Detection of Topic from Unstructured Text With Mixed Languages

This paper takes as a point of departure the hypothesis that Papiamentu descends from Upper Guinea Portuguese Creole (a term covering the sister varieties of the Cape Verde Islands and Guinea-Bissau and Casamance), speakers of which arrived on Curaçao in the second half of the 17th century, subsequently shifted their basic content vocabulary towards Spanish, but maintained the original morphosyntax. This scenario raises the question of whether, in addition to being a creole, Papiamentu can be analyzed as a so-called mixed (or intertwined) language. The present paper positively answers this question by drawing parallels between (the emergence of) Papiamentu and recognized mixed languages.

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Creative exploration using topic-based bisociative networks

Design Science ◽

10.1017/dsj.2018.5 ◽

2018 ◽

Vol 4 ◽

Cited By ~ 1

Author(s):

Faez Ahmed ◽

Mark Fuge

Keyword(s):

Idea Generation ◽

Information Network ◽

Large Collection ◽

Computational Framework ◽

Conceptual Similarity ◽

Cross Domain ◽

Unstructured Text ◽

Collaborative Community ◽

Creative Solutions ◽

Multiple Domains

Bisociative knowledge discovery is an approach that combines elements from two or more ‘incompatible’ domains to generate creative solutions and insight. Inspired by Koestler’s notion of bisociation, in this paper we propose a computational framework for the discovery of new connections between domains to promote creative discovery and inspiration in design. Specifically, we propose using topic models on a large collection of unstructured text ideas from multiple domains to discover creative sources of inspiration. We use these topics to generate a Bisociative Information Network – a graph that captures conceptual similarity between ideas – that helps designers find creative links within that network. Using a dataset of thousands of ideas from OpenIDEO, an online collaborative community, our results show usefulness of representing conceptual bridges through collections of words (topics) in finding cross-domain inspiration. We show that the discovered links between domains, whether presented on their own or via ideas they inspired, are perceived to be more novel and can also be used as creative stimuli for new idea generation.

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Term weighting using contextual information for categorization of unstructured text documents

2015 Annual IEEE India Conference (INDICON) ◽

10.1109/indicon.2015.7443216 ◽

2015 ◽

Cited By ~ 1

Author(s):

Anagha Kulkarni ◽

Vrinda Tokekar ◽

Parag Kulkarni

Keyword(s):

Contextual Information ◽

Term Weighting ◽

Text Documents ◽

Unstructured Text

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Comprehension of competing argument marking systems in two Australian mixed languages

Bilingualism Language and Cognition ◽

10.1017/s1366728911000307 ◽

2011 ◽

Vol 15 (2) ◽

pp. 378-396 ◽

Cited By ~ 5

Author(s):

CARMEL O'SHANNESSY ◽

FELICITY MEAKINS

Keyword(s):

Word Order ◽

Case Marking ◽

Comprehension Task ◽

Crosslinguistic Influence ◽

Mixed Languages

Crosslinguistic influence has been seen in bilingual adult and child learners when compared to monolingual learners. For speakers of Light Warlpiri and Gurindji Kriol there is no monolingual group for comparison, yet crosslinguistic influence can be seen in how the speakers resolve competition between case-marking and word order systems in each language. Light Warlpiri and Gurindji Kriol are two new Australian mixed languages, spoken in similar, yet slightly different, sociolinguistic contexts, and with similar, yet slightly different, argument marking systems. The different sociolinguistic situations and systems of argument marking lead to a difference in how speakers of each language interpret simple transitive sentences in a comprehension task. Light Warlpiri speakers rely on ergative case-marking as an indicator of agents more often than Gurindji Kriol speakers do. Conversely, Gurindji Kriol speakers rely on word order more often than Light Warlpiri speakers do.

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Information Extraction

The Oxford Handbook of Computational Linguistics 2nd edition ◽

10.1093/oxfordhb/9780199573691.013.009 ◽

2015 ◽

Cited By ~ 1

Author(s):

Ralph Grishman

Keyword(s):

Knowledge Representation ◽

Information Extraction ◽

Semantic Relations ◽

Supervised Training ◽

Unstructured Text ◽

Structured Knowledge

Information extraction constructs a structured knowledge representation from unstructured text, so that the knowledge may be further used for search, inference, and analysis. Given a specification of select types of entities, semantic relations, and events, it builds a database from instances of this information in text. This chapter describes the stages of processing involved and considers how such systems may be built using hand-coded rules, supervised training, and semi-supervised training.

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