MAG: A Multilingual, Knowledge-base Agnostic and Deterministic Entity Linking Approach

Entity linking has recently been the subject of a significant body of research. Currently, the best performing approaches rely on trained mono-lingual models. Porting these approaches to other languages is consequently a difficult endeavor as it requires corresponding training data and retraining of the models. We address this drawback by presenting a novel multilingual, knowledge-based agnostic and deterministic approach to entity linking, dubbed MAG. MAG is based on a combination of context-based retrieval on structured knowledge bases and graph algorithms. We evaluate MAG on 23 data sets and in 7 languages. Our results show that the best approach trained on English datasets (PBOH) achieves a micro F-measure that is up to 4 times worse on datasets in other languages. MAG, on the other hand, achieves state-of-the-art performance on English datasets and reaches a micro F-measure that is up to 0.6 higher than that of PBOH on non-English languages.Comment: Accepted in K-CAP 2017: Knowledge Capture Conferenc

Interleaved Parton Showers and Tuning Prospects

General-purpose Monte Carlo event generators have become important tools in particle physics, allowing the simulation of exclusive hadronic final states. In this article we examine the Pythia 8 generator, in particular focusing on its parton-shower algorithms. Some relevant new additions to the code are introduced, that should allow for a better description of data. We also implement and compare with 2 to 3 real-emission QCD matrix elements, to check how well the shower algorithm fills the phase space away from the soft and collinear regions. A tuning of the generator to Tevatron data is performed for two PDF sets and the impact of first new LHC data is examined

Factual and Personalized Recommendations using Language Models and Reinforcement Learning

Recommender systems (RSs) play a central role in connecting users to content, products, and services, matching candidate items to users based on their preferences. While traditional RSs rely on implicit user feedback signals, conversational RSs interact with users in natural language. In this work, we develop a comPelling, Precise, Personalized, Preference-relevant language model (P4LM) that recommends items to users while putting emphasis on explaining item characteristics and their relevance. P4LM uses the embedding space representation of a user's preferences to generate compelling responses that are factually-grounded and relevant w.r.t. the user's preferences. Moreover, we develop a joint reward function that measures precision, appeal, and personalization, which we use as AI-based feedback in a reinforcement learning-based language model framework. Using the MovieLens 25M dataset, we demonstrate that P4LM delivers compelling, personalized movie narratives to users

A proposed new policy for planetary protection

A critical review of the present policy was conducted with emphasis on its application to future planetary exploration. The probable impact of recent data on the implementation of the present policy was also assessed. The existing policy and its implementation were found to: be excessive for certain missions (e.g., Voyager), neglect the contamination hazard posed by the bulk constituent organics of spacecraft, be ambiguous for certain missions (e.g., Pioneer Venus), and treat all extraterrestrial sample return missions alike. The major features of the proposed policy are planet/mission combinations, a qualitative top level statement, and implementation by exception rather than rule. The concept of planet/mission categories permits the imposition of requirements according to both biological interest in the target planet and the relative contamination hazard of the mission type