实体关系抽取是信息抽取领域中的重要研究课题。
Entity Relation Extraction is an important research field in Information Extraction.
由于网络构建中边的重要性，本文主要对实体关系抽取进行研究。
Owing to the important of edges in the network modeling, we mainly research the entity relation extraction.
本文提出了一个基于网页实体关系抽取与融合的企业竞争情报获取系统框架。
In this paper, we present a system to acquire enterprise competitive intelligence, which is based on the entity relationship extraction and fusion of Web pages.
同时针对单文档以及稀疏文档集，本文实现了基于事件框架的实体关系抽取，以抽取用户指定的特殊实体关系。
Meanwhile, to analyzing sparse dataset, we implement the relation extract method based on event frame to extract the special relations which users set.
识别句子中实体关系是信息抽取的重要技术。
Identifying entity relation of sentence is important technology of information extraction.
实验证明srv算法用于命名实体关系的抽取是成功和有效的。
The experiment demonstrates that SRV is successful and effective for the named entity relation extraction.
这些地图抽取和定义信息模型、种类和作者、概念以及其它信息实体之间的关系。
These maps extract and define information patterns, categories, and relationships among authors, concepts, or other information entities.
关系抽取是文本挖掘的一项重要研究内容，它能够反映命名实体之间的关系，有助于发现隐含在大量数据和文本中的知识。
Relation extraction is an important task in text mining, it can reflect the relationship between the named entities and is helpful to find implicit knowledge in the substantial data and text.
通过以上两种方法，使命名实体之间关系抽取结果的性能大大提高。
By doing these, the performance of named entity relation extraction was enhanced greatly.
我们利用条件随机场模型抽取领域实体对象，并将其应用于比较句识别和比较关系抽取中，取得了良好的实验效果。
We use CRF model to extract domain entities which are applied to identify comparative sentences and mine comparative relation with good results.
我们利用条件随机场模型抽取领域实体对象，并将其应用于比较句识别和比较关系抽取中，取得了良好的实验效果。
We use CRF model to extract domain entities which are applied to identify comparative sentences and mine comparative relation with good results.
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