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Journal articles on the topic 'GraphColl'

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Author: Grafiati

Published: 30 May 2022

Last updated: 31 May 2022

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1

Baker, Paul. "The shapes of collocation." International Journal of Corpus Linguistics 21, no. 2 (August 29, 2016): 139–64. http://dx.doi.org/10.1075/ijcl.21.2.01bak.

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The tool GraphColl (Brezina et al. 2015) allows collocational networks to be identified within corpora, enabling corpus analysis to go beyond two-way collocation. This paper aims to illustrate the types of linguistic relationships that can appear when more than two words are considered, using graph theory to account for the different types of collocational “shapes” that can be formed within GraphColl networks. Using the reference corpus, the BE06, examples of different types of graphs were obtained and analysed in order to form an understanding of the sorts of relationships between words that occur in particular shapes. The analysis indicates that concepts from graph theory can be usefully integrated into corpus analysis of collocation as well as showing the potential for a more sophisticated understanding of the company that words keep.

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2

Brezina, Vaclav, Tony McEnery, and Stephen Wattam. "Collocations in context." International Journal of Corpus Linguistics 20, no. 2 (August 17, 2015): 139–73. http://dx.doi.org/10.1075/ijcl.20.2.01bre.

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The idea that text in a particular field of discourse is organized into lexical patterns, which can be visualized as networks of words that collocate with each other, was originally proposed by Phillips (1983). This idea has important theoretical implications for our understanding of the relationship between the lexis and the text and (ultimately) between the text and the discourse community/the mind of the speaker. Although the approaches to date have offered different possibilities for constructing collocation networks, we argue that they have not yet successfully operationalized some of the desired features of such networks. In this study, we revisit the concept of collocation networks and introduce GraphColl, a new tool developed by the authors that builds collocation networks from user-defined corpora. In a case study using data from McEnery’s (2006a) study of the Society for the Reformation of Manners Corpus (SRMC), we demonstrate that collocation networks provide important insights into meaning relationships in language.

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3

Adil Jaafar, Eman. "Collocation Networks of Selected Words in Academic Writing: A Corpus-Based Study." Arab World English Journal 13, no. 1 (March 24, 2022): 240–55. http://dx.doi.org/10.24093/awej/vol13no1.15.

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This study aims at shedding light on the linguistic significance of collocation networks in the academic writing context. Following Firth’s principle “You shall know a word by the company it keeps.” The study intends to examine three selected nodes (i.e. research, study, and paper) shared collocations in an academic context. This is achieved by using the corpus linguistic tool; GraphColl in #LancsBox software version 5 which was announced in June 2020 in analyzing selected nodes. The study focuses on academic writing of two corpora which were designed and collected especially to serve the purpose of the study. The corpora consist of a collection of abstracts extracted from two different academic journals that publish for writers from different countries around the world. This corpus-based study aims at examining the significance of chunks of language in texts. The concept of collocations is crucial in corpus linguistics to identify semantic relations. This can help in the teaching and learning processes. Furthermore, this study is conducted to answer the following research questions; first, whether the three words study, paper, and research are used interchangeably in the corpora or not? Second, what are the shared collocational associations surrounding the selected nodes? Finally, it is worth noting that the study of collocations highlights the linguistic features of texts through computational analytical tools that can save time and help to gain objective results systematically. The findings show that ‘research’ and ‘study’ are used rather interchangeably in the writing of the abstracts; however, ‘paper’ has fewer shared collocations in the same academic context.

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4

Chang, Li-Yun, Yen-Chi Chen, and Charles A. Perfetti. "GraphCom: A multidimensional measure of graphic complexity applied to 131 written languages." Behavior Research Methods 50, no. 1 (April 19, 2017): 427–49. http://dx.doi.org/10.3758/s13428-017-0881-y.

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5

Dong, Jihua, and Louisa Buckingham. "Identity construction and its collocation networks: a cross-register analysis of the finance domain." Text & Talk, March 11, 2022. http://dx.doi.org/10.1515/text-2020-0094.

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Abstract This study investigates the use of explicit manifestations of authorial identity (namely self-mention pronouns) and their collocation networks in academic and workplace written texts. Based on a purpose-built corpus of research articles and the Hong Kong Financial Services Corpus (HKFSC), this study used Antconc and Graphcoll to extract and analyze the pronouns and their collocation networks. The statistical analysis shows that the academic register contains significantly more self-mention pronouns than the workplace corpus, which can be attributed to a stronger tendency towards self-positioning. We also identified significant register-specific semantic features of the collocation networks of self-mention pronouns. These findings contribute to our understanding of how self-mention pronouns operate in tandem with their surrounding context in register-specific discourse. Pedagogically, the findings can be useful for workshop-based training for finance students and early-career professionals in this domain to support the development of the discipline-specific writing skills needed for careers in academia and industry.

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6

"Graphcial abstracts." Tetrahedron 58, no. 43 (October 2002): 8621–27. http://dx.doi.org/10.1016/s0040-4020(02)01207-3.

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7

"Graphcial abstracts." Tetrahedron 58, no. 44 (October 2002): 8883–89. http://dx.doi.org/10.1016/s0040-4020(02)01233-4.

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8

"Graphcal Abstracts." Journal of Fluorine Chemistry 129, no. 1 (January 2008): 1–3. http://dx.doi.org/10.1016/s0022-1139(07)00370-3.

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9

"Content list including Graphcal Abstracts." Journal of Controlled Release 335 (July 2021): II—XIV. http://dx.doi.org/10.1016/s0168-3659(21)00331-x.

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10

"Content list including Graphcal Abstracts." Journal of Controlled Release 337 (September 2021): II—XVII. http://dx.doi.org/10.1016/s0168-3659(21)00453-3.

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11

"Contents list including Graphcal Abstracts." Journal of Controlled Release 333 (May 2021): IV—XIV. http://dx.doi.org/10.1016/s0168-3659(21)00223-6.

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12

"Content list including Graphcal Abstracts." Journal of Controlled Release 334 (June 2021): II—XII. http://dx.doi.org/10.1016/s0168-3659(21)00271-6.

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13

"Content list including Graphcal Abstracts." Journal of Controlled Release 338 (October 2021): III—XX. http://dx.doi.org/10.1016/s0168-3659(21)00522-8.

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14

"Content list including Graphcal Abstracts." Journal of Controlled Release 343 (March 2022): II—XVIII. http://dx.doi.org/10.1016/s0168-3659(22)00139-0.

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15

"Content list including Graphcal Abstracts." Journal of Controlled Release 340 (December 2021): II—VIII. http://dx.doi.org/10.1016/s0168-3659(21)00645-3.

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"Content list including Graphcal Abstracts." Journal of Controlled Release 341 (January 2022): II—XVIII. http://dx.doi.org/10.1016/s0168-3659(22)00017-7.

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17

"Content list including Graphcal Abstracts." Journal of Controlled Release 342 (February 2022): II—X. http://dx.doi.org/10.1016/s0168-3659(22)00064-5.

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18

"Content list including Graphcal Abstracts." Journal of Controlled Release 339 (November 2021): II—XIII. http://dx.doi.org/10.1016/s0168-3659(21)00591-5.

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19

"Content list including Graphcal Abstracts." Journal of Controlled Release 344 (April 2022): II—IX. http://dx.doi.org/10.1016/s0168-3659(22)00175-4.

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"Content list including Graphcal Abstracts." Journal of Controlled Release 345 (May 2022): II—XIX. http://dx.doi.org/10.1016/s0168-3659(22)00252-8.

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21

"Graphco makes pensioners lives more secure…" Biometric Technology Today 10, no. 3 (March 2002): 2. http://dx.doi.org/10.1016/s0969-4765(02)00303-x.

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22

Yuan, Quan, Xiaoyuan Fu, Ziyan Li, Guiyang Luo, Jinglin Li, and Fangchun Yang. "GraphComm: Efficient Graph Convolutional Communication for Multi-Agent Cooperation." IEEE Internet of Things Journal, 2021, 1. http://dx.doi.org/10.1109/jiot.2021.3097947.

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23

Maddrell-Mander, Samuel, Lakshan Ram Madhan Mohan, Alexander Marshall, Daniel O’Hanlon, Konstantinos Petridis, Jonas Rademacker, Victoria Rege, and Alexander Titterton. "Studying the Potential of Graphcore® IPUs for Applications in Particle Physics." Computing and Software for Big Science 5, no. 1 (March 17, 2021). http://dx.doi.org/10.1007/s41781-021-00057-z.

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AbstractThis paper presents the first study of Graphcore’s Intelligence Processing Unit (IPU) in the context of particle physics applications. The IPU is a new type of processor optimised for machine learning. Comparisons are made for neural-network-based event simulation, multiple-scattering correction, and flavour tagging, implemented on IPUs, GPUs and CPUs, using a variety of neural network architectures and hyperparameters. Additionally, a Kálmán filter for track reconstruction is implemented on IPUs and GPUs. The results indicate that IPUs hold considerable promise in addressing the rapidly increasing compute needs in particle physics.

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