning Approach for Improving Product Title Compression with User Search Log Data
It is a challenging and practical research problem to obtain effective compression of lengthy product titles for E-commerce.
A Learning Error Analysis for Structured Prediction with Approximate Inference
In this work, we try to understand the differences between exact and approximate inference algorithms in structured prediction.
Multi-task Attention-based Neural Networks for Implicit Discourse Relationship Representation and Identification
We present a novel multi-task attention based neural network model to address implicit discourse relationship representation and identification through two types of representation learning, an attention based neural network for learning discourse relationship representation with two arguments and a multi-task framework for learning knowledge from annotated and unannotated corpora.
ECNU at SemEval-2017 Task 1: Leverage Kernel-based Traditional NLP features and Neural Networks to Build a Universal Model for Multilingual and Cross-lingual Semantic Textual Similarity
To address semantic similarity on multilingual and cross-lingual sentences, we firstly translate other foreign languages into English, and then feed our monolingual English system with various interactive features.
Cross-Lingual Semantic Textual Similarity
Machine Translation
+1
ECNU at SemEval-2017 Task 7: Using Supervised and Unsupervised Methods to Detect and Locate English Puns
This paper describes our submissions to task 7 in SemEval 2017, i. e., Detection and Interpretation of English Puns.
ECNU at SemEval-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for Twitter Message Polarity Classification
This paper reports our submission to subtask A of task 4 (Sentiment Analysis in Twitter, SAT) in SemEval 2017, i. e., Message Polarity Classification.
ECNU at SemEval-2017 Task 3: Using Traditional and Deep Learning Methods to Address Community Question Answering Task
This paper describes the systems we submitted to the task 3 (Community Question Answering) in SemEval 2017 which contains three subtasks on English corpora, i. e., subtask A: Question-Comment Similarity, subtask B: Question-Question Similarity, and subtask C: Question-External Comment Similarity.
ECNU at SemEval-2017 Task 8: Rumour Evaluation Using Effective Features and Supervised Ensemble Models
This paper describes our submissions to task 8 in SemEval 2017, i. e., Determining rumour veracity and support for rumours.
A Fast and Lightweight System for Multilingual Dependency Parsing
We present a multilingual dependency parser with a bidirectional-LSTM (BiLSTM) feature extractor and a multi-layer perceptron (MLP) classifier.
ECNU at SemEval-2017 Task 5: An Ensemble of Regression Algorithms with Effective Features for Fine-Grained Sentiment Analysis in Financial Domain
This paper describes our systems submitted to the Fine-Grained Sentiment Analysis on Financial Microblogs and News task (i. e., Task 5) in SemEval-2017.
Large-scale Opinion Relation Extraction with Distantly Supervised Neural Network
We investigate the task of open domain opinion relation extraction.
