E dge G raph: Revisiting Statistical Measures for Language Independent Keyphrase Extraction Leveraging on Bi-grams (2022)

Abstract: The NLP research community resort conventional Word Co-occurrence Network (WCN) for keyphrase extraction using random walk sampling mechanism such as PageRank algo rithm to identify candidate words/ phrases. We argue that the nature of WCN is a path-based network and does not follow a core-periphery structure as observed in web-page linking network. Thus, the language networks leveraging on bi-grams may represent better semantics for keyphrase extraction using random walk. In this work, we use bi-gram as a node and adjacent bi-grams are linked together to generate an EdgeGraph. We validate our method over four publicly available dataset to demonstrate the effectiveness of our simple yet effective language network and our extensive experiments show that random walk over EdgeGraph representation performs better than conventional WCN. We make our codes and supplementary materials available over Github.

## introduction the language network is a textual representation of documents in the shape of a graph to exploit the best features as their characteristics. with recent developments in statistical keyphrase extraction, language network plays a pivotal role in identifying underlying patterns among words, phrases or sentences @xcite . the research community maps these patterns using the network properties as the structural properties of language networks has gained much attention in recent years @xcite . existing literature contains substantial studies over the structural properties for different languages @xcite and different domains @xcite resulting into development of real-time language independent and domain-specific techniques, respectively. the conventional wcn is established as a benchmark representation of textual documents for random walk based keyphrase extraction @xcite . the random walk sampling is characterized by stochastic movement of several iterations over a network for redistributing weights to nodes. this concept of random walk was initially introduced for web-page linking due to the core-periphery structure @xcite of the world wide web (www) connectivity. however, we observe that: in this work, we study a significance of replacing wcn with edgegraph for random walk based gket. the overview of our proposed approach is shown in the fig. 1 . the major contributions of this research are: ## background and related work the automatic keyword extraction techniques are classified into the structured and unstructured algorithms. the supervised keyword extraction is not reliable for ever-changing and dynamically evolving information @xcite . the unsupervised algorithms are either statistical or graph-based. a well-studied approach of supervised algorithms is graph-based keyphrase extraction. ## our approach in this manuscript, we propose a variation in the graph-based text representation. we find candidate phrases which seems to be capable to being identified as keyphrases using random walk. in this section, we discuss edgegraph representation. ## experiments and evaluation we perform the experiments with textrank (mihalcea and tarau, 2004), singlerank @xcite , positionrank @xcite , and nerank (bellaachia and al-dhelaan, 2012) over publicly available text collections. in this section, we discuss the characteristics of datasets, the experimental setup, performance evaluation and statistical significance of the proposed textual representation over the baseline. ## conclusion here in this work, we propose an edgegraph representation for information retrieval tasks. the experimental results over four publicly available datasets shows that keyphrase extraction is significantly improved with edgegraph representation leveraging on bi-grams. the recall and f-measure improves upto 27% and 18%, respectively, for the datasets with medium-sized english texts. applicability of edgegraph on more than one languages (english and portuguese) suggests its languageindependencex. in future, edgegraph can be used for extractive text summarization, language generation and cross-lingual analysis and other information retrieval tasks. in addition to this, the massive online data can be handled using dynamics of edge-graph evolved from dynamically streaming data without using any pre-trained or supervised models.