Search Results for author:
Found 46 papers, 27 papers with code
Benchmark for Research Theme Classification of Scholarly Documents
We present a new gold-standard dataset and a benchmark for the Research Theme Identification task, a sub-task of the Scholarly Knowledge Graph Generation shared task, at the 3rd Workshop on Scholarly Document Processing.
DreamDrug - A crowdsourced NER dataset for detecting drugs in darknet markets
We present DreamDrug, a crowdsourced dataset for detecting mentions of drugs in noisy user-generated item listings from darknet markets.
DoSSIER at MedVidQA 2022: Text-based Approaches to Medical Video Answer Localization Problem
We propose two answer localization approaches that use only textual information extracted from the video.
A Reproducibility and Generalizability Study of Large Language Models for Query Generation
This paper presents an extensive study of Boolean query generation using LLMs for systematic reviews, reproducing and extending the work of Wang et al. and Alaniz et al. Our study investigates the replicability and reliability of results achieved using ChatGPT and compares its performance with open-source alternatives like Mistral and Zephyr to provide a more comprehensive analysis of LLMs for query generation.
Beyond the Numbers: Transparency in Relation Extraction Benchmark Creation and Leaderboards
This paper investigates the transparency in the creation of benchmarks and the use of leaderboards for measuring progress in NLP, with a focus on the relation extraction (RE) task.
AustroTox: A Dataset for Target-Based Austrian German Offensive Language Detection
Model interpretability in toxicity detection greatly profits from token-level annotations.
CSMeD: Bridging the Dataset Gap in Automated Citation Screening for Systematic Literature Reviews
Systematic literature reviews (SLRs) play an essential role in summarising, synthesising and validating scientific evidence.
Annotating Data for Fine-Tuning a Neural Ranker? Current Active Learning Strategies are not Better than Random Selection
We further find that gains provided by AL strategies come at the expense of more assessments (thus higher annotation costs) and AL strategies underperform random selection when comparing effectiveness given a fixed annotation cost.
CRUISE-Screening: Living Literature Reviews Toolbox
To this end, we developed CRUISE-Screening, a web-based application for conducting living literature reviews - a type of literature review that is continuously updated to reflect the latest research in a particular field.
Effective Matching of Patients to Clinical Trials using Entity Extraction and Neural Re-ranking
Our approach involves two key components in a pipeline-based model: (i) a data enrichment technique for enhancing both queries and documents during the first retrieval stage, and (ii) a novel re-ranking schema that uses a Transformer network in a setup adapted to this task by leveraging the structure of the CT documents.
Outcome-based Evaluation of Systematic Review Automation
We find that accounting for the difference in review outcomes leads to a different assessment of the quality of a system than if traditional evaluation measures were used.
Statute-enhanced lexical retrieval of court cases for COLIEE 2022
We discuss our experiments for COLIEE Task 1, a court case retrieval competition using cases from the Federal Court of Canada.
TripJudge: A Relevance Judgement Test Collection for TripClick Health Retrieval
Robust test collections are crucial for Information Retrieval research.
Are We There Yet? A Decision Framework for Replacing Term Based Retrieval with Dense Retrieval Systems
Recently, several dense retrieval (DR) models have demonstrated competitive performance to term-based retrieval that are ubiquitous in search systems.
Introducing Neural Bag of Whole-Words with ColBERTer: Contextualized Late Interactions using Enhanced Reduction
Recent progress in neural information retrieval has demonstrated large gains in effectiveness, while often sacrificing the efficiency and interpretability of the neural model compared to classical approaches.
Identifying the root cause of cable network problems with machine learning
Good quality network connectivity is ever more important.
Automation of Citation Screening for Systematic Literature Reviews using Neural Networks: A Replicability Study
In this work, we conduct a replicability study of the first two deep learning papers for citation screening and evaluate their performance on 23 publicly available datasets.
PARM: A Paragraph Aggregation Retrieval Model for Dense Document-to-Document Retrieval
However in the web domain we are in a setting with large amounts of training data and a query-to-passage or a query-to-document retrieval task.
Establishing Strong Baselines for TripClick Health Retrieval
We present strong Transformer-based re-ranking and dense retrieval baselines for the recently released TripClick health ad-hoc retrieval collection.
A Time-Optimized Content Creation Workflow for Remote Teaching
We describe our workflow to create an engaging remote learning experience for a university course, while minimizing the post-production time of the educators.
DoSSIER@COLIEE 2021: Leveraging dense retrieval and summarization-based re-ranking for case law retrieval
We address this challenge by combining lexical and dense retrieval methods on the paragraph-level of the cases for the first stage retrieval.
Linguistically Informed Masking for Representation Learning in the Patent Domain
Domain-specific contextualized language models have demonstrated substantial effectiveness gains for domain-specific downstream tasks, like similarity matching, entity recognition or information retrieval.
Intra-Document Cascading: Learning to Select Passages for Neural Document Ranking
An emerging recipe for achieving state-of-the-art effectiveness in neural document re-ranking involves utilizing large pre-trained language models - e. g., BERT - to evaluate all individual passages in the document and then aggregating the outputs by pooling or additional Transformer layers.
Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling
A vital step towards the widespread adoption of neural retrieval models is their resource efficiency throughout the training, indexing and query workflows.
Ranked #15 on
Zero-shot Text Search
on BEIR
Mitigating the Position Bias of Transformer Models in Passage Re-Ranking
In this work we analyze position bias on datasets, the contextualized representations, and their effect on retrieval results.
Cross-domain Retrieval in the Legal and Patent Domains: a Reproducibility Study
For reproducibility and transparency as well as to benefit the community we make our source code and the trained models publicly available.
Effective Crowd-Annotation of Participants, Interventions, and Outcomes in the Text of Clinical Trial Reports
Obtaining such a corpus from crowdworkers, however, has been shown to be ineffective since (i) workers usually lack domain-specific expertise to conduct the task with sufficient quality, and (ii) the standard approach of annotating entire abstracts of trial reports as one task-instance (i. e. HIT) leads to an uneven distribution in task effort.
Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation
Based on this finding, we propose a cross-architecture training procedure with a margin focused loss (Margin-MSE), that adapts knowledge distillation to the varying score output distributions of different BERT and non-BERT passage ranking architectures.
Fine-Grained Relevance Annotations for Multi-Task Document Ranking and Question Answering
We extend the ranked retrieval annotations of the Deep Learning track of TREC 2019 with passage and word level graded relevance annotations for all relevant documents.
DEXA: Supporting Non-Expert Annotators with Dynamic Examples from Experts
of 0. 68 to experts in DEXA vs. 0. 40 in CONTROL); (ii) already three per majority voting aggregated annotations of the DEXA approach reach substantial agreements to experts of 0. 78/0. 75/0. 69 for P/I/O (in CONTROL 0. 73/0. 58/0. 46).
Local Self-Attention over Long Text for Efficient Document Retrieval
In this work, we propose a local self-attention which considers a moving window over the document terms and for each term attends only to other terms in the same window.
Interpretable & Time-Budget-Constrained Contextualization for Re-Ranking
In addition, to gain insight into TK, we perform a clustered query analysis of TK's results, highlighting its strengths and weaknesses on queries with different types of information need and we show how to interpret the cause of ranking differences of two documents by comparing their internal scores.
DSR: A Collection for the Evaluation of Graded Disease-Symptom Relations
While existing disease-symptom relationship extraction methods are used as the foundation in the various medical tasks, no collection is available to systematically evaluate the performance of such methods.
Neural-IR-Explorer: A Content-Focused Tool to Explore Neural Re-Ranking Results
In this paper we look beyond metrics-based evaluation of Information Retrieval systems, to explore the reasons behind ranking results.
TU Wien @ TREC Deep Learning '19 -- Simple Contextualization for Re-ranking
The usage of neural network models puts multiple objectives in conflict with each other: Ideally we would like to create a neural model that is effective, efficient, and interpretable at the same time.
Deep Learning architectures for generalized immunofluorescence based nuclear image segmentation
In this work, we aim to evaluate the performance of state-of-the-art deep learning architectures to segment nuclei in fluorescence images of various tissue origins and sample preparation types without post-processing.
Let's measure run time! Extending the IR replicability infrastructure to include performance aspects
Establishing a docker-based replicability infrastructure offers the community a great opportunity: measuring the run time of information retrieval systems.
On the Effect of Low-Frequency Terms on Neural-IR Models
Low-frequency terms are a recurring challenge for information retrieval models, especially neural IR frameworks struggle with adequately capturing infrequently observed words.
Measuring Societal Biases from Text Corpora with Smoothed First-Order Co-occurrence
The common approach to measuring such biases using a corpus is by calculating the similarities between the embedding vector of a word (like nurse) and the vectors of the representative words of the concepts of interest (such as genders).
Why rankings of biomedical image analysis competitions should be interpreted with care
International challenges have become the standard for validation of biomedical image analysis methods.
Addressing Cross-Lingual Word Sense Disambiguation on Low-Density Languages: Application to Persian
We explore the use of unsupervised methods in Cross-Lingual Word Sense Disambiguation (CL-WSD) with the application of English to Persian.
Toward Incorporation of Relevant Documents in word2vec
As an alternative, explicit word representations propose vectors whose dimensions are easily interpretable, and recent methods show competitive performance to the dense vectors.
Uncertainty in Neural Network Word Embedding: Exploration of Threshold for Similarity
Word embedding, specially with its recent developments, promises a quantification of the similarity between terms.
Standard Test Collection for English-Persian Cross-Lingual Word Sense Disambiguation
In this paper, we address the shortage of evaluation benchmarks on Persian (Farsi) language by creating and making available a new benchmark for English to Persian Cross Lingual Word Sense Disambiguation (CL-WSD).
