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Found 30 papers, 19 papers with code
BEEDS: Large-Scale Biomedical Event Extraction using Distant Supervision and Question Answering
Automatic extraction of event structures from text is a promising way to extract important facts from the evergrowing amount of biomedical literature.
WBI at MEDIQA 2021: Summarizing Consumer Health Questions with Generative Transformers
This paper describes our contribution for the MEDIQA-2021 Task 1 question summarization competition.
Biomedical Event Extraction as Multi-turn Question Answering
Biomedical event extraction from natural text is a challenging task as it searches for complex and often nested structures describing specific relationships between multiple molecular entities, such as genes, proteins, or cellular components.
Extend, don’t rebuild: Phrasing conditional graph modification as autoregressive sequence labelling
Deriving and modifying graphs from natural language text has become a versatile basis technology for information extraction with applications in many subfields, such as semantic parsing or knowledge graph construction.
preon: Fast and accurate entity normalization for drug names and cancer types in precision oncology
Summary preon is a fast and accurate library for the normalization of drug names and cancer types in large-scale data integration.
HunFlair2 in a cross-corpus evaluation of biomedical named entity recognition and normalization tools
Instead, they are applied in the wild, i. e., on application-dependent text collections different from those used for the tools' training, varying, e. g., in focus, genre, style, and text type.
BELHD: Improving Biomedical Entity Linking with Homonoym Disambiguation
Biomedical entity linking (BEL) is the task of grounding entity mentions to a knowledge base (KB).
Large Language Models to the Rescue: Reducing the Complexity in Scientific Workflow Development Using ChatGPT
Scientific workflow systems are increasingly popular for expressing and executing complex data analysis pipelines over large datasets, as they offer reproducibility, dependability, and scalability of analyses by automatic parallelization on large compute clusters.
Raising the ClaSS of Streaming Time Series Segmentation
Ubiquitous sensors today emit high frequency streams of numerical measurements that reflect properties of human, animal, industrial, commercial, and natural processes.
Human Activity Segmentation Challenge @ ECML/PKDD’23
Despite its importance, existing methods demonstrate limited efficacy on real-world multivariate time series data.
BELB: a Biomedical Entity Linking Benchmark
Biomedical entity linking (BEL) is the task of grounding entity mentions to a knowledge base.
Time Series Segmentation Applied to a New Data Set for Mobile Sensing of Human Activities
Human activity recognition (HAR) systems implement workflows that automatically detect activities from motion data, captured e. g. by wearable devices such as smartphones.
Window Size Selection in Unsupervised Time Series Analytics: A Review and Benchmark
We provide, for the first time, a systematic survey and experimental study of 6 TS window size selection (WSS) algorithms on three diverse TSDM tasks, namely anomaly detection, segmentation and motif discovery, using state-of-the art TSDM algorithms and benchmarks.
WEASEL 2.0 -- A Random Dilated Dictionary Transform for Fast, Accurate and Memory Constrained Time Series Classification
Time series classification (TSC) is the task of assigning a time series to one of a set of predefined classes, usually based on a model learned from examples.
ClaSP -- Parameter-free Time Series Segmentation
Such processes often consist of multiple states, e. g. operating modes of a machine, such that state changes in the observed processes result in changes in the distribution of shape of the measured values.
Ranked #1 on
Change Point Detection
on TSSB
(Covering metric)
Motiflets -- Simple and Accurate Detection of Motifs in Time Series
Motif discovery (MD) is the task of finding such motifs in a given input series.
ClaSP - Time Series Segmentation
In our experimental evaluation using a benchmark of 98 datasets, we show that ClaSP outperforms the state-of-the-art in terms of accuracy and is also faster than the second best method.
Ranked #1 on
Change Point Detection
on TSSB
Early Detection of Sexual Predators in Chats
We define and study the task of early sexual predator detection (eSPD) in chats, where the goal is to analyze a running chat from its beginning and predict grooming attempts as early and as accurately as possible.
TabSim: A Siamese Neural Network for Accurate Estimation of Table Similarity
Tables display information as a two-dimensional matrix, the semantics of which is conveyed by a mixture of structure (rows, columns), headers, caption, and content.
HunFlair: An Easy-to-Use Tool for State-of-the-Art Biomedical Named Entity Recognition
Summary: Named Entity Recognition (NER) is an important step in biomedical information extraction pipelines.
NLProlog: Reasoning with Weak Unification for Question Answering in Natural Language
In contrast, neural models can cope very well with ambiguity by learning distributed representations of words and their composition from data, but lead to models that are difficult to interpret.
NLProlog: Reasoning with Weak Unification for Natural Language Question Answering
Currently, most work in natural language processing focuses on neural networks which learn distributed representations of words and their composition, thereby performing well in the presence of large linguistic variability.
Identifying Key Sentences for Precision Oncology Using Semi-Supervised Learning
For obtaining a realistic classification model, we propose the use of abstracts summarised in relevant sentences as unlabelled examples through Self-Training.
Predictive Performance Modeling for Distributed Computing using Black-Box Monitoring and Machine Learning
In many domains, the previous decade was characterized by increasing data volumes and growing complexity of computational workloads, creating new demands for highly data-parallel computing in distributed systems.
Cross-lingual Candidate Search for Biomedical Concept Normalization
Biomedical concept normalization links concept mentions in texts to a semantically equivalent concept in a biomedical knowledge base.
Multivariate Time Series Classification with WEASEL+MUSE
Multivariate time series (MTS) arise when multiple interconnected sensors record data over time.
Fast and Accurate Time Series Classification with WEASEL
On the popular UCR benchmark of 85 TS datasets, WEASEL is more accurate than the best current non-ensemble algorithms at orders-of-magnitude lower classification and training times, and it is almost as accurate as ensemble classifiers, whose computational complexity makes them inapplicable even for mid-size datasets.
SCARE ― The Sentiment Corpus of App Reviews with Fine-grained Annotations in German
This corpus consists of 1, 760 annotated application reviews from the Google Play Store with 2, 487 aspects and 3, 959 subjective phrases.
