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Found 48 papers, 15 papers with code
Graph-Structured Trajectory Extraction from Travelogues
Previous studies on sequence-based extraction of human movement trajectories have an issue of inadequate trajectory representation.
Introducing Syllable Tokenization for Low-resource Languages: A Case Study with Swahili
Tokenization is one such technique because it allows for the words to be split based on characters or subwords, creating word embeddings that best represent the structure of the language.
Distilling Named Entity Recognition Models for Endangered Species from Large Language Models
Natural language processing (NLP) practitioners are leveraging large language models (LLM) to create structured datasets from semi-structured and unstructured data sources such as patents, papers, and theses, without having domain-specific knowledge.
Enhancing Semantic Correlation between Instances and Relations for Zero-Shot Relation Extraction
This study argues that enhancing the semantic correlation between instances and relations is key to effectively solving the zero-shot relation extraction task.
Ranked #1 on
Zero-shot Relation Classification
on FewRel
Arukikata Travelogue Dataset with Geographic Entity Mention, Coreference, and Link Annotation
Geoparsing is a fundamental technique for analyzing geo-entity information in text.
Arukikata Travelogue Dataset
We have constructed Arukikata Travelogue Dataset and released it free of charge for academic research.
Improved Decomposition Strategy for Joint Entity and Relation Extraction
A recent study (Yu et al. 2020) proposed a novel decomposition strategy that splits the task into two interrelated subtasks: detection of the head-entity (HE) and identification of the corresponding tail-entity and relation (TER) for each extracted head-entity.
Nested Named Entity Recognition via Explicitly Excluding the Influence of the Best Path
This paper presents a novel method for nested named entity recognition.
Ranked #13 on
Nested Named Entity Recognition
on ACE 2005
Coordination Boundary Identification without Labeled Data for Compound Terms Disambiguation
We propose a simple method for nominal coordination boundary identification.
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
Length-controllable Abstractive Summarization by Guiding with Summary Prototype
Unlike the previous models, our length-controllable abstractive summarization model incorporates a word-level extractive module in the encoder-decoder model instead of length embeddings.
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
Global Entity Disambiguation with BERT
We propose a global entity disambiguation (ED) model based on BERT.
Ranked #1 on
Entity Disambiguation
on MSNBC
Gated Graph Recursive Neural Networks for Molecular Property Prediction
Molecule property prediction is a fundamental problem for computer-aided drug discovery and materials science.
Improving Multi-Word Entity Recognition for Biomedical Texts
The proposed SR model, FROBES, improves the representation of multi-word entities.
Stochastic Tokenization with a Language Model for Neural Text Classification
To make the model robust against infrequent tokens, we sampled segmentation for each sentence stochastically during training, which resulted in improved performance of text classification.
Relation Classification Using Segment-Level Attention-based CNN and Dependency-based RNN
Recently, relation classification has gained much success by exploiting deep neural networks.
Decomposed Local Models for Coordinate Structure Parsing
We propose a simple and accurate model for coordination boundary identification.
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.
Playing by the Book: An Interactive Game Approach for Action Graph Extraction from Text
Understanding procedural text requires tracking entities, actions and effects as the narrative unfolds.
A Span Selection Model for Semantic Role Labeling
We present a simple and accurate span-based model for semantic role labeling (SRL).
Ranked #7 on
Semantic Role Labeling
on CoNLL 2005
Cooperating Tools for MWE Lexicon Management and Corpus Annotation
Those two tools cooperate so that the words and multi-word expressions stored in Cradle are directly referred to by ChaKi in conducting corpus annotation, and the words and expressions annotated in ChaKi can be output as a list of lexical entities that are to be stored in Cradle.
Sentence Suggestion of Japanese Functional Expressions for Chinese-speaking Learners
We present a computer-assisted learning system, Jastudy, which is particularly designed for Chinese-speaking learners of Japanese as a second language (JSL) to learn Japanese functional expressions with suggestion of appropriate example sentences.
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
Interpretable Adversarial Perturbation in Input Embedding Space for Text
This paper restores interpretability to such methods by restricting the directions of perturbations toward the existing words in the input embedding space.
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).
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.
Coordination Boundary Identification with Similarity and Replaceability
We propose a neural network model for coordination boundary detection.
Joint Prediction of Morphosyntactic Categories for Fine-Grained Arabic Part-of-Speech Tagging Exploiting Tag Dictionary Information
One reason for this is that in the tagging scheme for such languages, a complete POS tag is formed by combining tags from multiple tag sets defined for each morphosyntactic category.
Neural Modeling of Multi-Predicate Interactions for Japanese Predicate Argument Structure Analysis
The performance of Japanese predicate argument structure (PAS) analysis has improved in recent years thanks to the joint modeling of interactions between multiple predicates.
English Multiword Expression-aware Dependency Parsing Including Named Entities
Because syntactic structures and spans of multiword expressions (MWEs) are independently annotated in many English syntactic corpora, they are generally inconsistent with respect to one another, which is harmful to the implementation of an aggregate system.
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
Ensemble of Neural Classifiers for Scoring Knowledge Base Triples
This paper describes our approach for the triple scoring task at the WSDM Cup 2017.
Japanese Text Normalization with Encoder-Decoder Model
Text normalization is the task of transforming lexical variants to their canonical forms.
Identification of Flexible Multiword Expressions with the Help of Dependency Structure Annotation
This paper presents our ongoing work on compilation of English multi-word expression (MWE) lexicon.
Construction of an English Dependency Corpus incorporating Compound Function Words
Nevertheless, this method often leads to the following problem: A node derived from an MWE could have multiple heads and the whole dependency structure including MWE might be cyclic.
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
