The relationship between reading literary novels and predictive inference generation

This study examined the relationship between reading literary novels and generating predictive inferences by analyzing a corpus of Japanese novels. Latent semantic analysis (LSA) was used to capture the statistical structure of the corpus. Then, the authors asked 74 Japanese college students to generate predictive inferences (e.g., “The newspaper burned”) in response to Japanese event sentences (e.g., “A newspaper fell into a bonfire”) and obtained more than 5,000 predicted events. The analysis showed a significant relationship between LSA similarity between the event sentences and the predicted events and frequency of the predicted events. This result suggests that exposure to literary works may help develop readers’ inference generation skills. In addition, two vector operation methods for sentence vector constructions from word vectors were compared: the “Average” method and the “Predication Algorithm” method (Kintsch, 2001). The results support the superiority of the Predication Algorithm method over the Average method.

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Effect of Alignment on Text Cohesion in the Continuation Task

English Language Teaching ◽

10.5539/elt.v9n10p133 ◽

2016 ◽

Vol 9 (10) ◽

pp. 133 ◽

Cited By ~ 1

Author(s):

Lin Jiang ◽

Xin Xu

Keyword(s):

College Students ◽

Latent Semantic Analysis ◽

Semantic Analysis ◽

Think Aloud ◽

Collaborative Dialogue ◽

Think Aloud Protocols ◽

Text Cohesion ◽

Task Conditions ◽

Positive Effect ◽

Writing Task

<p>A continuation task provides learners with a text with its ending removed and requires them to complete it through writing in a most coherent and logical way. The current study investigated (a) whether the continuation task had a positive effect on text cohesion and (b) whether texts produced by pairs exhibited higher cohesion than those produced by individual learners. A total of 80 college students were randomly assigned to one of three task conditions: 1) 40 students working in pairs in a continuation task; 2) 20 working individually in a continuation task; and 3) 20 working individually in a picture writing task. Text cohesion was analyzed by using three indices from Coh-metrix: Argument Overlap, Latent Semantic Analysis, and Causal Cohesion. Moreover, the collaborative dialogue and think-aloud protocols were collected and transcribed for identifying language-related episodes (LREs). The results showed that learners in Condition 1 produced the highest text cohesion while those in Conditions 3 the lowest. Furthermore, learners in Condition 1 produced more cohesion-related LREs, especially proportionally more correctly resolved LREs than those in Conditions 2 and 3. The implications of these findings from the perspective of alignment are discussed.</p>

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Latent semantic analysis for tagging activation states and identifiability in northwestern Mexican news outlets

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-219235 ◽

2021 ◽

pp. 1-9

Author(s):

Manuel-Alejandro Sánchez-Fernández ◽

Alfonso Medina-Urrea ◽

Juan-Manuel Torres-Moreno

Keyword(s):

Random Forests ◽

Noun Phrase ◽

Latent Semantic Analysis ◽

Semantic Analysis ◽

Noun Phrases ◽

Inflectional Morphology ◽

Bag Of Words ◽

Binary Division ◽

Correlation Tests ◽

The Relationship

The present work aims to study the relationship between measures, obtained from Latent Semantic Analysis (LSA) and a variant known as SPAN, and activation and identifiability states (Informative States) of referents in noun phrases present in journalistic notes from Northwestern Mexican news outlets written in Spanish. The aim and challenge is to find a strategy to achieve labelling of new / given information in the discourse rooted in a theoretically linguistic stance. The new / given distinction can be defined from different perspectives in which it varies what linguistic forms are taken into account. Thus, the focus in this work is to work with full referential devices (n = 2 388). Pearson’s R correlation tests, analysis of variance, graphical exploration of the clustering of labels, and a classification experiment with random forests are performed. For the experiment, two groups were used: noun phrases labeled with all 10 tags of informative states and a binary labelling, as well as the use of two bags-of-words for each noun phrase: the interior and the exterior. It was found that using LSA in conjunction with the inner bag of words can be used to classify certain informational states. This same measure showed good results for the binary division, detecting which sentences introduce new referents in discourse. In previous work using a similar method in noun phrases in English, 80% accuracy (n = 478) was reached in their classification exercise. Our best test for Spanish reached 79%. No work on Spanish using this method has been done before and this kind of experiment is important because Spanish exhibits a more complex inflectional morphology.

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Predictive Inference Activation

Swiss Journal of Psychology ◽

10.1024/1421-0185/a000081 ◽

2012 ◽

Vol 71 (3) ◽

pp. 141-148 ◽

Cited By ~ 2

Author(s):

Doriane Gras ◽

Hubert Tardieu ◽

Serge Nicolas

Keyword(s):

Naming Task ◽

Word Naming ◽

Predictive Inference ◽

Text Reading ◽

Predictive Inferences

Predictive inferences are anticipations of what could happen next in the text we are reading. These inferences seem to be activated during reading, but a delay is necessary for their construction. To determine the length of this delay, we first used a classical word-naming task. In the second experiment, we used a Stroop-like task to verify that inference activation was not due to strategies applied during the naming task. The results show that predictive inferences are naturally activated during text reading, after approximately 1 s.

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