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Event Pattern Analysis and Prediction at Sentence Level using Neuro-Fuzzy Model for Crime Event Detection

机译：使用神经模糊模型的犯罪事件检测在句子水平上的事件模式分析和预测

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摘要

Classifying the sentences that describe Events is an important task for many applications. In this paper, Event patterns are identified and extracted at sentence level using term features. The terms that trigger Events along with the sentences are extracted from web documents. The sentence structures are analyzed using POS tags. A hierarchal sentence classification model is proposed by considering specific term features of the sentence and the rules are derived. The rules fail to define a clear boundary between the patterns and create ambiguity and impreciseness. To overcome this, suitable fuzzy rules are derived which gives importance to all term features of the sentence. The fuzzy rules are constructed with more variables and generate sixteen patterns. Artificial Neuro-Fuzzy Inference System (ANFIS) model is proposed for training and classifying the sentence patterns for capturing the knowledge present in sentences. The obtained patterns are assigned linguistic grades based on previous classification knowledge. These grades represent the type and quality of information in the patterns. The membership function is used to evaluate the fuzzy rules. The patterns share the membership values between [0-1] which determines the weights for each pattern. Later, higher weighted patterns are considered to build Event Corpus, which helps in retrieving useful and interested information of Event Instances. The performance of the proposed approach classification is evaluated for 'Crime' Event by crawling documents from WWW and also evaluated for benchmark dataset for 'Die' Event. It is found that the performance of the proposed approach is encouraging when compared with recently proposed similar approaches.
机译：对描述事件的句子进行分类是许多应用程序的重要任务。在本文中，使用术语功能在句子级别识别并提取事件模式。触发事件以及句子的术语是从Web文档中提取的。使用POS标签分析句子结构。通过考虑句子的特定术语特征，提出了一种分层句子分类模型，并推导了规则。规则无法在模式之间定义清晰的边界，并造成歧义和不精确。为了克服这个问题，导出了合适的模糊规则，该规则对句子的所有术语特征都具有重要性。模糊规则由更多变量构成，并生成十六种模式。提出了人工神经模糊推理系统（ANFIS）模型，用于训练和分类句子模式以捕获句子中存在的知识。根据先前的分类知识，为获得的模式分配语言等级。这些等级表示模式中信息的类型和质量。隶属度函数用于评估模糊规则。这些模式在[0-1]之间共享成员资格值，该值确定每个模式的权重。以后，可以考虑使用较高权重的模式来构建事件语料库，这有助于检索事件实例的有用信息和感兴趣信息。通过从WWW爬网文档对“犯罪”事件评估建议的方法分类的性能，并对“死亡”事件的基准数据集进行评估。发现与最近提出的类似方法相比，提出的方法的性能令人鼓舞。

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