NLP-Progress记录NLP最新数据集、论文和代码: 助你紧跟NLP前沿 NLP-Progress 同时涵盖了传统的NLP任务,如依赖解析和词性标注,和一些新的任务,如阅读理解和自然语言推理。它的不仅为读者提供这些任务的 baseline 和 标准数据集,还记录了这些问题的state-of-the-art。 下面小编简单列举了几个NLP-Progress 记录的任务: ...
1.NLP Progress 本文对记录自然语言处理(NLP)领域的新进展,并概述最常见的NLP任务及其相应数据集的新技术,涵盖了目前NLP领域常用任务的最佳实验 结果和数据集资源。 新智元介绍:新智元专栏 原文介绍:nlpprogress.com/ Github链接:github.com/sebastianrud 2.State of the Art 来自MIT 和 UNAM 的四名学生构建了一个...
项目地址:https://github.com/sebastianruder/NLP-progress 参考博客:http://ruder.io/tracking-progress-nlp/ 目录(任务和对应数据集) 1.CCG 超级标记 CCGBank 2.分块 Penn Treebank 3.选区解析 Penn Treebank 4.指代消歧 CoNLL 2012 5.依存解析 Penn Treebank 6.对话 第二对话状态追踪挑战赛 7.域适应 多...
Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks. - sebastianruder/NLP-progress
项目地址:https://github.com/sebastianruder/NLP-progress参考博客:http://ruder.io/tracking-progress-nlp/ 目录(任务和对应数据集)1.CCG 超级标记 CCGBank 2.分块 Penn Treebank 3.选区解析 Penn Treebank 4.指代消歧 CoNLL 2012 5.依存解析 Penn Treebank 6.对话 第二对话状态追踪挑战赛 7.域适应 多...
Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.HindiChunkingModelDev accuracyTest F1Paper / SourceCode Dalal et al. (2006) 87.40 82.40 Hindi Part-of-Speech Tagging and Chunking: A Ma...
Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.Nepali Machine Translation ModelBLEUPaper / SourceCode Guzman et al. (2019) 21.5 (NE-EN) & 8.8 (EN-NE) The FLoRes Evaluation ...
项目地址:https://github.com/sebastianruder/NLP-progress 参考博客:http://ruder.io/tracking-progress-nlp/ 目录(任务和对应数据集) 1.CCG 超级标记 CCGBank 2.分块 Penn Treebank 3.选区解析 Penn Treebank 4.指代消歧 CoNLL 2012 5.依存解析
AGNEWS, DBPedia, TREC: http://nlpprogress.com/english/text_classification.html 中文:https://github.com/SophonPlus/ChineseNlpCorpus 文本分类模型 什么是一个分类器?一个从输入(inputs)特征x投射到标注y的函数一个简单的分类器:对于输入x,给每一个label y打一个分数,score(x, y, w),其中w是...
In this paper, we will review the latest progress in the neural network-based NLP framework (neural NLP) from three perspectives: modeling, learning, and reasoning. In the modeling section, we will describe several fundamental neural network-based modeling paradigms, such as word embedding, ...