Keyphrase extraction is a critical step in many natural language processing and Information retrieval applications. In this paper, we introduce AKEA, a keyphrase extraction algorithm for single Arabic documents. AKEA is an unsupervised algorithm as it does not need any type of training in order to achieve its task. We rely on heuristics that collaborate linguistic patterns based on Part-Of-Speech (POS) tags, statistical knowledge, and the internal structural pattern of terms (i.e. word-occurrence). We employ the usage of Arabic Wikipedia to improve the ranking (or significance) of candidate keyphrases by adding a confidence score if the candidate exist as an indexed Wikipedia concept. Experimental results show that on average AKEA has the highest precision value, the highest F-measure value which indicates it presents more accurate results compared to its other algorithms

AKEA: An Arabic Keyphrase Extraction Algorithm

Keyword extraction is the foundation for solving various text mining tasks. However, the literature heavily relies on statistical, linguistic feature-based, or graph-based metrics to gauge corpus-representative keywords, the process of which is sensitive to preprocessing and stopword selection. In this paper, we propose a general-use keyword extraction model designed to work with document groups of various sizes, domains, and readability, as well as the existence of keyword labels. To extract a better selection of keywords, we employ a simple logistic regression model with the least absolute shrinkage and selection operator regularization (Tibshirani, 1996). The classification-based structure of our approach ensures learning words that distinctively characterize the given document group against the comparison groups, enhancing the representativeness of the extracted keywords. Furthermore, our model repeatedly modifies coefficients as it learns the document label classifiers, rather than relying directly on the term frequencies, reducing the model’s sensitivity to words of very high and very low frequencies. We test our model’s performance against numerous classic keyword extraction frameworks as baseline models using online customer reviews, news articles, and patent documentation. The results indicate that our proposed method has robust performance in terms of representability and distinctiveness across document groups with varying sizes, number of class labels, levels of readability, and domain. Additionally, we show that our model beats baseline models even when applied to documents without class labels compared with the baseline models. Given its generalizability and simplicity, we believe that our proposed model may serve as an easy-to-use, yet a powerful, general-use tool for keyword extraction, especially when working with various groups of documents from different domains. 

General-use unsupervised keyword extraction model for keyword analysis

MGRank: A keyword extraction system based on multigraph GoW model and novel edge weighting procedure

Keyword extraction is a branch of natural language processing, which plays an important role in many tasks, such as long text classification, automatic summary, machine translation, dialogue system, etc. All of them need to use high-quality keywords as a starting point. In this paper, we propose a deep learning network called deep neural semantic network (DNSN) to solve the problem of short text keyword extraction. It can map short text and words to the same semantic space, get the semantic vector of them at the same time, and then compute the similarity between short text and words to extract top-ranked words as keywords. The Bidirectional Encoder Representations from Transformers was first used to obtain the initial semantic feature vectors of short text and words, and then feed the initial semantic feature vectors to the residual network so as to obtain the final semantic vectors of short text and words at the same vector space. Finally, the keywords were extracted by calculating the similarity between short text and words. Compared with existed baseline models including Frequency, Term Frequency Inverse Document Frequency (TF-IDF) and Text-Rank, the model proposed is superior to the baseline models in Precision, Recall, and F-score on the same batch of test dataset. In addition, the precision, recall, and F-score are 6.79%, 5.67%, and 11.08% higher than the baseline model in the best case, respectively.

Deep Neural Semantic Network for Keywords Extraction on Short Text

为解决传统万有引力模型因词语质量、词间距离度量不足导致关键词效果较差的问题,分别从词语质量表示和距离计算两方面对传统万有引力模型进行改进。提出基于词频-文档分布熵的方法构建通用词表,过滤候选词后,综合位置、词性、词长特征改进TF-IDF方法,计算词语外部重要性;构建共现网络图,通过计算词语关联度衡量单词内部重要性,融合内部重要性和外部重要性计算词语质量并赋予图节点差异化初始权重;在语义距离的基础上引入依存句法距离,计算词间引力作为边的权重,多次迭代后排序输出TopK个关键词。实验结果表明,该方法在3GPP技术规范和公开的SemEval2010、DUC2001数据集上较传统方法取得了更好的效果,验证了方法的有效性和通用性。

Automatic keyword extraction method based on gravitational model

Keyword extraction plays an increasingly crucial role in several texts related researches. Applications that utilize feature word selection include text mining, and information retrieval etc. This paper introduces novel word features for keyword extraction. These new word features are derived according to the background knowledge supplied by patent data. Given a document, to acquire its background knowledge, this paper first generates a query for searching the patent data based on the key facts present in the document. The query is used to find files in patent data that are closely related to the contents of the document. With the patent search result file set, the information of patent inventors, assignees, and citations in each file are used to mining the hidden knowledge and relationship between different patent files. Then the related knowledge is imported to extend the background knowledge base, which would be extracted to derive the novel word features. The newly introduced word features that reflect the document’s background knowledge offer valuable indications on individual words’ importance in the input document and serve as nice complements to the traditional word features derivable from explicit information of a document. The keyword extraction problem can then be regarded as a classification problem and the Support Vector Machine (SVM) is used to extract the keywords. Experiments have been done using two different data sets. The results show our method improves the performance of keyword extraction.

Novel Word Features for Keyword Extraction

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Yiqun Chen

Author Tools

Chat about Author

Papers

Novel Word Features for Keyword Extraction