Zobrazeno 1 - 10
of 140
pro vyhledávání: '"Cudre-Mauroux, Philippe"'
Autor:
Arnold, Sebastian, Schneider, Rudolf, Cudré-Mauroux, Philippe, Gers, Felix A., Löser, Alexander
Publikováno v:
Transactions of the Association for Computational Linguistics, Vol 7, Pp 169-184 (2019)
When searching for information, a human reader first glances over a document, spots relevant sections, and then focuses on a few sentences for resolving her intention. However, the high variance of document structure complicates the identification of
Externí odkaz:
https://doaj.org/article/5187928a2c8a46caab4598208ad7488f
Autor:
Acosta, Maribel, Cudré-Mauroux, Philippe, Maleshkova, Maria, Pellegrini, Tassilo, Sack, Harald, Sure-Vetter, York
This open access book constitutes the refereed proceedings of the 15th International Conference on Semantic Systems, SEMANTiCS 2019, held in Karlsruhe, Germany, in September 2019. The 20 full papers and 8 short papers presented in this volume were ca
Externí odkaz:
http://library.oapen.org/handle/20.500.12657/23320
Prompting and Multiple Choices Questions (MCQ) have become the preferred approach to assess the capabilities of Large Language Models (LLMs), due to their ease of manipulation and evaluation. Such experimental appraisals have pointed toward the LLMs'
Externí odkaz:
http://arxiv.org/abs/2406.14986
Autor:
Deng, Bangchao, Qu, Bingqing, Wang, Pengyang, Yang, Dingqi, Fankhauser, Benjamin, Cudre-Mauroux, Philippe
Location prediction forecasts a user's location based on historical user mobility traces. To tackle the intrinsic sparsity issue of real-world user mobility traces, spatiotemporal contexts have been shown as significantly useful. Existing solutions m
Externí odkaz:
http://arxiv.org/abs/2402.16310
Publikováno v:
IEEE International Conference on Big Data 2023
With the recent spike in the number and availability of Large Language Models (LLMs), it has become increasingly important to provide large and realistic benchmarks for evaluating Knowledge Graph Question Answering (KGQA) systems. So far the majority
Externí odkaz:
http://arxiv.org/abs/2309.16248
Bridging the Gap between Community and Node Representations: Graph Embedding via Community Detection
Graph embedding has become a key component of many data mining and analysis systems. Current graph embedding approaches either sample a large number of node pairs from a graph to learn node embeddings via stochastic optimization or factorize a high-o
Externí odkaz:
http://arxiv.org/abs/1912.08808
Publikováno v:
AAAI, 2020
Microblogging platforms such as Twitter are increasingly being used in event detection. Existing approaches mainly use machine learning models and rely on event-related keywords to collect the data for model training. These approaches make strong ass
Externí odkaz:
http://arxiv.org/abs/1912.00667
Clustering is a crucial component of many data mining systems involving the analysis and exploration of various data. Data diversity calls for clustering algorithms to be accurate while providing stable (i.e., deterministic and robust) results on arb
Externí odkaz:
http://arxiv.org/abs/1909.08786
We address the problem of tuning word embeddings for specific use cases and domains. We propose a new method that automatically combines multiple domain-specific embeddings, selected from a wide range of pre-trained domain-specific embeddings, to imp
Externí odkaz:
http://arxiv.org/abs/1909.02307
Autor:
Luggen, Michael, Difallah, Djellel, Sarasua, Cristina, Demartini, Gianluca, Cudré-Mauroux, Philippe
Collaborative Knowledge Graph platforms allow humans and automated scripts to collaborate in creating, updating and interlinking entities and facts. To ensure both the completeness of the data as well as a uniform coverage of the different topics, it
Externí odkaz:
http://arxiv.org/abs/1909.01109