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Found 20 papers, 6 papers with code
RFBFN: A Relation-First Blank Filling Network for Joint Relational Triple Extraction
However, most existing works either ignore the semantic information of relations or predict subjects and objects sequentially.
Learning from Sibling Mentions with Scalable Graph Inference in Fine-Grained Entity Typing
In this paper, we firstly empirically find that existing models struggle to handle hard mentions due to their insufficient contexts, which consequently limits their overall typing performance.
Fine-grained Entity Typing without Knowledge Base
Existing work on Fine-grained Entity Typing (FET) typically trains automatic models on the datasets obtained by using Knowledge Bases (KB) as distant supervision.
TexSmart: A System for Enhanced Natural Language Understanding
This paper introduces TexSmart, a text understanding system that supports fine-grained named entity recognition (NER) and enhanced semantic analysis functionalities.
Continuity of Topic, Interaction, and Query: Learning to Quote in Online Conversations
Quotations are crucial for successful explanations and persuasions in interpersonal communications.
Domain-Adaptive Pretraining Methods for Dialogue Understanding
Language models like BERT and SpanBERT pretrained on open-domain data have obtained impressive gains on various NLP tasks.
Conversational Semantic Role Labeling
Semantic role labeling (SRL) aims to extract the arguments for each predicate in an input sentence.
TexSmart: A Text Understanding System for Fine-Grained NER and Enhanced Semantic Analysis
This technique report introduces TexSmart, a text understanding system that supports fine-grained named entity recognition (NER) and enhanced semantic analysis functionalities.
Semantic Role Labeling Guided Multi-turn Dialogue ReWriter
For multi-turn dialogue rewriting, the capacity of effectively modeling the linguistic knowledge in dialog context and getting rid of the noises is essential to improve its performance.
Hypernymy Detection for Low-Resource Languages via Meta Learning
Hypernymy detection, a. k. a, lexical entailment, is a fundamental sub-task of many natural language understanding tasks.
SongNet: Rigid Formats Controlled Text Generation
(3) Although they are restricted to some formats, the sentence integrity must be guaranteed.
CASE: Context-Aware Semantic Expansion
In this study, we demonstrate that annotations for this task can be harvested at scale from existing corpora, in a fully automatic manner.
Coupling Global and Local Context for Unsupervised Aspect Extraction
Aspect words, indicating opinion targets, are essential in expressing and understanding human opinions.
Generating Classical Chinese Poems via Conditional Variational Autoencoder and Adversarial Training
It is a challenging task to automatically compose poems with not only fluent expressions but also aesthetic wording.
A Hybrid Approach to Automatic Corpus Generation for Chinese Spelling Check
Chinese spelling check (CSC) is a challenging yet meaningful task, which not only serves as a preprocessing in many natural language processing(NLP) applications, but also facilitates reading and understanding of running texts in peoples{'} daily lives.
Domain Adaptation for Disease Phrase Matching with Adversarial Networks
With the development of medical information management, numerous medical data are being classified, indexed, and searched in various systems.
Directional Skip-Gram: Explicitly Distinguishing Left and Right Context for Word Embeddings
In this paper, we present directional skip-gram (DSG), a simple but effective enhancement of the skip-gram model by explicitly distinguishing left and right context in word prediction.
A Manually Annotated Chinese Corpus for Non-task-oriented Dialogue Systems
This paper presents a large-scale corpus for non-task-oriented dialogue response selection, which contains over 27K distinct prompts more than 82K responses collected from social media.
hyperdoc2vec: Distributed Representations of Hypertext Documents
Hypertext documents, such as web pages and academic papers, are of great importance in delivering information in our daily life.
