landscape model of reading, priming, cooccurrence graphs
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This model captures both on-line comprehension processes during reading and the off-line memory representation after reading is completed, incorporating both memory-based and coherence-based mechanisms of comprehension. http://www.brainandeducationlab.nl/downloads
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A central component of successful reading comprehension is the construction of a coherent memory representation of the text. https://www.questia.com/library/journal/1P3-440581011/a-landscape-model-of-reading-comprehension-inferential
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The model is based on the premise that, during reading, the ideas and concepts associated with the text fluctuate in their activation. The result is a dynamically shifting landscape of activations. Two factors contribute to the shape of this landscape: readers' limited attentional resources and their attempts to maintain standards for coherence. https://www.questia.com/library/journal/1P3-440581011/a-landscape-model-of-reading-comprehension-inferential
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Priming is an implicit memory effect in which exposure to one stimulus influences a response to another stimulus. http://en.wikipedia.org/wiki/Priming_%28psychology%29
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Priming can occur following perceptual, semantic, or conceptual stimulus repetition. For example, if a person reads a list of words including the word table, and is later asked to complete a word starting with tab, the probability that he or she will answer table is greater than if they are not primed. http://en.wikipedia.org/wiki/Priming_%28psychology%29
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Semantic priming is theorized to work because of spreading neural networks.[9] When a person thinks of one item in a category, similar items are stimulated by the brain. Even if they are not words, morphemes can prime for complete words that include them.[16] An example of this would be that the morpheme 'psych' can prime for the word 'psychology'. http://en.wikipedia.org/wiki/Priming_%28psychology%29
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Context priming works by using a context to speed up processing for stimuli that are likely to occur in that context. A useful application of this effect is reading written text.[18] The grammar and vocabulary of the sentence provide contextual clues for words that will occur later in the sentence. These later words are processed more quickly than if they had been read alone, and the effect is greater for more difficult or uncommon words http://en.wikipedia.org/wiki/Priming_%28psychology%29
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Priming is a nonconscious form of human memory concerned with perceptual identification of words and objects. It refers to activating particular representations or associations in memory just before carrying out an action or task. For example, a person who sees the word "yellow" will be slightly faster to recognize the word "banana." This happens because yellow and banana are closely associated in memory. Additionally, priming can also refer to a technique in psychology used to train a person's memory in both positive and negative ways. https://www.psychologytoday.com/basics/priming
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In computational linguistics, word-sense induction (WSI) or discrimination is an open problem of natural language processing, which concerns the automatic identification of the senses of a word (i.e. meanings). http://en.wikipedia.org/wiki/Word-sense_induction
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The main hypothesis of co-occurrence graphs is assuming that the semantic of a word is represented by means of co-occurrence graph, whose vertices are co-occurrences and edges are co-occurrence relations. These approaches are related to word clustering methods, where co-occurrences between words can be obtained on the basis of grammatical [8] or collocational relations.[9] HyperLex is the successful approaches of a graph algorithm, based on the identification of hubs in co-occurrence graphs, which have to cope with the need to tune a large number of parameters. http://en.wikipedia.org/wiki/Word-sense_induction
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Visualize co-occurrence graph from document occurrence input using R package 'igraph' http://planspace.org/2013/01/30/visualize-co_occurrence/
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java - large-scale document co-occurrence analysis - Stack Overflow http://stackoverflow.com/questions/21090020/large-scale-document-co-occurrence-analysis
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This study used graph analysis to investigate how age differences modify the structure of semantic word association networks of children and adults and if the networks present a small-world structure and a scale-free distribution which are typical of natural languages. All networks presented a small-world structure, but they did not show entirely scale-free distributions. These results suggest that from childhood to adulthood, there is an increase not only in the number of words semantically linked to a target but also an increase in the connectivity of the network. http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0102-79722014000100011
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Correcting Between-Participant Discourse Bias in Comment Classification — Text mining and natural language processing have gained great momentum in recent years, with user-generated content becoming widely available. One key use is comment classification, with much attention being given to sentiment analysis and opinion mining. We find that content extracted from between-participants' discourse is often highly correlated https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2601942
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Acceptable bias?: Using corpus linguistics methods with critical discourse analysis corpus linguistics approaches can improve the objectivity of critical discourse analysis research, Taking a recent project which examined the representation of Islam and Muslims in the British press, corpus-driven procedures identified that Muslims tended to be linked to the concept of extreme belief much more http://www.research.lancs.ac.uk/portal/en/publications/-(f059d10e-5292-4379-8d0d-ce729bfe6373).html
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http://www.research.lancs.ac.uk/portal/en/publications/-(f059d10e-5292-4379-8d0d-ce729bfe6373).html
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Syntactic ambiguity resolution in discourse: Modeling the effects of referential context and lexical frequency. Regression analyses demonstrated that the effects of discourse context at the point of ambiguity (e.g., selected) interacted with the past participle frequency of the ambiguous verb. Reading times were modeled using a constraint-based competition framework in which multiple constraints are immediately integrated during parsing and interpretation.
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The Empirical Approach to the Study of Media Framing Like other concepts of mass communication research, the term framing has also appeared in popular discourse-particularly that of media critics, politicians, and campaign insiders. Noam Chomsky https://www.taylorfrancis.com/books/e/9781135655921/chapters/10.4324%2F9781410605689-12
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Framing as a theory of media effects In this paper I systematize the fragmented approaches to framing in political communication and integrate them into a comprehensive model. I classify previous approaches to framing research along two dimensions: the type of frame examined (media frames vs. audience frames) and the way frames are operationalized (independent variable or dependent variable). https://academic.oup.com/joc/article-abstract/49/1/103/4110088
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Discourse and Text: Linguistic and Intertextual Analysis within Discourse Analysis diverse approaches to discourse analysis can be enhanced through systematic use of these two forms of analysis, even those which claim a concern with the content rather than the form of texts. It is suggested that textual analysis needs to be based upon a multifunctional theory of language such as systematic-functional linguistics http://journals.sagepub.com/doi/abs/10.1177/0957926592003002004
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Strength of Discourse Context as a Determinant of the Subordinate Bias Effect an immediate influence of prior discourse information on lexical processing; and (2) that the strength of discourse constraints can play a governing role in lexical ambiguity resolution https://www.tandfonline.com/doi/abs/10.1080/713755861
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Identifying policy frames through semantic network analysis: an examination of nuclear energy policy across six countries The discourse of top-level decision-makers is analyzed to highlight similarities and differences in policy frames and to identify the key policy arguments in the integrated network of all six countries. https://link.springer.com/article/10.1007/s11077-015-9211-3
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Collocations in context: A new perspective on collocation networks 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.
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Language and Discourse Analysis with Coh-Metrix: Applications from Educational Material to Learning Environments at Scale Coh-Metrix including methodological and practical implications for the learning analytics (LA) and educational data mining (EDM) community. http://lak13.learning-analytics.info/journals/index.php/JLA/article/view/4330
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Analyzing Discourse and Text Complexity for Learning and Collaborating https://link.springer.com/book/10.1007%2F978-3-319-03419-5
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A semantic network analysis of associative frames associative frames and the method of semantic network analysis to the PR research field. By building on a more advanced understanding of communication as process of social meaning construction that is embedded in networks of differential relations between different actors, https://www.sciencedirect.com/science/article/abs/pii/S0363811111000944
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Rhetorical structure theory studies relations between different parts of a text http://www.cis.upenn.edu/~nenkova/Courses/cis700-2/rst.pdf
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Semantic Network Analysis as a Method for Visual Text Analytics a method for visual text analytics to support knowledge building, analytical reasoning and explorative analysis. Semantic networks are analyzed with methods of network analysis to gain quantitative and qualitative insights. Quantitative metrics can support the qualitative analysis and exploration of semantic structures. https://www.sciencedirect.com/science/article/pii/S1877042813010227
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Detecting contrast patterns in newspaper articles by combining discourse analysis and text mining Text mining aims at constructing classification models and finding interesting patterns in large text collections. This paper investigates the utility of applying these techniques to media analysis, more specifically to support discourse analysis of news reports http://www.jbe-platform.com/content/journals/10.1075/prag.21.4.07pol
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Complexity of Word Collocation Networks: A Preliminary Structural Analysis We analyzed graphically and statistically how the global properties of these networks varied across different genres, and among different network types within the same genre. Our results indicate that the distributions of network properties are visually similar but statistically apart across different genres, and interesting variations emerge when we consider different network types within a single genre.
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Discovery of Collocation Patterns: from Visual Words to Visual Phrases - collocation helps develop better image recognition and categorization systems https://ieeexplore.ieee.org/abstract/document/4270247/
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Word co-occurrence networks are one Global topology of word co-occurrence networks: beyond the two-regime power-law of the most common linguistic networks studied in the past and they are known to exhibit several interesting topological characteristics. In this article, we investigate the global topological properties of word co-occurrence networks and, in particular, present a detailed study of their spectrum. https://dl.acm.org/citation.cfm?id=1944585
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KeyGraph: automatic indexing by co-occurrence graph based on building construction metaphor. Our algorithm, KeyGraph, is based on the segmentation of a graph, representing the co-occurrence between terms in a document, into clusters. Each cluster corresponds to a concept on which an author's idea is based, and the top-ranked terms are selected as keywords using a statistic based on each term's relationship to these clusters. https://ieeexplore.ieee.org/abstract/document/670375/
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Small world of human language http://rspb.royalsocietypublishing.org/content/268/1482/2261.short
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Featuring web communities based on word co-occurrence structure of communications: 736 . We present a communication-content based generalization of an existing business-oriented classification of Web communities, using KeyGraph, a method for visualizing the co-occurrence relations between words and word clusters in text. Here, the text in a message board is analyzed with KeyGraph, and the structure obtained is shown to reflect the essence of the content-flow.
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ecological ideas
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A way of trapping carbon with "green coal" biochar
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Dumping iron dust in the ocean to remove carbon.
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plant-derived biofuels
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biofuel biochar
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hydrogen-hybrid boats
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hydrogen coal
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drinking fountains
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drinking water resources coal
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community electrical grid fuel
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species relocation resource fuel
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ecological - clever use of resources, renewing resources, producing new energy, moving relocation to the new parts of the spectrum
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ocean water
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water and carbon — fuel and medium.
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moving like water and burning carbon - breathing
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moving resources, like water and carbon around you, small-scale and big-scale
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interactions among organisms and their environment - ecological
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organisms and resources in the environment
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biodiversity, distribution, biomass, and populations of organisms, as well as cooperation and competition within and between species.
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Ecosystems are dynamically interacting systems of organisms, the communities they make up, and the non-living components of their environment.
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How can we save resources, produce the new ones, make it possible to survive, to live and develop physically, when we consume, when we lay waste — on every context. And how we can also direct energy within and modulate interactions.
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A practice of ecological operation on the physical, personal, and collective levels.
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how to relocation and redistribute energy through ecological interaction
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Main Topical Groups:
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+ ⤓ ?The topics are the nodes (words) that tend to co-occur together in the same context (next to each other).
We use a combination of clustering and graph community detection algorithm (Blondel et al based on Louvain) to identify the groups of nodes are more densely connected together than with the rest of the network. They are aligned closer to each other on the graph and are given a distinct color.
We use a combination of clustering and graph community detection algorithm (Blondel et al based on Louvain) to identify the groups of nodes are more densely connected together than with the rest of the network. They are aligned closer to each other on the graph and are given a distinct color.
Most Influential Elements:
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+ ⤓ ↻ ?We use the Jenks elbow cutoff algorithm to select the top prominent nodes that have significantly higher influence than the rest.
Click the Reveal Non-obvious button to remove the most influential words (or the ones you select) from the graph, to see what terms are hiding behind them.
The most influential nodes are either the ones with the highest betweenness centrality — appearing most often on the shortest path between any two randomly chosen nodes (i.e. linking the different distinct communities) — or the ones with the highest degree.
Click the Reveal Non-obvious button to remove the most influential words (or the ones you select) from the graph, to see what terms are hiding behind them.
The most influential nodes are either the ones with the highest betweenness centrality — appearing most often on the shortest path between any two randomly chosen nodes (i.e. linking the different distinct communities) — or the ones with the highest degree.
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Action Advice:
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Structural Gap
(ask a research question that would link these two topics):N/A
?A structural gap shows the two distinct communities (clusters of words) in this graph that are important, but not yet connected. That's where the new potential and innovative ideas may reside.
This measure is based on a combination of the graph's connectivity and community structure, selecting the groups of nodes that would either make the graph more connected if it's too dispersed or that would help maintain diversity if it's too connected.
This measure is based on a combination of the graph's connectivity and community structure, selecting the groups of nodes that would either make the graph more connected if it's too dispersed or that would help maintain diversity if it's too connected.
Latent Topical Brokers
:
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?These are the latent brokers between the topics: the nodes that have an unusually high rate of influence (betweenness centrality) to their freqency — meaning they may appear not as often as the most influential nodes but they are important narrative shifting points.
These are usually brokers between different clusters / communities of nodes, playing not easily noticed and yet important role in this network, like the "grey cardinals" of sorts.
These are usually brokers between different clusters / communities of nodes, playing not easily noticed and yet important role in this network, like the "grey cardinals" of sorts.
Emerging Topics
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Top Relations
:
The most prominent relations between the nodes that exist in this graph are shown above. We treat the graph as undirected by default as it allows us to better detect general patterns.
As an option, you can also downloaded directed bigrams above, in case the direction of the relations is important (for any application other than language).
As an option, you can also downloaded directed bigrams above, in case the direction of the relations is important (for any application other than language).
Main Topics
(according to Latent Dirichlet Allocation):loading...
Most Influential Words
(main topics and words according to LDA):loading...
LDA works only for English-language texts at the moment. More support is coming soon, subscribe @noduslabs to be informed.