Semantic ambiguity and memory

2021 ◽  
Vol 121 ◽  
pp. 104286
Author(s):  
C.J. Brainerd ◽  
M. Chang ◽  
D.M. Bialer ◽  
Michael P. Toglia
Keyword(s):  
Semantic Ambiguity

Sentiment analysis of students in ideological and political teaching based on artificial intelligence and data mining

2021 ◽  
pp. 1-10
Author(s):  
Wan Hongmei ◽  
Tang Songlin
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Sentiment Analysis ◽  
Machine Learning Algorithms ◽  
Classroom Teaching ◽  
Functional Modules ◽  
Semantic Ambiguity ◽  
Calculation Methods ◽  
Multiple Factors ◽  
Quantitative Calculation

In order to improve the efficiency of sentiment analysis of students in ideological and political classrooms, under the guidance of artificial intelligence ideas, this paper combines data mining and machine learning algorithms to improve and propose a method for quantifying the semantic ambiguity of sentiment words. Moreover, this paper designs different quantitative calculation methods of sentiment polarity intensity, and constructs video image sentiment recognition, text sentiment recognition, and speech sentiment recognition functional modules to obtain a combined sentiment recognition model. In addition, this article studies student emotions in ideological and political classrooms from the perspective of multimodal transfer learning, and optimizes the deep representation of images and texts and their corresponding deep networks through single-depth discriminative correlation analysis. Finally, this paper designs experiments to verify the model effect from two perspectives of single factor sentiment analysis and multi-factor sentiment analysis. The research results show that comprehensive analysis of multiple factors can effectively improve the effect of sentiment analysis of students in ideological and political classrooms, and enhance the effect of ideological and political classroom teaching.

Emotionality effects in ambiguous word recognition: The crucial role of the affective congruence between distinct meanings of ambiguous words

2021 ◽  
pp. 174702182199000
Author(s):  
Pilar Ferré ◽  
Juan Haro ◽  
Daniel Huete-Pérez ◽  
Isabel Fraga
Keyword(s):  
Word Processing ◽  
Reaction Times ◽  
Ambiguous Word ◽  
Semantic Ambiguity ◽  
Negative Valence ◽  
Ambiguous Words ◽  
Positive Valence ◽  
Affective Meanings ◽  
Valence Effect ◽  
Affective Word

There is substantial evidence that affectively charged words (e.g., party or gun) are processed differently from neutral words (e.g., pen), although there are also inconsistent findings in the field. Some lexical or semantic variables might explain such inconsistencies, due to the possible modulation of affective word processing by these variables. The aim of the present study was to examine the extent to which affective word processing is modulated by semantic ambiguity. We conducted a large lexical decision study including semantically ambiguous words (e.g., cataract) and semantically unambiguous words (e.g., terrorism), analysing the extent to which reaction times (RTs) were influenced by their affective properties. The findings revealed a valence effect in which positive valence made RTs faster, whereas negative valence slowed them. The valence effect diminished as the semantic ambiguity of words increased. This decrease did not affect all ambiguous words, but was observed mainly in ambiguous words with incongruent affective meanings. These results highlight the need to consider the affective properties of the distinct meanings of ambiguous words in research on affective word processing.

scholarly journals ERN: Edge Loss Reinforced Semantic Segmentation Network for Remote Sensing Images

2018 ◽  
Vol 10 (9) ◽  
pp. 1339 ◽  
Author(s):  
Shuo Liu ◽  
Wenrui Ding ◽  
Chunhui Liu ◽  
Yu Liu ◽  
Yufeng Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Edge Detection ◽  
Qualitative Evaluation ◽  
Semantic Segmentation ◽  
Model Complexity ◽  
Semantic Ambiguity ◽  
Remote Sensing Images ◽  
Future Networks ◽  
Spatial Boundary ◽  
Boundary Information

The semantic segmentation of remote sensing images faces two major challenges: high inter-class similarity and interference from ubiquitous shadows. In order to address these issues, we develop a novel edge loss reinforced semantic segmentation network (ERN) that leverages the spatial boundary context to reduce the semantic ambiguity. The main contributions of this paper are as follows: (1) we propose a novel end-to-end semantic segmentation network for remote sensing, which involves multiple weighted edge supervisions to retain spatial boundary information; (2) the main representations of the network are shared between the edge loss reinforced structures and semantic segmentation, which means that the ERN simultaneously achieves semantic segmentation and edge detection without significantly increasing the model complexity; and (3) we explore and discuss different ERN schemes to guide the design of future networks. Extensive experimental results on two remote sensing datasets demonstrate the effectiveness of our approach both in quantitative and qualitative evaluation. Specifically, the semantic segmentation performance in shadow-affected regions is significantly improved.

scholarly journals Determinants of translation ambiguity

2016 ◽  
Vol 6 (3) ◽  
pp. 290-307 ◽  
Author(s):  
Tamar Degani ◽  
Anat Prior ◽  
Chelsea M. Eddington ◽  
Ana B. Arêas da Luz Fontes ◽  
Natasha Tokowicz
Keyword(s):  
Second Language ◽  
Language Learning ◽  
Second Language Learning ◽  
Lexical Processing ◽  
English Word ◽  
Direct Access ◽  
Semantic Ambiguity ◽  
Dominance Structure ◽  
Translation Ambiguity

Abstract Ambiguity in translation is highly prevalent, and has consequences for second-language learning and for bilingual lexical processing. To better understand this phenomenon, the current study compared the determinants of translation ambiguity across four sets of translation norms from English to Spanish, Dutch, German and Hebrew. The number of translations an English word received was correlated across these different languages, and was also correlated with the number of senses the word has in English, demonstrating that translation ambiguity is partially determined by within-language semantic ambiguity. For semantically-ambiguous English words, the probability of the different translations in Spanish and Hebrew was predicted by the meaning-dominance structure in English, beyond the influence of other lexical and semantic factors, for bilinguals translating from their L1, and translating from their L2. These findings are consistent with models postulating direct access to meaning from L2 words for moderately-proficient bilinguals.

Semantic ambiguity norms for 530 Spanish words

2016 ◽  
Vol 38 (2) ◽  
pp. 457-475 ◽  
Author(s):  
JUAN HARO ◽  
PILAR FERRÉ ◽  
ROGER BOADA ◽  
JOSEP DEMESTRE
Keyword(s):  
Language Processing ◽  
Ambiguous Word ◽  
Age Of Acquisition ◽  
Emotional Valence ◽  
Objective Measures ◽  
Semantic Ambiguity ◽  
Subjective Ratings ◽  
Subjective Measures ◽  
Word Meanings ◽  
Ambiguous Words

ABSTRACTThis study presents semantic ambiguity norms for 530 Spanish words. Two subjective measures of semantic ambiguity and two subjective measures of relatedness of ambiguous word meanings were collected. In addition, two objective measures of semantic ambiguity were included. Furthermore, subjective ratings were obtained for some relevant lexicosemantic variables, such as concreteness, familiarity, emotional valence, arousal, and age of acquisition. In sum, the database overcomes some of the limitations of the published databases of Spanish ambiguous words; in particular, the scarcity of measures of ambiguity, the lack of relatedness of ambiguous word meanings measures, and the absence of a set of unambiguous words. Thus, it will be very helpful for researchers interested in exploring semantic ambiguity as well as for those using semantic ambiguous words to study language processing in clinical populations.

scholarly journals Dissociating Frontotemporal Contributions to Semantic Ambiguity Resolution in Spoken Sentences

2011 ◽  
Vol 22 (8) ◽  
pp. 1761-1773 ◽  
Author(s):  
J. M. Rodd ◽  
I. S. Johnsrude ◽  
M. H. Davis
Keyword(s):  
Ambiguity Resolution ◽  
Semantic Ambiguity

Three accounts of propositional relation reports

2018 ◽  
Vol 15 (2) ◽  
pp. 237-269 ◽  
Author(s):  
Wayne A. Davis
Keyword(s):  
Speech Acts ◽  
Propositional Attitudes ◽  
Context Sensitivity ◽  
Semantic Ambiguity ◽  
Pragmatic Enrichment ◽  
Scott Soames ◽  
Cognitive Event

Abstract Peter Hanks and Scott Soames have developed a theory of propositions as structured cognitive event types, as have I in earlier works. They use the theory to offer similar accounts of transparent propositional relation reports, and very different accounts of opaque reports. For both, the sentences used to report propositional attitudes or speech acts are semantically unambiguous. Hanks invokes context-sensitivity, Soames pragmatics, to account for the different interpretations. I raise problems and offer solutions. Their accounts succumb to the non-compositionality of transparent reports, and wrongly predict that all propositional relation reports have both transparent and opaque interpretations. Soames’s pragmatic enrichment account of the opaque interpretation is unfounded, and forces him to conclude that competent speakers do not know what the sentences they use mean. The notion of an “object-dependent” or “bare” proposition is both problematic and unnecessary. I offer a new account, on which propositional relation reports have the semantic ambiguity characteristic of idioms, with the transparent interpretation being highly but not completely compositional.

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