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Found 11 papers, 2 papers with code
Learn The Big Picture: Representation Learning for Clustering
Existing supervised models for text clustering find it difficult to directly optimize for clustering results.
An Exam-based Evaluation Approach Beyond Traditional Relevance Judgments
We envision the role of a human judge to edit and define an exam question bank that will test for the presence of relevant information in text.
Fine-grained Forecasting Models Via Gaussian Process Blurring Effect
In contrast, we are building on successful denoising approaches for image generation by advocating for an end-to-end forecasting and denoising paradigm.
Retrieve-Cluster-Summarize: An Alternative to End-to-End Training for Query-specific Article Generation
Query-specific article generation is the task of, given a search query, generate a single article that gives an overview of the topic.
Perspectives on Large Language Models for Relevance Judgment
When asked, large language models (LLMs) like ChatGPT claim that they can assist with relevance judgments but it is not clear whether automated judgments can reliably be used in evaluations of retrieval systems.
Report on the First HIPstIR Workshop on the Future of Information Retrieval
The vision of HIPstIR is that early stage information retrieval (IR) researchers get together to develop a future for non-mainstream ideas and research agendas in IR.
UNH at SemEval-2019 Task 12: Toponym Resolution in Scientific Papers
We focus on Subtask 1: Toponym Detection which is the identification of spans of text for place names mentioned in a document.
An Analysis of Deep Contextual Word Embeddings and Neural Architectures for Toponym Mention Detection in Scientific Publications
Toponym detection in scientific papers is an open task and a key first step in place entity enrichment of documents.
Knowledge-rich Image Gist Understanding Beyond Literal Meaning
We investigate the problem of understanding the message (gist) conveyed by images and their captions as found, for instance, on websites or news articles.
Localizing Bugs in Program Executions with Graphical Models
We devise a graphical model that supports the process of debugging software by guiding developers to code that is likely to contain defects.
