# IWF-TextRank Keyword Extraction Algorithm Modelling
## 1 Introduction  
In the digital age, the rapid proliferation of data presents challenges in efficiently identifying valuable information from large volumes of irrelevant content. This problem is particularly acute in specialised fields such as traffic accident cause analysis, where relevant keywords must be accurately extracted to summarise extensive textual datasets. Keyword extraction techniques constitute a fundamental tool in natural language processing (NLP) and play a critical role in addressing these challenges by assisting users in promptly identifying core content [1]. Currently, keyword extraction methods can generally be classified into two categories: supervised and unsupervised keyword extraction [2; 3]. Supervised keyword extraction methods typically approach the process as a binary classification task, extracting keywords by training models on manually labelled corpora [4; 5]. Typically, complex machine learning algorithms are employed to learn from rich feature sets and optimise keyword prediction performance. However, these approaches are constrained by their requirement for substantial annotated datasets, which are often difficult to acquire [6]. Furthermore, models trained on specific datasets may not generalise well to various document types or keyword extraction tasks, resulting in potential overfitting. In contrast, unsupervised methods do not require labelled data; instead, they rank keywords based on specific metrics, allowing unsupervised methods to be more adaptable to different contexts. In response to the limitations of traditional unsupervised approaches, [PERSON] and [PERSON] [7] proposed the TextRank algorithm in 2004, which has since gained widespread attention. TextRank is a graph-based algorithm that constructs co-occurrence networks by treating words as nodes and their co-occurrence relations as edges. However, the algorithm assumes that all words are equally important initially, which might not fully capture the semantic and syntactic significance of individual words within a document. To overcomethese constraints, this paper proposes an improved method, the IWF-TextRank algorithm, which integrates a variety of lexical features (e.g., semantic vectors, word frequency, lexical properties, and word length) into the traditional TextRank framework. The innovation of this method lies in the dynamic allocation of word weights through backpropagation (BP) neural networks and sequence relationship analysis. This method can capture more nuanced contextual information, thereby improving the relevance and accuracy of keyword extraction, especially on domain-specific datasets such as traffic accident reports. The main contribution of this article is to propose a new keyword extraction algorithm, IWF-TextRank, which extends the traditional TextRank into a multi-feature weighted framework. It integrates features such as semantic vectors, word frequency, lexicality, and word length, and combines them with a BP neural network to achieve dynamic weight distribution, which significantly improves the keyword extraction effect of the algorithm in data in specific fields (such as traffic accident reports). The specific features are as follows:  
* Semantic vectors: word vectors are utilised to take into account the semantic relationships between words;
* Word frequency and lexicality: adjusts the importance of keywords based on their frequency of occurrence and grammatical roles in the sentence;
* Word length: word length is taken into account as longer words usually carry more information;
* Backpropagation neural network: a BP neural network is used to dynamically adjust word weights to ensure that more important, contextually relevant words are emphasised. In addition, by adding sequence relationship analysis to the BP neural network, IWF-TextRank can more accurately capture the contextual association of the text, ultimately achieving higher precision and recall rates.