Visualizing Sequences of Texts Using Collocational Networks

Magnusson, Camilla; Vanharanta, Hannu

doi:10.1007/3-540-45065-3_24

Cited by 12 publications

(15 citation statements)

References 3 publications

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“…In order to compare the textual and financial information in these reports, two data mining methods were used. The texts were turned into collocational networks according to the method used by Magnusson and Vanharanta [16]. The financial data were visualised using a self-organising map.…”

Section: Methodsmentioning

confidence: 99%

The language of quarterly reports as an indicator of change in the company’s financial status

Magnusson

Arppe

Eklund

et al. 2005

Information & Management

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Section: Methodsmentioning

confidence: 99%

The language of quarterly reports as an indicator of change in the company’s financial status

Magnusson

Arppe

Eklund

et al. 2005

Information & Management

Self Cite

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“…The various patterns identified can be used to produce collocational networks [21]. Since we believe that many of these networks may provide evidence of semantic inclusion, we assert isa relationships between the words and their collocates and produce hierarchies from these collocational networks (see, for example, [6]).…”

Section: Automatic Extraction Of Compound Words and Diachronic Variationmentioning

confidence: 86%

Automatic Ontology Extraction from Unstructured Texts

Ahmad

Gillam

2005

Lecture Notes in Computer Science

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Abstract. Construction of the ontology of a specific domain currently relies on the intuition of a knowledge engineer, and the typical output is a thesaurus of terms, each of which is expected to denote a concept. Ontological 'engineers' tend to hand-craft these thesauri on an ad-hoc basis and on a relatively smallscale. Workers in the specific domain create their own special language, and one device for this creation is the repetition of select keywords for consolidating or rejecting one or more concepts. A more scalable, systematic and automatic approach to ontology construction is possible through the automatic identification of these keywords. An approach for the study and extraction of keywords is outlined where a corpus of randomly collected unstructured, i.e. not containing any kind of mark-up, texts in a specific domain is analysed with reference to the lexical preferences of the workers in the domain. An approximation about the role of frequently used single words within multiword expressions leads us to the creation of a semantic network. The network can be asserted into a terminology database or knowledge representation formalism, and the relationship between the nodes of the network helps in the visualisation of, and automatic inference over, the frequently used words denoting important concepts in the domain. We illustrate our approach with a case study using corpora from three time periods on the emergence and consolidation of nuclear physics. The text-based approach appears to be less subjective and more suitable for introspection, and is perhaps useful in ontology evolution.

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“…However, STC creates a purely partitional clustering, not a hierarchical one. Collocation networks [79] are another similar method for extracting terms and their relationships from documents; this method uses frequency of term occurrence and mutual information (see Section 6.4) to generate a visualization of a document.…”

Section: Examination Of Chca As a Clustering Methodsmentioning

confidence: 99%