Related papers: Extracting Information in a Low-resource Setting: …
Clinical studies often require understanding elements of a patient's narrative that exist only in free text clinical notes. To transform notes into structured data for downstream use, these elements are commonly extracted and normalized to…
Techniques for automatically extracting important content elements from business documents such as contracts, statements, and filings have the potential to make business operations more efficient. This problem can be formulated as a…
The fundamental process of evidence extraction and synthesis in evidence-based medicine involves extracting PICO (Population, Intervention, Comparison, and Outcome) elements from biomedical literature. However, Outcomes, being the most…
Supervised named entity recognition (NER) in the biomedical domain depends on large sets of annotated texts with the given named entities. The creation of such datasets can be time-consuming and expensive, while extraction of new entities…
Requirements identification in textual documents or extraction is a tedious and error prone task that many researchers suggest automating. We manually annotated the PURE dataset and thus created a new one containing both requirements and…
This study proposes a medical entity extraction method based on Transformer to enhance the information extraction capability of medical literature. Considering the professionalism and complexity of medical texts, we compare the performance…
We present a system that allows life-science researchers to search a linguistically annotated corpus of scientific texts using patterns over dependency graphs, as well as using patterns over token sequences and a powerful variant of boolean…
Process extraction from text is an important task of process discovery, for which various approaches have been developed in recent years. However, in contrast to other information extraction tasks, there is a lack of gold-standard corpora…
Text mining and information extraction for the medical domain has focused on scientific text generated by researchers. However, their direct access to individual patient experiences or patient-doctor interactions can be limited. Information…
This paper presents a practical approach to fine-grained information extraction. Through plenty of experiences of authors in practically applying information extraction to business process automation, there can be found a couple of…
Function is increasingly recognized as an important indicator of whole-person health, although it receives little attention in clinical natural language processing research. We introduce the first public annotated dataset specifically on…
The delivery of appropriate targeted therapies to cancer patients requires the complete analysis of the molecular profiling of tumors and the patient's clinical characteristics in the context of existing knowledge and recent findings…
We present a weakly-supervised data augmentation approach to improve Named Entity Recognition (NER) in a challenging domain: extracting biomedical entities (e.g., proteins) from the scientific literature. First, we train a neural NER (NNER)…
The automation of text summarisation of biomedical publications is a pressing need due to the plethora of information available on-line. This paper explores the impact of several supervised machine learning approaches for extracting…
We curated WikiPII, an automatically labeled dataset composed of Wikipedia biography pages, annotated for personal information extraction. Although automatic annotation can lead to a high degree of label noise, it is an inexpensive process…
Domain-specific named entity recognition (NER) on Computer Science (CS) scholarly articles is an information extraction task that is arguably more challenging for the various annotation aims that can beset the task and has been less studied…
Identifying concepts and relationships in biomedical text enables knowledge to be applied in computational analyses. Many biological natural language process (BioNLP) projects attempt to address this challenge, but the state of the art in…
Motivation: State-of-the-art biomedical named entity recognition (BioNER) systems often require handcrafted features specific to each entity type, such as genes, chemicals and diseases. Although recent studies explored using neural network…
Recent multilingual named entity recognition (NER) work has shown that large language models (LLMs) can provide effective synthetic supervision, yet such datasets have mostly appeared as by-products of broader experiments rather than as…
Biomedical Information Extraction is an exciting field at the crossroads of Natural Language Processing, Biology and Medicine. It encompasses a variety of different tasks that require application of state-of-the-art NLP techniques, such as…