人际互动模式挖掘方法的比较分析

S. Uma, J. Suguna
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引用次数: 0

摘要

自然语言处理中的意见挖掘和情感分析具有挑战性,因为它们需要深入理解。理解涉及能够区分显性和隐性、规则和不规则、句法和语义语言规则的事实的方法。面向自然语言处理和情感分析的研究有许多悬而未决的问题,如共指消解、否定处理、回指消解、命名实体识别和词义消歧。本文提出了一种能够挖掘人类交互模式的优化偏祖先图(O-PAG),并将其与现有的基于树的模式挖掘方法进行了比较。实验结果暴露于频繁的交互次数和执行时间。结果表明,在使用O-PAG方法的基础上,整体性能可以得到显著的提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
COMPARATIVE ANALYSIS OF HUMAN INTERACTION PATTERN MINING APPROACHES
Opinion Mining and Sentiment Analysis in Natural Language Processing (NLP) are challenging, as they require deep understanding. Understanding involves methods that could differentiate between the facts of explicit and implicit, regular and irregular, syntactical and semantic language rules. Researches oriented towards Natural Language Processing and Sentiment Analysis have many unresolved problems like co-reference resolution, negation handling, anaphora resolution, named-entity recognition, and word-sense disambiguation. This paper is proposed to develop an Optimized Partial Ancestral Graph (O-PAG) which is capable of mining patterns in human interactions and compare it with an existing tree based pattern mining approach. The experimental results are exposed to number of frequent interactions made and execution time. Results indicate that the overall performance can reach considerable improvements on using O-PAG approach.
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