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Found 22 papers, 7 papers with code
OIE@OIA: an Adaptable and Efficient Open Information Extraction Framework
OIE@OIA follows the methodology of Open Information eXpression (OIX): parsing a sentence to an Open Information Annotation (OIA) Graph and then adapting the OIA graph to different OIE tasks with simple rules.
Fooling the Textual Fooler via Randomizing Latent Representations
Despite outstanding performance in a variety of NLP tasks, recent studies have revealed that NLP models are vulnerable to adversarial attacks that slightly perturb the input to cause the models to misbehave.
Eva-KELLM: A New Benchmark for Evaluating Knowledge Editing of LLMs
In this paper, we propose Eva-KELLM, a new benchmark for evaluating knowledge editing of LLMs.
RE-Matching: A Fine-Grained Semantic Matching Method for Zero-Shot Relation Extraction
However, general matching methods lack explicit modeling of the above matching pattern.
Actively Supervised Clustering for Open Relation Extraction
The key to the setting is selecting which instances to label.
How Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Tasks
The GPT-3. 5 models have demonstrated impressive performance in various Natural Language Processing (NLP) tasks, showcasing their strong understanding and reasoning capabilities.
Natural Language Inference
Natural Language Understanding
+1
Improving Semantic Matching through Dependency-Enhanced Pre-trained Model with Adaptive Fusion
Transformer-based pre-trained models like BERT have achieved great progress on Semantic Sentence Matching.
NL2GDPR: Automatically Develop GDPR Compliant Android Application Features from Natural Language
At the core, NL2GDPR is a privacy-centric information extraction model, appended with a GDPR policy finder and a policy generator.
Label-Smoothed Backdoor Attack
In order to achieve a high attack success rate using as few poisoned training samples as possible, most existing attack methods change the labels of the poisoned samples to the target class.
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing
To guarantee user acceptability, all the text transformations are linguistically based, and we provide a human evaluation for each one.
Topic-Oriented Spoken Dialogue Summarization for Customer Service with Saliency-Aware Topic Modeling
In a customer service system, dialogue summarization can boost service efficiency by automatically creating summaries for long spoken dialogues in which customers and agents try to address issues about specific topics.
Toward Recognizing More Entity Types in NER: An Efficient Implementation using Only Entity Lexicons
In this work, we explore the way to quickly adjust an existing named entity recognition (NER) system to make it capable of recognizing entity types not defined in the system.
A Lexicon-Based Graph Neural Network for Chinese NER
Recurrent neural networks (RNN) used for Chinese named entity recognition (NER) that sequentially track character and word information have achieved great success.
Ranked #13 on
Chinese Named Entity Recognition
on OntoNotes 4
Chinese Named Entity Recognition
named-entity-recognition
+3
Weighed Domain-Invariant Representation Learning for Cross-domain Sentiment Analysis
To address this problem, we propose a modification to DIRL, obtaining a novel weighted domain-invariant representation learning (WDIRL) framework.
Simplify the Usage of Lexicon in Chinese NER
This method avoids designing a complicated sequence modeling architecture, and for any neural NER model, it requires only subtle adjustment of the character representation layer to introduce the lexicon information.
Ranked #8 on
Chinese Named Entity Recognition
on Resume NER
Chinese Named Entity Recognition
named-entity-recognition
+2
Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning
In this work, we explore the way to perform named entity recognition (NER) using only unlabeled data and named entity dictionaries.
Address Instance-level Label Prediction in Multiple Instance Learning
Existent approaches in this field were mainly designed for the bag-level label prediction (predict labels for bags) but not the instance-level (predict labels for instances), with the task loss being only defined at the bag level.
Learning Task-specific Representation for Novel Words in Sequence Labeling
However, representations of unseen or rare words trained on the end task are usually poor for appreciable performance.
Long Short-Term Memory with Dynamic Skip Connections
In recent years, long short-term memory (LSTM) has been successfully used to model sequential data of variable length.
Ranked #33 on
Sentiment Analysis
on IMDb
Transferring from Formal Newswire Domain with Hypernet for Twitter POS Tagging
However, from a linguistic perspective, Twitter users not only tend to mimic the formal expressions of traditional media, like news, but they also appear to be developing linguistically informal styles.
Ranked #2 on
Part-Of-Speech Tagging
on Ritter
Cross-Domain Sentiment Classification with Target Domain Specific Information
And we introduce a few target domain labeled data for learning domain-specific information.
Part-of-Speech Tagging for Twitter with Adversarial Neural Networks
In this work, we study the problem of part-of-speech tagging for Tweets.
Ranked #3 on
Part-Of-Speech Tagging
on Ritter
