Aiming at the task of Chinese Prepositional Phrase Identification, we proposed that word and part-of-speech are the main factors which construct a feature space of ME model. And an algorithm is presented to automatically acquire a feature set.
因此在本文中针对汉语介词短语的划分和识别，提出了词、词性标注是构成介词短语识别的主要因素，并根据这几种因素结合介词短语的语用特征来确定最大熵模型的特征空间，从中自动获取介词短语识别的有效特征集合。
参考来源 - 基于最大熵的汉语介词短语自动识别·2,447,543篇论文数据，部分数据来源于NoteExpress
The first step is candidate phrase identification, which is based on statistical rules and a Length Standard Deviation measurement.
前者使用了统计规则和长度-标准差模型，后者采用感知器算法及共现模型实现。
The identification of English basic noun phrase is an important sub-task in natural language processing.
基本名词短语识别是自然语言处理领域的非常重要的子任务。
It simultaneously solves ambiguous phrase boundary resolution and unknown word identification problems.
它同时解决了模糊的短语边界的问题和未登录词识别问题。
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