A survey of graphs in natural language processing

Năstase, Vivi; Mihalcea, Rada; Radev, Dragomir

doi:10.1017/s1351324915000340

Cited by 63 publications

(17 citation statements)

References 103 publications

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“…Moreover, the burgeoning field of social network analysis has offered unprecedented insight into how humans transmit information and interact with one another [31,32]. Perhaps more than any other branch of cognitive science, quantitative linguistics has adopted network-based approaches as a cornerstone methodology [33–36]. Across levels of the language hierarchy, graph theoretical methods have been applied to the study of phonological [37,38], semantic [39–41], and syntactic dependency systems [42,43].…”

Section: Complex Network Are Pervasivementioning

confidence: 99%

Local Patterns to Global Architectures: Influences of Network Topology on Human Learning

Karuza

Thompson-Schill

Bassett

2016

Trends in Cognitive Sciences

163

109

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A core question in cognitive science is how humans acquire and represent knowledge about their environments. To this end, quantitative theories of learning processes have been formalized in an attempt to explain and predict changes in brain and behavior. Here we connect statistical learning approaches in cognitive science, which are rooted in learners’ sensitivity to local distributional regularities, and network science approaches to characterizing global patterns and their emergent properties. We focus on innovative work that describes how learning is influenced by the topological properties underlying sensory input. The confluence of these theoretical approaches and this recent empirical evidence motivate the importance of scaling up quantitative approaches to learning at both behavioral and neural levels.

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Section: Complex Network Are Pervasivementioning

confidence: 99%

Local Patterns to Global Architectures: Influences of Network Topology on Human Learning

Karuza

Thompson-Schill

Bassett

2016

Trends in Cognitive Sciences

163

109

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“…Modeling language as a graph has a long tradition (Dorogovtsev and Mendes, 2001;Mihalcea and Radev, 2011;Cong and Liu, 2014;Nastase et al, 2015). We propose to employ word cooccurrence graphs to jointly solve the problems of multiple senses and diachrony.…”

Section: Related Workmentioning

confidence: 99%

Modeling the Evolution of Word Senses with Force-Directed Layouts of Co-occurrence Networks

Reke¹,

Schwanhold²,

Krestel³

2021

Proceedings of the 2nd International Workshop on Computational Approaches to Historical Language Change 2021

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Languages evolve over time and the meaning of words can shift. Furthermore, individual words can have multiple senses. However, existing language models often only reflect one word sense per word and do not reflect semantic changes over time. While there are language models that can either model semantic change of words or multiple word senses, none of them cover both aspects simultaneously. We propose a novel force-directed graph layout algorithm to draw a network of frequently cooccurring words. In this way, we are able to use the drawn graph to visualize the evolution of word senses. In addition, we hope that jointly modeling semantic change and multiple senses of words results in improvements for the individual tasks.

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“…Edges are the relationships between text units. One prevalent text graph formulation is the word co-occurrence network, also known as the Graph-of-Words (GoW) [ 30 ]. Nodes in this network are words, and edges indicate whether two words appear in a specified text unit (e.g., document, sentence, etc.).…”

Section: Text Graphs: Graph Of Words (Gow) Representationmentioning

confidence: 99%

Proactively Identifying Emerging Hacker Threats from the Dark Web

Samtani

Zhu

Chen

2020

ACM Trans. Priv. Secur.

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Cybersecurity experts have appraised the total global cost of malicious hacking activities to be $450 billion annually. Cyber Threat Intelligence (CTI) has emerged as a viable approach to combat this societal issue. However, existing processes are criticized as inherently reactive to known threats. To combat these concerns, CTI experts have suggested proactively examining emerging threats in the vast, international online hacker community. In this study, we aim to develop proactive CTI capabilities by exploring online hacker forums to identify emerging threats in terms of popularity and tool functionality. To achieve these goals, we create a novel Diachronic Graph Embedding Framework (D-GEF). D-GEF operates on a Graph-of-Words (GoW) representation of hacker forum text to generate word embeddings in an unsupervised manner. Semantic displacement measures adopted from diachronic linguistics literature identify how terminology evolves. A series of benchmark experiments illustrate D-GEF's ability to generate higher quality than state-of-the-art word embedding models (e.g., word2vec) in tasks pertaining to semantic analogy, clustering, and threat classification. D-GEF's practical utility is illustrated with in-depth case studies on web application and denial of service threats targeting PHP and Windows technologies, respectively. We also discuss the implications of the proposed framework for strategic, operational, and tactical CTI scenarios. All datasets and code are publicly released to facilitate scientific reproducibility and extensions of this work.

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A survey of graphs in natural language processing

Cited by 63 publications

References 103 publications

Local Patterns to Global Architectures: Influences of Network Topology on Human Learning

Local Patterns to Global Architectures: Influences of Network Topology on Human Learning

Modeling the Evolution of Word Senses with Force-Directed Layouts of Co-occurrence Networks

Proactively Identifying Emerging Hacker Threats from the Dark Web

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