Discovering Semantic Patterns of a Word from Texts

机译：从文本中发现单词的语义模式

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This paper describes a methodology for identifying semantic patterns of a word from texts by creating a sequence of coexisting triples that consistently carries the semantics of the word and developing an algorithm that finds the most important information among these triples in order to make a decision. This method is evaluated by words having different categories such as nouns, adjectives, or verbs. The results show that our method is effective and surpass the state-of-art accuracy level.

机译：本文介绍了一种通过创建一系列共存三维来识别文本的语义模式的方法，该三个是始终携带单词的语义和开发这些三维之间最重要信息的算法，以便做出决定。该方法由具有不同类别的单词评估，例如名词，形容词或动词。结果表明，我们的方法是有效和超越最先进的准确度水平。

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《International Conference on Artificial Intelligence and Pattern Recognition》|2010年||共7页
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Minhua Huang; Robert M. Haralick;
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中图分类 TP18-53;
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4. Discovering Semantic Patterns of a Word from Texts [C] . Minhua Huang, Robert M. Haralick International Conference on Artificial Intelligence and Pattern Recognition . 2010

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5. ANSWER: A Cognitively-Inspired System for the Unsupervised Detection of Semantically Salient Words in Texts [D] . Candadai Vasu, Madhavun 2015

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7. Figure 1: (A) Example of a text-based forma mentis network. A TFMN can be represented either as an edge-coloured graph or as a multiplex network. Positive (negative) words are highlighted in cyan (red). Neutral words are in black. Syntactic links between positive (negative) words are highlighted in cyan (red) too. Syntactic links between positive and negative concepts are in purple. All semantic links of meaning overlap are highlighted in green. (B) Infographics about how a TFMN is assembled. Individuals organise their knowledge and emotional perception of the real world in their mental lexicon (comic clouds). [O] . -1

机译：图1：（a）基于文本的Forma Mentis网络示例。 TFMN可以用作边缘彩色图形或作为多路复用网络表示。在青色（红色）突出显示正（负）单词。中立词是黑色的。在青色（红色）突出显示正（否定）单词之间的句法链接。正面和消极概念之间的句法链接在紫色。含义重叠的所有语义链接都以绿色突出显示。（b）关于TFMN如何组装的信息图表。个人在他们的精神词典（漫画云）中对现实世界组织了他们的知识和情感感知。

Discovering Semantic Patterns of a Word from Texts

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