Progress in Neural NLP: Modeling, Learning, and Reasoning
نویسندگان
چکیده
منابع مشابه
Recent Progress in Deep Learning for NLP
Neural networkbased methods have been viewed as one of the major driving force in the recent development of natural language processing (NLP). We all have witnessed with great excitement how this subfield advances: new ideas emerge at an unprecedented speed and old ideas resurge in unexpected ways. In a nutshell, there are two major trends: ● Ideas and techniques from other fields of machine l...
متن کاملInvited Keynote Talk Modeling Reasoning Mechanisms by Neural-Symbolic Learning
Currently, neural-symbolic integration covers – at least in theory – a whole bunch of types of reasoning: neural representations (and partially also neural-inspired learning approaches) exist for modeling propositional logic (programs), whole classes of manyvalued logics, modal logic, temporal logic, and epistemic logic, just to mention some important examples [2,4]. Besides these propositional...
متن کاملscour modeling piles of kambuzia industrial city bridge using hec-ras and artificial neural network
today, scouring is one of the important topics in the river and coastal engineering so that the most destruction in the bridges is occurred due to this phenomenon. whereas the bridges are assumed as the most important connecting structures in the communications roads in the country and their importance is doubled while floodwater, thus exact design and maintenance thereof is very crucial. f...
compare learning function in neural networks for river runoff modeling
accurate prediction of river flow is one of the most important factors in surface water recourses management especially during floods and drought periods. in fact deriving a proper method for flow forecasting is an important challenge in water resources management and engineering. although, during recent decades, some black box models based on artificial neural networks (ann), have been develop...
متن کاملVisualizing and Understanding Neural Models in NLP
While neural networks have been successfully applied to many NLP tasks the resulting vector-based models are very difficult to interpret. For example it’s not clear how they achieve compositionality, building sentence meaning from the meanings of words and phrases. In this paper we describe four strategies for visualizing compositionality in neural models for NLP, inspired by similar work in co...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Engineering
سال: 2020
ISSN: 2095-8099
DOI: 10.1016/j.eng.2019.12.014