Dataset Collection

3 Path-based Question Generation

2 Multi-Constraint Question

1 Introduction

5 SciNLP-KG Framework

4 Dataset Construction

3 NLP Knowledge Graph Schema

1 Introduction

5 CRONKGQA: Our proposed method

4 Temporal KG Embeddings

3 CRONQUESTIONS: The new TemporalKGQA dataset

1 Introduction

3 Approach

4 Approach

2 Task Formulation & Dataset

1 Introduction

4 Diachronic Embedding

1 Introduction

4 Experiments

3 Time-Aware Representations

1 Introduction

3 METHOD

2 CONCEPTS

1 INTRODUCTION

3. Representation Learning for Static Graphs

2. Background and Notation

1. Introduction

3 A Recommended Framework for Quality Evaluationof Knowledge Graph

2 Quality of Knowledge Graph

3 Our Method

1 Introduction

1 Introduction

3 Knowledge Graph Reasoning

1 Introduction

3 TEMPORAL SCOPE PREDICTION

1 INTRODUCTION

1. Introduction

2PROPOSEDMETHOD

1INTRODUCTION

3 Learning to Update a KG

2 TheTextWorld KGDataset

1 Introduction

1INTRODUCTION

4 An Algorithm for Dynamic Model Optimization

3 Specifying Models for Dynamic Knowledge Graphs

2 A Formal Objective for Dynamic Knowledge Graph Construction

2 Related work

1 Introduction

Deliberate practice leverages the spacing effect

Deliberate practice requires intense focus

Deliberate practice takes time and can be a lifelong process

Deliberate practice requires intrinsic motivation

Deliberate practice is most effective with the help of a coach or some kind of teacher

Deliberate practice involves constant feedback and measurement

Deliberate practice requires rest and recovery time

Deliberate practice is challenging and uncomfortable

Deliberate practice is structured and methodical

What is deliberate practice?

The elements of deliberate practice

3 Scoping Text Normalisation

3.3 Effect of Context Diversity

3.2 Effect of Mention Coverage

3.1 Effect of Name regularity

1 Introduction

2 Model

1 Introduction

4 Discussion

3.5 Main Results

2 Counterfactual Generator

1 Introduction

1 Introduction

5.2 Knowledge Graph Embeddings

5.1 Graph Analytics

5 INDUCTIVE KNOWLEDGE

4.3 Reasoning

4.2 Semantics and Entailment

4.1 Ontologies

4 DEDUCTIVE KNOWLEDGE

3.3 Context

3.2 Identity

3.1 Schema

3 SCHEMA, IDENTITY, CONTEXT

2.2 Querying

2.1 Models

2 DATA GRAPHS

7 Conclusions

1 Introduction

7 Conclusion

1 Introduction

1 Introduction

5 Conclusion

1 Introduction

2.7 Zero-Shot Learning

2 Prototypical Networks

1 Introduction

5 Conclusion

4 Experimental results

3 Experimental setup

2 BERTweet

1 Introduction

6 Conclusion

2 Background

1 Introduction

1 Introduction

7 Conclusions and Ongoing Work

1 Introduction

4 Ablation Study and Analyses

3 Experiments

2 Proposed Model: FinBERT

3 Evaluation

2 Knowledge Augmented NER

5 Financial Sentiment Experiments

4 FinBERT Training

3 Financial Corpora

2. ADDING EMERGING ENTITIES

1. MOTIVATION AND INTRODUCTION

V. RESULTS ANDDISCUSSION

IV. EXPERIMENTS

III. APPROACH: RDANER

B. Domain-specific Pre-training Methods

A. Learning-based Methods

II. RELATEDWORK

I. INTRODUCTION

Scale Effects

Reinforcement

“Platform Business Model” (Less helpful term)

Viral Effects & Virality

Geometric (Exponential/Non-Linear) Growth vs. Linear Growth

Part V – Related Concepts

Retention

Disintermediation

Multi-Tenanting

Chicken or Egg Problem (Cold Start Problem)

Switching Costs

Multiplayer vs. Single-Player Mode

Part IV – Building and Maintaining Network Effects

Negative Network Effects

Indirect Network Effects

Cross-Side Network Effects

Same-Side Network Effects

Asymptotic Network Effects

Homogeneous vs. Heterogeneous Networks

Asymmetry

Real Identity vs Pseudonymity vs Anonymity

Irregularity

Part III – Network Properties

The Network “Laws”

Critical Mass

Clustering

One-to-One vs One-to-Many

Directionality

Network Density

Part II – How Networks Work

3 Our Framework: CrossWeigh

1 Introduction

6 CONCLUSION

4 APPROACH

1 INTRODUCTION

3.3 Ablation: Character Embeddings Only

2 Method

6 Error Analysis

5 Results and Discussion

4 Experimental Settings

3.3 Sequential Inference

3.2 Model Description

3.1 Feature Representation

3 Methodology

1 Introduction

3 Data

2 Task Definition

4 RESULTS AND EVALUATION

3.2 Temporal resolution

3.1 Data selection and computations

3 SCHEMA AND KNOWLEDGE GRAPHPOPULATION

2 SOCIAL KNOWLEDGE GRAPH

1 INTRODUCTION

Further Opportunities and Challenges

3.2.4 Integration with External Sources.

3.2.3 Population and Maintenance.

3.2.2 Semantic Annotation of Text.

3 PERSONAL KNOWLEDGE GRAPHS

1INTRODUCTION

6 Conclusions

5.3 Effects of Extra Pretraining

5.2 Effects of Entity-aware Self-attention

5.1 Effects of Entity Representations

5 Analysis

4.5 Extractive Question Answering

4.4 Cloze-style Question Answering

4.3 Named Entity Recognition

4.2 Relation Classification

4.1 Entity Typing

4 Experiments

3.3 Pretraining Task

3.2 Entity-aware Self-attention

3.1 Input Representation

3 LUKE

1 Introduction

III. KEYTECHNIQUES INCONSTRUCTINGKNOWLEDGEGRAPHS

II. THE2019 ICDM/ICBK CONTEST