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Found 52 papers, 20 papers with code
Large-scale Opinion Relation Extraction with Distantly Supervised Neural Network
We investigate the task of open domain opinion relation extraction.
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 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.
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 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 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 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.
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.
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.
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.
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.
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 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 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.
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)
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.
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)
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
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.
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 laser-driven neutron sources for neutron capture cascades and the production of neutron-rich isotopes
The production of neutron-rich isotopes and the occurrence of neutron capture cascades via laser-driven (pulsed) neutron sources are investigated theoretically.
Nuclear Theory
A Span-based Linearization for Constituent Trees
We propose a novel linearization of a constituent tree, together with a new locally normalized model.
Visual Attack and Defense on Text
Modifying characters of a piece of text to their visual similar ones often ap-pear in spam in order to fool inspection systems and other conditions, which we regard as a kind of adversarial attack to neural models.
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
SentiX: A Sentiment-Aware Pre-Trained Model for Cross-Domain Sentiment Analysis
However, due to the variety of users{'} emotional expressions across domains, fine-tuning the pre-trained models on the source domain tends to overfit, leading to inferior results on the target domain.
In-Order Chart-Based Constituent Parsing
We propose a novel in-order chart-based model for constituent parsing.
Generating CCG Categories
Previous CCG supertaggers usually predict categories using multi-class classification.
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
Is ``hot pizza'' Positive or Negative? Mining Target-aware Sentiment Lexicons
Modelling a word{'}s polarity in different contexts is a key task in sentiment analysis.
ENPAR:Enhancing Entity and Entity Pair Representations for Joint Entity Relation Extraction
Current state-of-the-art systems for joint entity relation extraction (Luan et al., 2019; Wad-den et al., 2019) usually adopt the multi-task learning framework.
Probabilistic Graph Reasoning for Natural Proof Generation
In this paper, we propose PRobr, a novel approach for joint answer prediction and proof generation.
UniRE: A Unified Label Space for Entity Relation Extraction
Entities and relations are represented by squares and rectangles in the table.
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)
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)
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.
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.
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.
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
HIORE: Leveraging High-order Interactions for Unified Entity Relation Extraction
Entity relation extraction consists of two sub-tasks: entity recognition and relation extraction.
CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors
A common practice is to recast the task into a text-to-text format such that generative LLMs of natural language (NL-LLMs) like GPT-3 can be prompted to solve it.
A Confidence-based Partial Label Learning Model for Crowd-Annotated Named Entity Recognition
Particularly, we propose a Confidence-based Partial Label Learning (CPLL) method to integrate the prior confidence (given by annotators) and posterior confidences (learned by models) for crowd-annotated NER.
Unlearning with Fisher Masking
Experiments on various datasets and network structures show the effectiveness of the method: without any fine-tuning, the proposed Fisher masking could unlearn almost completely while maintaining most of the performance on the remain data.
Text2MDT: Extracting Medical Decision Trees from Medical Texts
In this work, we propose a novel task, Text2MDT, to explore the automatic extraction of MDTs from medical texts such as medical guidelines and textbooks.
Length Generalization of Causal Transformers without Position Encoding
Generalizing to longer sentences is important for recent Transformer-based language models.
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.
A Unified Encoding of Structures in Transition Systems
Transition systems usually contain various dynamic structures (e. g., stacks, buffers).
Word Reordering for Zero-shot Cross-lingual Structured Prediction
Adapting word order from one language to another is a key problem in cross-lingual structured prediction.
Pre-training Entity Relation Encoder with Intra-span and Inter-span Information
In particular, a span encoder is trained to recover a random shuffling of tokens in a span, and a span pair encoder is trained to predict positive pairs that are from the same sentences and negative pairs that are from different sentences using contrastive loss.
