Titlebook: Computational Linguistics and Intelligent Text Processing; 17th International C Alexander Gelbukh Conference proceedings 2018 Springer Inte

ISH

書目名稱Computational Linguistics and Intelligent Text Processing影響因子(影響力)




書目名稱Computational Linguistics and Intelligent Text Processing影響因子(影響力)學(xué)科排名




書目名稱Computational Linguistics and Intelligent Text Processing網(wǎng)絡(luò)公開度




書目名稱Computational Linguistics and Intelligent Text Processing網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Computational Linguistics and Intelligent Text Processing被引頻次




書目名稱Computational Linguistics and Intelligent Text Processing被引頻次學(xué)科排名




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書目名稱Computational Linguistics and Intelligent Text Processing年度引用學(xué)科排名




書目名稱Computational Linguistics and Intelligent Text Processing讀者反饋




書目名稱Computational Linguistics and Intelligent Text Processing讀者反饋學(xué)科排名

octogenarian

A Roadmap Towards Machine Intelligenceerties these machines should have, focusing in particular on . and .. We discuss a simple environment that could be used to incrementally teach a machine the basics of natural-language-based communication, as a prerequisite to more complex interaction with human users. We also present some conjectur

Torrid

AXIS

Persianp: A Persian Text Processing Toolboxprovides fundamental Persian text processing steps includes several modules. In developing some modules of the toolbox such as normalizer, tokenizer, sentencizer, stop word detector, and Part-Of-Speech tagger previous studies are applied. In other modules i.e. Persian lemmatizer and NP chunker, new

松馳

gene-therapy

gene-therapy

沐浴

A New Language Model Based on Possibility Theorys paper, we propose a new language modeling approach based on the possibility theory. Our goal is to suggest a method for estimating the possibility of a word-sequence and to test this new approach in a machine translation system. We propose a word-sequence possibilistic measure, which can be estima

Fraudulent

Combining Discrete and Neural Features for Sequence Labelingwith discrete features, neural models have two main advantages. First, they take low-dimensional, real-valued embedding vectors as inputs, which can be trained over large raw data, thereby addressing the issue of feature sparsity in discrete models. Second, deep neural networks can be used to automa

Employee

New Recurrent Neural Network Variants for Sequence Labelingwe compare them to the more traditional RNN architectures of Elman and Jordan. We explain in details the advantages of these new variants of RNNs with respect to Elman’s and Jordan’s RNN. We evaluate all models, either new or traditional, on three different tasks: POS-tagging of the French Treebank,