Search Results for author:
Found 21 papers, 14 papers with code
Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation
The KB graph model learns the relatedness of entities using the link structure of the KB, whereas the anchor context model aims to align vectors such that similar words and entities occur close to one another in the vector space by leveraging KB anchors and their context words.
Ranked #4 on
Entity Disambiguation
on TAC2010
Ensemble of Neural Classifiers for Scoring Knowledge Base Triples
This paper describes our approach for the triple scoring task at the WSDM Cup 2017.
Learning Distributed Representations of Texts and Entities from Knowledge Base
Given a text in the KB, we train our proposed model to predict entities that are relevant to the text.
Ranked #2 on
Entity Disambiguation
on TAC2010
Named Entity Disambiguation for Noisy Text
We address the task of Named Entity Disambiguation (NED) for noisy text.
Segment-Level Neural Conditional Random Fields for Named Entity Recognition
We present Segment-level Neural CRF, which combines neural networks with a linear chain CRF for segment-level sequence modeling tasks such as named entity recognition (NER) and syntactic chunking.
Studio Ousia's Quiz Bowl Question Answering System
In this chapter, we describe our question answering system, which was the winning system at the Human-Computer Question Answering (HCQA) Competition at the Thirty-first Annual Conference on Neural Information Processing Systems (NIPS).
Representation Learning of Entities and Documents from Knowledge Base Descriptions
In this paper, we describe TextEnt, a neural network model that learns distributed representations of entities and documents directly from a knowledge base (KB).
Ranked #1 on
Entity Typing
on Freebase FIGER
Trick Me If You Can: Human-in-the-loop Generation of Adversarial Examples for Question Answering
We propose human-in-the-loop adversarial generation, where human authors are guided to break models.
Wikipedia2Vec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from Wikipedia
The embeddings of entities in a large knowledge base (e. g., Wikipedia) are highly beneficial for solving various natural language tasks that involve real world knowledge.
Global Entity Disambiguation with BERT
We propose a global entity disambiguation (ED) model based on BERT.
Ranked #1 on
Entity Disambiguation
on MSNBC
Neural Attentive Bag-of-Entities Model for Text Classification
This study proposes a Neural Attentive Bag-of-Entities model, which is a neural network model that performs text classification using entities in a knowledge base.
Ranked #9 on
Text Classification
on 20NEWS
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
In this paper, we propose new pretrained contextualized representations of words and entities based on the bidirectional transformer.
Ranked #1 on
Entity Typing
on Open Entity
NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned
We review the EfficientQA competition from NeurIPS 2020.
Efficient Passage Retrieval with Hashing for Open-domain Question Answering
Most state-of-the-art open-domain question answering systems use a neural retrieval model to encode passages into continuous vectors and extract them from a knowledge source.
Ranked #2 on
Open-Domain Question Answering
on TQA
mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models
We train a multilingual language model with 24 languages with entity representations and show the model consistently outperforms word-based pretrained models in various cross-lingual transfer tasks.
Ranked #1 on
Cross-Lingual Question Answering
on XQuAD
(Average F1 metric, using extra
training data)
A Multilingual Bag-of-Entities Model for Zero-Shot Cross-Lingual Text Classification
We present a multilingual bag-of-entities model that effectively boosts the performance of zero-shot cross-lingual text classification by extending a multilingual pre-trained language model (e. g., M-BERT).
EASE: Entity-Aware Contrastive Learning of Sentence Embedding
We present EASE, a novel method for learning sentence embeddings via contrastive learning between sentences and their related entities.
MIA 2022 Shared Task: Evaluating Cross-lingual Open-Retrieval Question Answering for 16 Diverse Languages
We present the results of the Workshop on Multilingual Information Access (MIA) 2022 Shared Task, evaluating cross-lingual open-retrieval question answering (QA) systems in 16 typologically diverse languages.
Arukikata Travelogue Dataset with Geographic Entity Mention, Coreference, and Link Annotation
Geoparsing is a fundamental technique for analyzing geo-entity information in text.
LEIA: Facilitating Cross-Lingual Knowledge Transfer in Language Models with Entity-based Data Augmentation
In this study, we introduce LEIA, a language adaptation tuning method that utilizes Wikipedia entity names aligned across languages.
