a)以当前的精度和广泛使用的矢量空间模型的两倍精度执行
b)在识别相似词语时“相当准确”.
c)使microsearch更准确
这是一个很好的interview.
不幸的是,我还没有找到发表的论文,但是,从我几年前的图形模型和机器学习课程中记得的那些,我认为我们应该能够从他的submision摘要中重建它,以及他是什么在采访中谈到它.
来自采访：
Some searches find words that appear in similar contexts. That’s
pretty good, but that’s following the relationships to the first
degree. My algorithm tries to follow connections further. Connections
that are close are deemed more valuable. In theory, it follows
connections to an infinite degree.
摘要将其置于上下文中：
A novel information retrieval algorithm called “Apodora” is introduced,
using limiting powers of Markov chain-like matrices to determine
models for the documents and making contextual statistical inferences
about the semantics of words. The system is implemented and compared
to the vector space model. Especially when the query is short, the
novel algorithm gives results with approximately twice the precision
and has interesting applications to microsearch.
我觉得有人知道马尔可夫链式矩阵或信息检索会立即意识到他正在做的事情.
那么：他在做什么？
通过使用像“上下文”这样的词语以及他引入统计依赖性的二级秩序的事实,我怀疑他正在做一些与本文概述的LDA-HMM方法相关的事情：Griffiths,T.,Steyvers,M. ,Blei,D.,& Tenenbaum,J.(2005).集成主题和语法.神经信息处理系统的进展.由于模型平均,搜索的分辨率存在一些固有的限制.但是,我很羡慕在17岁的时候做这样的事情,我希望他能做出一些独立的事情,至少会逐渐好转.即使是同一主题的不同方向也会非常酷.