Search Results for author: Tom Hope

Found 20 papers, 10 papers with code

CascadER: Cross-Modal Cascading for Knowledge Graph Link Prediction

no code implementations16 May 2022 Tara Safavi, Doug Downey, Tom Hope

Knowledge graph (KG) link prediction is a fundamental task in artificial intelligence, with applications in natural language processing, information retrieval, and biomedicine.

Information Retrieval Knowledge Graph Embeddings +1

A Dataset for N-ary Relation Extraction of Drug Combinations

1 code implementation4 May 2022 Aryeh Tiktinsky, Vijay Viswanathan, Danna Niezni, Dana Meron Azagury, Yosi Shamay, Hillel Taub-Tabib, Tom Hope, Yoav Goldberg

Furthermore, the relations in this dataset predominantly require language understanding beyond the sentence level, adding to the challenge of this task.

Relation Extraction

A Computational Inflection for Scientific Discovery

no code implementations4 May 2022 Tom Hope, Doug Downey, Oren Etzioni, Daniel S. Weld, Eric Horvitz

We stand at the foot of a significant inflection in the trajectory of scientific discovery.

Literature-Augmented Clinical Outcome Prediction

1 code implementation16 Nov 2021 Aakanksha Naik, Sravanthi Parasa, Sergey Feldman, Lucy Lu Wang, Tom Hope

We present BEEP (Biomedical Evidence-Enhanced Predictions), a novel approach for clinical outcome prediction that retrieves patient-specific medical literature and incorporates it into predictive models.

Decision Making

Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity

1 code implementation16 Nov 2021 Sheshera Mysore, Arman Cohan, Tom Hope

We present a new scientific document similarity model based on matching fine-grained aspects of texts.

A Search Engine for Discovery of Scientific Challenges and Directions

1 code implementation NeurIPS Workshop AI4Scien 2021 Dan Lahav, Jon Saad Falcon, Bailey Kuehl, Sophie Johnson, Sravanthi Parasa, Noam Shomron, Duen Horng Chau, Diyi Yang, Eric Horvitz, Daniel S. Weld, Tom Hope

To address this problem, we present a novel task of extraction and search of scientific challenges and directions, to facilitate rapid knowledge discovery.

Bursting Scientific Filter Bubbles: Boosting Innovation via Novel Author Discovery

no code implementations NeurIPS Workshop AI4Scien 2021 Jason Portenoy, Marissa Radensky, Jevin West, Eric Horvitz, Daniel Weld, Tom Hope

We also demonstrate an approach for displaying information about authors, boosting the ability to understand the work of new, unfamiliar scholars.

Scaling Creative Inspiration with Fine-Grained Functional Aspects of Ideas

no code implementations19 Feb 2021 Tom Hope, Ronen Tamari, Hyeonsu Kang, Daniel Hershcovich, Joel Chan, Aniket Kittur, Dafna Shahaf

Large repositories of products, patents and scientific papers offer an opportunity for building systems that scour millions of ideas and help users discover inspirations.

Extracting a Knowledge Base of Mechanisms from COVID-19 Papers

3 code implementations NAACL 2021 Tom Hope, Aida Amini, David Wadden, Madeleine van Zuylen, Sravanthi Parasa, Eric Horvitz, Daniel Weld, Roy Schwartz, Hannaneh Hajishirzi

The COVID-19 pandemic has spawned a diverse body of scientific literature that is challenging to navigate, stimulating interest in automated tools to help find useful knowledge.

SciSight: Combining faceted navigation and research group detection for COVID-19 exploratory scientific search

no code implementations EMNLP 2020 Tom Hope, Jason Portenoy, Kishore Vasan, Jonathan Borchardt, Eric Horvitz, Daniel S. Weld, Marti A. Hearst, Jevin West

The COVID-19 pandemic has sparked unprecedented mobilization of scientists, generating a deluge of papers that makes it hard for researchers to keep track and explore new directions.

Language Modelling

Language (Re)modelling: Towards Embodied Language Understanding

no code implementations ACL 2020 Ronen Tamari, Chen Shani, Tom Hope, Miriam R. L. Petruck, Omri Abend, Dafna Shahaf

While natural language understanding (NLU) is advancing rapidly, today's technology differs from human-like language understanding in fundamental ways, notably in its inferior efficiency, interpretability, and generalization.

Natural Language Understanding

Learning a faceted customer segmentation for discovering new business opportunities at Intel

no code implementations27 Nov 2019 Itay Lieder, Meirav Segal, Eran Avidan, Asaf Cohen, Tom Hope

For sales and marketing organizations within large enterprises, identifying and understanding new markets, customers and partners is a key challenge.

Ballpark Crowdsourcing: The Wisdom of Rough Group Comparisons

1 code implementation13 Dec 2017 Tom Hope, Dafna Shahaf

By collecting rough guesses on groups of instances and using machine learning to infer the individual labels, our lightweight framework is able to address core crowdsourcing challenges and train machine learning models in a cost-effective way.

Accelerating Innovation Through Analogy Mining

no code implementations17 Jun 2017 Tom Hope, Joel Chan, Aniket Kittur, Dafna Shahaf

The availability of large idea repositories (e. g., the U. S. patent database) could significantly accelerate innovation and discovery by providing people with inspiration from solutions to analogous problems.

Information Retrieval

Ballpark Learning: Estimating Labels from Rough Group Comparisons

no code implementations30 Jun 2016 Tom Hope, Dafna Shahaf

We are interested in estimating individual labels given only coarse, aggregated signal over the data points.

Sentiment Analysis

Clustering Noisy Signals with Structured Sparsity Using Time-Frequency Representation

1 code implementation18 Oct 2015 Tom Hope, Avishai Wagner, Or Zuk

We propose a simple and efficient time-series clustering framework particularly suited for low Signal-to-Noise Ratio (SNR), by simultaneous smoothing and dimensionality reduction aimed at preserving clustering information.

Dimensionality Reduction Time Series +2

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