Search Results for author:
Found 21 papers, 7 papers with code
Multi-Level Sequence Denoising with Cross-Signal Contrastive Learning for Sequential Recommendation
To the end, in this paper, we propose a novel model named Multi-level Sequence Denoising with Cross-signal Contrastive Learning (MSDCCL) for sequential recommendation.
Toward FAIR Semantic Publishing of Research Dataset Metadata in the Open Research Knowledge Graph
We design a specific application of the ORKG-Dataset semantic model based on 40 diverse research datasets on scientific information extraction.
Enhancing Software-Related Information Extraction via Single-Choice Question Answering with Large Language Models
This paper describes our participation in the Shared Task on Software Mentions Disambiguation (SOMD), with a focus on improving relation extraction in scholarly texts through generative Large Language Models (LLMs) using single-choice question-answering.
Dissecting Paraphrases: The Impact of Prompt Syntax and supplementary Information on Knowledge Retrieval from Pretrained Language Models
Typically, designing these prompts is a tedious task because small differences in syntax or semantics can have a substantial impact on knowledge retrieval performance.
TACO -- Twitter Arguments from COnversations
Twitter has emerged as a global hub for engaging in online conversations and as a research corpus for various disciplines that have recognized the significance of its user-generated content.
nuScenes Knowledge Graph -- A comprehensive semantic representation of traffic scenes for trajectory prediction
Further, we present nuScenes Knowledge Graph (nSKG), a knowledge graph for the nuScenes dataset, that models explicitly all scene participants and road elements, as well as their semantic and spatial relationships.
GSAP-NER: A Novel Task, Corpus, and Baseline for Scholarly Entity Extraction Focused on Machine Learning Models and Datasets
Named Entity Recognition (NER) models play a crucial role in various NLP tasks, including information extraction (IE) and text understanding.
Ranked #1 on
Scholarly Named Entity Recognition
on GSAP-NER
Large Language Models and Knowledge Graphs: Opportunities and Challenges
Large Language Models (LLMs) have taken Knowledge Representation -- and the world -- by storm.
Which Factors are associated with Open Access Publishing? A Springer Nature Case Study
Employing correlation and regression analyses, we describe the relationship between authors affiliated with countries from different income levels, their choice of publishing model, and the citation impact of their papers.
Still Haven't Found What You're Looking For -- Detecting the Intent of Web Search Missions from User Interaction Features
Web search is among the most frequent online activities.
SciTweets -- A Dataset and Annotation Framework for Detecting Scientific Online Discourse
Scientific topics, claims and resources are increasingly debated as part of online discourse, where prominent examples include discourse related to COVID-19 or climate change.
SaL-Lightning Dataset: Search and Eye Gaze Behavior, Resource Interactions and Knowledge Gain during Web Search
The emerging research field Search as Learning investigates how the Web facilitates learning through modern information retrieval systems.
SoMeSci- A 5 Star Open Data Gold Standard Knowledge Graph of Software Mentions in Scientific Articles
To the best of our knowledge, SoMeSci is the most comprehensive corpus about software mentions in scientific articles, providing training samples for Named Entity Recognition, Relation Extraction, Entity Disambiguation, and Entity Linking.
Predicting Knowledge Gain during Web Search based on Multimedia Resource Consumption
Related work in this field, also called search as learning, has focused on behavioral or text resource features to predict learning outcome and knowledge gain.
Better Together -- An Ensemble Learner for Combining the Results of Ready-made Entity Linking Systems
Entity linking (EL) is the task of automatically identifying entity mentions in text and resolving them to a corresponding entity in a reference knowledge base like Wikipedia.
The Role of Word-Eye-Fixations for Query Term Prediction
We investigate the relationship between a range of in-session features, in particular, gaze data, with the query terms and train models for predicting query terms.
Exploiting stance hierarchies for cost-sensitive stance detection of Web documents
In this context, stance detection aims at identifying the position (stance) of a document towards a claim.
TweetsCOV19 -- A Knowledge Base of Semantically Annotated Tweets about the COVID-19 Pandemic
Publicly available social media archives facilitate research in the social sciences and provide corpora for training and testing a wide range of machine learning and natural language processing methods.
Same but Different: Distant Supervision for Predicting and Understanding Entity Linking Difficulty
Entity Linking (EL) is the task of automatically identifying entity mentions in a piece of text and resolving them to a corresponding entity in a reference knowledge base like Wikipedia.
Time-Aware and Corpus-Specific Entity Relatedness
Entity relatedness has emerged as an important feature in a plethora of applications such as information retrieval, entity recommendation and entity linking.
Inferring Missing Categorical Information in Noisy and Sparse Web Markup
Nevertheless, given the scale and diversity of Web markup data, nodes that provide missing information can be obtained from the Web in large quantities, in particular for categorical properties.
