35,493 research outputs found
Named Entity Extraction and Disambiguation: The Reinforcement Effect.
Named entity extraction and disambiguation have received much attention in recent years. Typical fields addressing these topics are information retrieval, natural language processing, and semantic web. Although these topics are highly dependent, almost no existing works examine this dependency. It is the aim of this paper to examine the dependency and show how one affects the other, and vice versa. We conducted experiments with a set of descriptions of holiday homes with the aim to extract and disambiguate toponyms as a representative example of named entities. We experimented with three approaches for disambiguation with the purpose to infer the country of the holiday home. We examined how the effectiveness of extraction influences the effectiveness of disambiguation, and reciprocally, how filtering out ambiguous names (an activity that depends on the disambiguation process) improves the effectiveness of extraction. Since this, in turn, may improve the effectiveness of disambiguation again, it shows that extraction and disambiguation may reinforce each other.\u
Only Relevant Information Matters: Filtering Out Noisy Samples to Boost RL
In reinforcement learning, policy gradient algorithms optimize the policy
directly and rely on sampling efficiently an environment. Nevertheless, while
most sampling procedures are based on direct policy sampling, self-performance
measures could be used to improve such sampling prior to each policy update.
Following this line of thought, we introduce SAUNA, a method where
non-informative transitions are rejected from the gradient update. The level of
information is estimated according to the fraction of variance explained by the
value function: a measure of the discrepancy between V and the empirical
returns. In this work, we use this metric to select samples that are useful to
learn from, and we demonstrate that this selection can significantly improve
the performance of policy gradient methods. In this paper: (a) We define
SAUNA's metric and introduce its method to filter transitions. (b) We conduct
experiments on a set of benchmark continuous control problems. SAUNA
significantly improves performance. (c) We investigate how SAUNA reliably
selects samples with the most positive impact on learning and study its
improvement on both performance and sample efficiency.Comment: Accepted at IJCAI 202
AI Dining Suggestion App
Trying to decide what to eat can sometimes be challenging and time-consuming for people. Google and Yelp have large scale data sets of restaurant information as well as Application Program Interfaces (APIs) for using them. This restaurant data includes time, price range, traffic, temperature, etc. The goal of this project is to build an app that eases the process of finding a restaurant to eat. This app has a Tinder-like user friendly User Interface (UI) design to change the common way that lists of restaurants are presented to users on mobile apps. It also uses the help of Artificial Intelligence (AI) with neural networks to train both supervised and unsupervised learning models that can learn from one\u27s dining pattern over time to make better suggestions at any time
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