论文札记之 - A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval...
前言 在统计自然语言处理任务中，最基础也是最关键的一步是将人能够理解的文本编码为机器能够计算的向量，并且在编码过程中，尽量保留原有的语法和语义特征。语法特征包括词法：形容词，动词，名词等；句法：主谓宾，定状补；语义角色：如施事、受事、与事。语义特征则是需要结合上下文推到出的文本真正的含义，对歧义句式进行更严格的分化，可以解释某些同形格式产生歧义的原因。这篇 paper 讨论的是如何利用卷积
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相关文章
- 1. Deep Learning for Information Retrieval
- 2. 《A Joint Neural Model for Information Extraction with Global Features》论文笔记
- 3. 论文导读｜《Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model》
- 4. 【论文笔记】Semantic Localization through Propagation of Scene Information in a Hierarchical Model
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