N-Grams TextRank A Novel Domain Keyword Extraction Technique
The rapid growth of the internet has given us a wealth of information and data spread across the web. However, as the data begins to grow we simultaneously face the grave problem of an Information Explosion. An abundance of data can lead to large scale data management problems as well as the loss of the true meaning of the data. In this paper, we present an advanced domain specific keyword extraction algorithm in order to tackle this problem of paramount importance. Our algorithm is based on a modified version of TextRank algorithm - an algorithm based on PageRank to successfully determine the keywords from a domain specific document. Furthermore, this paper proposes a modification to the traditional TextRank algorithm that takes into account bigrams and trigrams and returns results with an extremely high precision. We observe how the precision and f1-score of this model outperforms other models in many domains and the recall can be easily increased by increasing the number of results without affecting the precision. We also discuss about the future work of extending the same algorithm to Indian languages.
Code (0)
등록된 구현이 없습니다.
Tasks
Keyword ExtractionManagementSimilar Papers 제목 키워드 기반
VisualTextRank: Unsupervised Graph-based Content Extraction for Automating Ad Text to Image Search
Numerous online stock image libraries offer high quality yet copyright free images for use in marketing campaigns. To assist advertisers in navigating such third party libraries, we study the problem of automatically fet…
Image RetrievalKeyword ExtractionMarketingSentenceExploring Linguistically-Lightweight Keyword Extraction Techniques for Indexing News Articles in a Multilingual Set-up
This paper presents a study of state-of-the-art unsupervised and linguistically unsophisticated keyword extraction algorithms, based on statistic-, graph-, and embedding-based approaches, including, i.a., Total Keyword F…
ArticlesKeyword ExtractionTextRank: Bringing Order into Texts
In this paper, we introduce TextRank – a graph-based ranking model for text processing and show how this model can be successfully used in natural language applications. In particular, we propose two innovative unsupervi…
SentenceGraph-based Semantical Extractive Text Analysis
In the past few decades, there has been an explosion in the amount of available data produced from various sources with different topics. The availability of this enormous data necessitates us to adopt effective computat…
Keyword ExtractionSemantic SimilaritySemantic Textual SimilarityText SummarizationBiased TextRank: Unsupervised Graph-Based Content Extraction
We introduce Biased TextRank, a graph-based content extraction method inspired by the popular TextRank algorithm that ranks text spans according to their importance for language processing tasks and according to their re…