SYSTEM AND METHOD FOR SEMANTIC-LEVEL SENTIMENT ANALYSIS OF TEXT
    1.
    发明申请
    SYSTEM AND METHOD FOR SEMANTIC-LEVEL SENTIMENT ANALYSIS OF TEXT 审中-公开
    用于语义水平分析的系统和方法

    公开(公告)号:WO2015053607A1

    公开(公告)日:2015-04-16

    申请号:PCT/MY2014/000182

    申请日:2014-06-12

    Applicant: MIMOS BERHAD

    CPC classification number: G06F17/2785

    Abstract: The present invention relates to a system and method for semantic level sentiment analysis. The system (100) comprises of a graph generator component (10), a semantic sentiment analyser component (20), a sentiment processor component (30), a sentiment dictionary (40), a sentiment taxonomy (50), a semantic sentiment patterns repository (60) and a propagation rules repository (70). The system (100) accepts text data as input and analyses sentiment in the text. The method enables semantically valid sentiment in terms of the entire text as well as the individual entities in the text.

    Abstract translation: 本发明涉及语义层面情绪分析的系统和方法。 系统(100)包括图形生成器组件(10),语义情绪分析器组件(20),情感处理器组件(30),情感词典(40),情绪分类(50),语义情绪模式 存储库(60)和传播规则存储库(70)。 系统(100)接受文本数据作为输入并分析文本中的情绪。 该方法在整个文本以及文本中的各个实体方面实现了语义上有效的观点。

    A METHOD AND SYSTEM FOR AUTOMATED ENTITY RECOGNITION
    2.
    发明申请
    A METHOD AND SYSTEM FOR AUTOMATED ENTITY RECOGNITION 审中-公开
    一种用于自动实体识别的方法和系统

    公开(公告)号:WO2015080558A1

    公开(公告)日:2015-06-04

    申请号:PCT/MY2014/000153

    申请日:2014-05-29

    Applicant: MIMOS Berhad

    CPC classification number: G06F17/278

    Abstract: The present invention provides a system for extracting concept and named-entities from a text-containing document. An entity recognition eagine (102) is provided to process an entity with a Rule-based Named-Entity Recognition (NER) (122), a Natural-Language-Processing (NLP) based NER (124), and a knowledge-based NER (126). The NERs are further scored and weighted, wherein the highest weighted score will be taken. A method thereof is also provided.

    Abstract translation: 本发明提供了一种用于从包含文本的文档中提取概念和命名实体的系统。 提供实体识别eagine(102)以处理具有基于规则的命名实体识别(NER)(122),基于自然语言处理(NLP)的NER(124)和基于知识的NER (126)。 NER进一步得分和加权,其中将采用最高加权分数。 还提供了其方法。

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