Search Results for author:
Found 25 papers, 14 papers with code
AnnoCTR: A Dataset for Detecting and Linking Entities, Tactics, and Techniques in Cyber Threat Reports
In our few-shot scenario, we find that for identifying the MITRE ATT&CK concepts that are mentioned explicitly or implicitly in a text, concept descriptions from MITRE ATT&CK are an effective source for training data augmentation.
Discourse-Aware In-Context Learning for Temporal Expression Normalization
In this work, we explore the feasibility of proprietary and open-source large language models (LLMs) for TE normalization using in-context learning to inject task, document, and example information into the model.
Rehearsal-Free Modular and Compositional Continual Learning for Language Models
Continual learning aims at incrementally acquiring new knowledge while not forgetting existing knowledge.
BoschAI @ Causal News Corpus 2023: Robust Cause-Effect Span Extraction using Multi-Layer Sequence Tagging and Data Augmentation
Understanding causality is a core aspect of intelligence.
DelucionQA: Detecting Hallucinations in Domain-specific Question Answering
Hallucination is a well-known phenomenon in text generated by large language models (LLMs).
GradSim: Gradient-Based Language Grouping for Effective Multilingual Training
However, not all languages positively influence each other and it is an open research question how to select the most suitable set of languages for multilingual training and avoid negative interference among languages whose characteristics or data distributions are not compatible.
TADA: Efficient Task-Agnostic Domain Adaptation for Transformers
Our broad evaluation in 4 downstream tasks for 14 domains across single- and multi-domain setups and high- and low-resource scenarios reveals that TADA is an effective and efficient alternative to full domain-adaptive pre-training and adapters for domain adaptation, while not introducing additional parameters or complex training steps.
NLNDE at SemEval-2023 Task 12: Adaptive Pretraining and Source Language Selection for Low-Resource Multilingual Sentiment Analysis
In this work, we propose to leverage language-adaptive and task-adaptive pretraining on African texts and study transfer learning with source language selection on top of an African language-centric pretrained language model.
SwitchPrompt: Learning Domain-Specific Gated Soft Prompts for Classification in Low-Resource Domains
Prompting pre-trained language models leads to promising results across natural language processing tasks but is less effective when applied in low-resource domains, due to the domain gap between the pre-training data and the downstream task.
Multilingual Normalization of Temporal Expressions with Masked Language Models
The detection and normalization of temporal expressions is an important task and preprocessing step for many applications.
CLIN-X: pre-trained language models and a study on cross-task transfer for concept extraction in the clinical domain
The field of natural language processing (NLP) has recently seen a large change towards using pre-trained language models for solving almost any task.
Boosting Transformers for Job Expression Extraction and Classification in a Low-Resource Setting
In this paper, we explore possible improvements of transformer models in a low-resource setting.
To Share or not to Share: Predicting Sets of Sources for Model Transfer Learning
For this, we study the effects of model transfer on sequence labeling across various domains and tasks and show that our methods based on model similarity and support vector machines are able to predict promising sources, resulting in performance increases of up to 24 F1 points.
ANEA: Distant Supervision for Low-Resource Named Entity Recognition
Distant supervision allows obtaining labeled training corpora for low-resource settings where only limited hand-annotated data exists.
Low Resource Named Entity Recognition
named-entity-recognition
+2
NLNDE at CANTEMIST: Neural Sequence Labeling and Parsing Approaches for Clinical Concept Extraction
The recognition and normalization of clinical information, such as tumor morphology mentions, is an important, but complex process consisting of multiple subtasks.
A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios
Deep neural networks and huge language models are becoming omnipresent in natural language applications.
NLNDE: The Neither-Language-Nor-Domain-Experts' Way of Spanish Medical Document De-Identification
Natural language processing has huge potential in the medical domain which recently led to a lot of research in this field.
NLNDE: Enhancing Neural Sequence Taggers with Attention and Noisy Channel for Robust Pharmacological Entity Detection
Named entity recognition has been extensively studied on English news texts.
The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain
With this paper, we publish our annotation guidelines, as well as our SOFC-Exp corpus consisting of 45 open-access scholarly articles annotated by domain experts.
Adversarial Alignment of Multilingual Models for Extracting Temporal Expressions from Text
Although temporal tagging is still dominated by rule-based systems, there have been recent attempts at neural temporal taggers.
Ranked #1 on
Temporal Tagging
on Catalan TimeBank 1.0
Closing the Gap: Joint De-Identification and Concept Extraction in the Clinical Domain
Exploiting natural language processing in the clinical domain requires de-identification, i. e., anonymization of personal information in texts.
On the Choice of Auxiliary Languages for Improved Sequence Tagging
Recent work showed that embeddings from related languages can improve the performance of sequence tagging, even for monolingual models.
Feature-Dependent Confusion Matrices for Low-Resource NER Labeling with Noisy Labels
In low-resource settings, the performance of supervised labeling models can be improved with automatically annotated or distantly supervised data, which is cheap to create but often noisy.
Low Resource Named Entity Recognition
named-entity-recognition
+4
