<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[浩比的专栏]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/MaybeForever</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; MaybeForever]]></copyright><item><title><![CDATA[Android学习笔记：Android10.0.+版本下ContentProvider的工作过程（流程分析文档）]]></title><link>https://blog.youkuaiyun.com/MaybeForever/article/details/109241705</link><guid>https://blog.youkuaiyun.com/MaybeForever/article/details/109241705</guid><author>MaybeForever</author><pubDate>Sat, 24 Oct 2020 10:54:16 +0800</pubDate><description><![CDATA[一、	源代码版本
源代码版本：Source for Android 30 （Android10.0+®）
内容提供器的用法一般有两种，一种是使用现有的内容提供器来读取和操作相应程序中的数据，另一种是创建自己的内容提供器给我们程序提供外部数据访问。
首先，我们编写一个ContentProviderDemo，该工程用来读取系统的联系人信息，该工程下有一个Activity即MainActivity，还有一个布局文件activity_main.xml。
二、	ContentProvider的启动过程

当Conte]]></description><category></category></item><item><title><![CDATA[Android学习笔记：Android10.0.+版本下Service的工作过程（流程分析文档）]]></title><link>https://blog.youkuaiyun.com/MaybeForever/article/details/108714002</link><guid>https://blog.youkuaiyun.com/MaybeForever/article/details/108714002</guid><author>MaybeForever</author><pubDate>Tue, 22 Sep 2020 22:44:25 +0800</pubDate><description><![CDATA[一、源代码版本

源代码版本：Source for Android 30 （Android10.0+(R)）

Service有两种工作状态，一种是启动状态，主要用于执行后台计算；另一种是绑定状态，主要用于和其他人组件和Service交互。需要注意的是，Service的这两种状态是可以共存的，即Service即可以处于启动状态又可以处于绑定状态。

二、Service的启动状态



首先，我们编写一个ServiceDemo，该工程下有一个Activity即MainActivity，和一个Service即M]]></description><category></category></item><item><title><![CDATA[Android学习笔记：Android10.0.+版本下BroadcastReceiver的工作过程（流程分析文档）]]></title><link>https://blog.youkuaiyun.com/MaybeForever/article/details/108736234</link><guid>https://blog.youkuaiyun.com/MaybeForever/article/details/108736234</guid><author>MaybeForever</author><pubDate>Tue, 22 Sep 2020 22:44:17 +0800</pubDate><description><![CDATA[BroadcastReceiver的工作过程，主要包含两方面内容，一是广播的注册过程，二是广播的发送和接收过程。而广播的注册分为静态注册和动态注册，静态注册是通过AndroidManifest.xml配置来完成的，具体来说就是有PMS（PackageManagerService）来完成整个注册过程。我们采用动态注册的方式来分析广播。

首先，创建BroadcastReceiverDemo工程，该工程包含两个类文件，分别是MainActivity和MyReceiver，以及布局文件activity_main.]]></description><category></category></item><item><title><![CDATA[Android学习笔记：Android10.0.+版本下Activity的工作过程（流程分析文档）]]></title><link>https://blog.youkuaiyun.com/MaybeForever/article/details/108615862</link><guid>https://blog.youkuaiyun.com/MaybeForever/article/details/108615862</guid><author>MaybeForever</author><pubDate>Fri, 18 Sep 2020 23:10:05 +0800</pubDate><description><![CDATA[一、源代码版本

源代码版本：Source for Android 30 （Android10.0+(R)）

二、流程分析

Activity的启动由Intent触发，Intent可以分为显式Intent和隐式Intent。

首先，我们编写一个ActivityDemo，该工程下有两个Activity，分别为MainActivity和SecondActivity，还有两个布局文件，分别为activity_main.xml和activity_second.xml。

从入口方法startActivity()]]></description><category></category></item><item><title><![CDATA[Linux基础知识]]></title><link>https://blog.youkuaiyun.com/MaybeForever/article/details/107902491</link><guid>https://blog.youkuaiyun.com/MaybeForever/article/details/107902491</guid><author>MaybeForever</author><pubDate>Thu, 03 Sep 2020 19:12:48 +0800</pubDate><description><![CDATA[一、文件结构
Linux 文件系统是一个目录树的结构，文件系统结构从一个根目录开始，根目录下可以有任意多个文件和子目录，子目录中又可以有任意多个文件和子目录。

bin   存放二进制可执行文件(ls,cat,mkdir等)
boot  存放用于系统引导时使用的各种文件
dev   用于存放设备文件
etc    存放系统配置文件
home  存放所有用户文件的根目录
lib    存放跟文件系统中的程序运行所需要的共享库及内核模块
mnt   系统管理员安装临时文件系统的安装点
opt    额外安装的可]]></description><category></category></item><item><title><![CDATA[Java学习手册：BigInteger和BigDecimal]]></title><link>https://blog.youkuaiyun.com/MaybeForever/article/details/107902028</link><guid>https://blog.youkuaiyun.com/MaybeForever/article/details/107902028</guid><author>MaybeForever</author><pubDate>Sun, 09 Aug 2020 23:04:41 +0800</pubDate><description><![CDATA[一、BigInteger（高精度整型）
package com.haobi;

import java.math.BigInteger;
/*
 * BigInteger:可以让超过Integer范围内的数进行运算
 */
public class Test1 {
	public static void main(String[] args) {
		BigInteger b1 = new BigInteger("111111111111111111111111111111");
		BigInteger ]]></description><category></category></item><item><title><![CDATA[文本相似度：Bilateral Multi-Perspective Matching for Natural Language Sentences]]></title><link>https://blog.youkuaiyun.com/MaybeForever/article/details/103774890</link><guid>https://blog.youkuaiyun.com/MaybeForever/article/details/103774890</guid><author>MaybeForever</author><pubDate>Tue, 31 Dec 2019 17:23:01 +0800</pubDate><description><![CDATA[文章地址：https://arxiv.org/pdf/1702.03814.pdf
文章标题：Bilateral Multi-Perspective Matching for Natural Language Sentences（自然语言句子的双边多视角匹配）IJCAI2017
文章代码：https://github.com/zhiguowang/BiMPM
实验数据集：the Quora Que...]]></description><category></category></item><item><title><![CDATA[文本相似度：Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, NLI and QA]]></title><link>https://blog.youkuaiyun.com/MaybeForever/article/details/103672127</link><guid>https://blog.youkuaiyun.com/MaybeForever/article/details/103672127</guid><author>MaybeForever</author><pubDate>Mon, 23 Dec 2019 20:36:32 +0800</pubDate><description><![CDATA[文章地址：https://arxiv.org/pdf/1806.04330.pdf
文章标题：Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering（用于意译识别、语义文本相似性、自然语言推...]]></description><category></category></item><item><title><![CDATA[文本相似度：Distributed Representations of Sentences and Documents]]></title><link>https://blog.youkuaiyun.com/MaybeForever/article/details/103615624</link><guid>https://blog.youkuaiyun.com/MaybeForever/article/details/103615624</guid><author>MaybeForever</author><pubDate>Thu, 19 Dec 2019 16:22:20 +0800</pubDate><description><![CDATA[文章地址：https://arxiv.org/pdf/1405.4053.pdf
文章标题：Distributed Representations of Sentences and Documents（句子和文档的分布式表示）ICML2014
Abstract
许多机器学习算法要求输入用定长特征向量表示。当涉及到文本时，最常见的固定长度特性之一是词袋。尽管它们很受欢迎，但bag-of-words特...]]></description><category></category></item><item><title><![CDATA[文本相似度：A Survey of Text Similarity Approaches]]></title><link>https://blog.youkuaiyun.com/MaybeForever/article/details/103606371</link><guid>https://blog.youkuaiyun.com/MaybeForever/article/details/103606371</guid><author>MaybeForever</author><pubDate>Thu, 19 Dec 2019 10:40:07 +0800</pubDate><description><![CDATA[文章地址：https://research.ijcaonline.org/volume68/number13/pxc3887118.pdf
文章标题：A Survey of Text Similarity Approaches（文本相似性方法的调查）2013
ABSTRACT
在信息检索、文档聚类、词义消歧、自动作文评分、简答题评分、机器翻译和文本摘要等各种任务中，测量单词、句子、段落和文档之间的...]]></description><category></category></item><item><title><![CDATA[文本相似度：Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Networks]]></title><link>https://blog.youkuaiyun.com/MaybeForever/article/details/103594895</link><guid>https://blog.youkuaiyun.com/MaybeForever/article/details/103594895</guid><author>MaybeForever</author><pubDate>Wed, 18 Dec 2019 21:20:41 +0800</pubDate><description><![CDATA[文章地址：https://www.aclweb.org/anthology/D15-1181.pdf
文章标题：Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Networks（基于卷积神经网络的多视角句子相似度建模）EMNLP2015
Abstract
句子相似度建模由于语言表达的模糊性和可变性而变...]]></description><category></category></item><item><title><![CDATA[文本相似度：Sentence Similarity Learning by Lexical Decomposition and Composition]]></title><link>https://blog.youkuaiyun.com/MaybeForever/article/details/103575944</link><guid>https://blog.youkuaiyun.com/MaybeForever/article/details/103575944</guid><author>MaybeForever</author><pubDate>Tue, 17 Dec 2019 22:09:00 +0800</pubDate><description><![CDATA[文章地址：https://arxiv.org/pdf/1602.07019.pdf
文章标题：Sentence Similarity Learning by Lexical Decomposition and Composition（通过词汇分解和组合进行句子相似度学习）COLING2016
Abstract
传统的句子相似度分析方法只关注两个输入句子中相似的部分，而忽略了不相似的部分，这往往会给...]]></description><category></category></item><item><title><![CDATA[文本相似度：Multi-Modal Similarity Metric Learning for Answer Selection]]></title><link>https://blog.youkuaiyun.com/MaybeForever/article/details/103569979</link><guid>https://blog.youkuaiyun.com/MaybeForever/article/details/103569979</guid><author>MaybeForever</author><pubDate>Mon, 16 Dec 2019 21:52:16 +0800</pubDate><description><![CDATA[文章地址：https://arxiv.org/pdf/1604.05519v1.pdf
文章标题：Multi-Modal Similarity Metric Learning for Answer Selection（答案选择的多模态相似度度量学习）2018
Abstract
近年来，基于分布式词表示的人工神经网络的研究工作极大地提高了答案选择问题的性能。然而，以往的研究大多采用深度学习的方法(如...]]></description><category></category></item><item><title><![CDATA[实体-关系联合抽取：入门资料汇总]]></title><link>https://blog.youkuaiyun.com/MaybeForever/article/details/103454470</link><guid>https://blog.youkuaiyun.com/MaybeForever/article/details/103454470</guid><author>MaybeForever</author><pubDate>Mon, 09 Dec 2019 11:31:52 +0800</pubDate><description><![CDATA[写在前面：实体-关系联合抽取这个方向刚刚入门就要换方向了，很不舍，因此将自己这段时间看的一些入门资料分享出来，供大家学习，希望能起到一些抛砖引玉的作用。如有一丝帮助到您，请点赞支持，谢谢！！！
一、知识图谱概述

http://ex.cssn.cn/glx_tsqbx/201710/t20171030_3685466_2.shtml
https://www.jiqizhixin.com/artic...]]></description><category></category></item><item><title><![CDATA[实体-关系联合抽取：Table Filling Multi-Task Recurrent Neural Network for Joint Entity and Relation Extraction]]></title><link>https://blog.youkuaiyun.com/MaybeForever/article/details/103280346</link><guid>https://blog.youkuaiyun.com/MaybeForever/article/details/103280346</guid><author>MaybeForever</author><pubDate>Wed, 27 Nov 2019 20:05:26 +0800</pubDate><description><![CDATA[文章地址：https://www.aclweb.org/anthology/C16-1239.pdf
文章标题：Table Filling Multi-Task Recurrent Neural Network for Joint Entity and Relation Extraction（表填充多任务递归神经网络用于联合实体和关系提取）COLING2016
Abstract
摘要提出了一种基于...]]></description><category></category></item><item><title><![CDATA[实体-关系联合抽取：Incorporating Copying Mechanism in Sequence-to-Sequence Learning]]></title><link>https://blog.youkuaiyun.com/MaybeForever/article/details/103125082</link><guid>https://blog.youkuaiyun.com/MaybeForever/article/details/103125082</guid><author>MaybeForever</author><pubDate>Mon, 18 Nov 2019 17:36:07 +0800</pubDate><description><![CDATA[文章标题：https://www.aclweb.org/anthology/P16-1154.pdf
文章题目：Incorporating Copying Mechanism in Sequence-to-Sequence Learning（将复制机制整合到序列到序列的学习中）ACL2016
Abstract
我们解决了一个重要的问题，在序列到序列(Seq2Seq)学习中被称为复制，其中输入序列中...]]></description><category></category></item><item><title><![CDATA[实体-关系联合抽取：CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases]]></title><link>https://blog.youkuaiyun.com/MaybeForever/article/details/103047804</link><guid>https://blog.youkuaiyun.com/MaybeForever/article/details/103047804</guid><author>MaybeForever</author><pubDate>Wed, 13 Nov 2019 13:57:36 +0800</pubDate><description><![CDATA[文章地址：https://arxiv.org/pdf/1610.08763.pdf
文章标题：CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases（联合提取类型化实体和与知识库的关系）WWW2017
文章源代码：https://github.com/INK-USC/DS-RelationExtra...]]></description><category></category></item><item><title><![CDATA[实体关系联合抽取：Supertagging with LSTMs]]></title><link>https://blog.youkuaiyun.com/MaybeForever/article/details/103024197</link><guid>https://blog.youkuaiyun.com/MaybeForever/article/details/103024197</guid><author>MaybeForever</author><pubDate>Tue, 12 Nov 2019 12:01:31 +0800</pubDate><description><![CDATA[文章地址：https://www.aclweb.org/anthology/N16-1027.pdf
文章标题：Supertagging with LSTMs（超标注LSTMs）NNACL2016
模型和超标签代码：https://bitbucket.org/ashish_vaswani/lstm_supertagger
Abstract
在本文中，我们介绍了CCG超标记和解析的最新性能。我们的模...]]></description><category></category></item><item><title><![CDATA[实体-关系联合抽取：Neural Architectures for Named Entity Recognition]]></title><link>https://blog.youkuaiyun.com/MaybeForever/article/details/103010982</link><guid>https://blog.youkuaiyun.com/MaybeForever/article/details/103010982</guid><author>MaybeForever</author><pubDate>Mon, 11 Nov 2019 16:26:03 +0800</pubDate><description><![CDATA[文章地址：https://arxiv.org/pdf/1603.01360.pdf
文章标题：Neural Architectures for Named Entity Recognition（命名实体识别的神经结构）ACL2016
代码地址：

1、LSTM-CRF：https://github.com/glample/tagger
2、Stack-LSTM：https://github.com...]]></description><category></category></item><item><title><![CDATA[实体-关系联合抽取：Incremental Joint Extraction of Entity Mentions and Relations]]></title><link>https://blog.youkuaiyun.com/MaybeForever/article/details/102972330</link><guid>https://blog.youkuaiyun.com/MaybeForever/article/details/102972330</guid><author>MaybeForever</author><pubDate>Fri, 08 Nov 2019 15:48:57 +0800</pubDate><description><![CDATA[论文地址：https://www.aclweb.org/anthology/P14-1038.pdf
文章标题：Incremental Joint Extraction of Entity Mentions and Relations（增量联合提取提及的实体和关系）ACL2014
Abstract
提出了一种增量联合框架，利用结构感知器和有效的集束搜索同时提取提及的实体和关系。新框架采用了基于半马...]]></description><category></category></item></channel></rss>