Top keywords (global influence):
Top topics (local contexts):
Explore the main topics and terms outlined above or see them in the excerpts from this text below.
See the relevant data in context: click here to show the excerpts from this text that contain these topics below.
Tip: use the form below to save the most relevant keywords for this search query. Or start writing your content and see how it relates to the existing search queries and results.
Tip: here are the keyword queries that people search for but don't actually find in the search results.

landscape model of reading, priming, cooccurrence graphs

   edit   unpin & show all

 

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

   edit   unpin & show all

 

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

   edit   unpin & show all

 

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

   edit   unpin & show all

 

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

   edit   unpin & show all

 

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

   edit   unpin & show all

 

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

   edit   unpin & show all

 

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

   edit   unpin & show all

 

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

   edit   unpin & show all

 

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

   edit   unpin & show all

 

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

   edit   unpin & show all

 

Visualize co-occurrence graph from document occurrence input using R package 'igraph' http://planspace.org/2013/01/30/visualize-co_occurrence/

   edit   unpin & show all

 

java - large-scale document co-occurrence analysis - Stack Overflow http://stackoverflow.com/questions/21090020/large-scale-document-co-occurrence-analysis

   edit   unpin & show all

 

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

   edit   unpin & show all

 
in graph:
     

    Show Nodes with degree > 0:

    0 0

    Filter Graphs:


    Filter Time Range
    from: 0
    to: 0


    Recalculate Metrics   Reset Filters
    mindviral immunity:
             
    Main Topical Groups:

    N/A
    +     ?

    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.
    Most Influential Elements:
    N/A
    +     Reveal Non-obvious   ?

    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.

    Network Structure:
    N/A
     ?

    Modularity
    0
    Influence Distribution
    0
    %
    Topics Nodes in Top Topic Components Nodes in Top Comp
    0
    0
    %
    0
    0
    %
    Nodes Av Degree Density  
    0
    0
    0
     


    Undo Select   Export: PNG SVG Gexf
    Action Advice:
    N/A
    Structural Gap
    (ask a research question that would link these two topics):
    N/A
    Reveal the Gap   ?
     
    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.

    Latent Topical Brokers
    :
    N/A
    ?

    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.

    Emerging Topics
    N/A

    Top Relations
    :

    ⤓ Download   ⤓ Directed Bigrams   ?

    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).

     
    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.

     
    download data: CSV  Excel
    Please, enter a search query to visualize the difference between what people search for (related queries) and what they actually find (search results):

     
    We will build two graphs:
    1) Google search results for your query;
    2) Related searches for your query (Google's SERP);
    Click the Missing Content tab to see the graph that shows the difference between what people search for and what they actually find, indicating the content you could create to fulfil this gap.
    Find a market niche for a certain product, category, idea or service: what people are looking for but cannot yet find*

     
    We will build two graphs:
    1) the content that already exists when you make this search query (informational supply);
    2) what else people are searching for when they make this query (informational demand);
    You can then click the Niche tab to see the difference between the supply and the demand — what people need but do not yet find — the opportunity gap to fulfil.
    Please, enter your query to visualize the search results as a graph, so you can learn more about this topic:

     
       advanced settings
     
    Enter a search query to analyze the Twitter discourse around this topic (last 7 days):

     
       advanced settings

    Sign Up