scholarly journals Asymmetric Underlying Mechanisms of Relation-Based and Property-Based Noun–Noun Conceptual Combination

2021 ◽  
Vol 12 ◽  
Author(s):  
Mingyeong Choi ◽  
Sangsuk Yoon
Keyword(s):  
Semantic Information ◽  
Head Noun ◽  
Semantic Feature ◽  
Future Research ◽  
Decision Task ◽  
Semantic Features ◽  
Conceptual Combination ◽  
Semantic Activation ◽  
Processing Mechanisms ◽  
Combinatorial Process

Conceptual combination is a fundamental human cognitive ability by which people can experience infinite thinking by artfully combining finite knowledge. For example, one can instantly combine “cactus” and “fish” together as “prickly fish” even if one has never previously heard of a “cactus fish.” Although two major combinatorial types—property and relational combinations—have been identified, the underlying processes of each remain elusive. This study investigates the asymmetric processing mechanisms underlying property and relational combinations by examining differential semantic activation during noun–noun conceptual combination. Across two experiments utilizing each combinatorial process as semantic priming and implementing a lexical decision task immediately after combination, we measure and compare the semantic activation patterns of intrinsic and extrinsic semantic features in these two combinatorial types. We found converging evidence that property and relational combinations involve asymmetric semantic information and entail distinct processing mechanisms. In property combination, the intrinsic feature in the modifier concept showed greater activation than the semantic feature of the same dimension in the head concept. In contrast, in relational combination, the extrinsic semantic feature in the head concept and the whole modifier concept showed similar levels of activation. Moreover, our findings also showed that these patterns of semantic activation occurred only when the combinatorial process was complete, indicating that accessing the same lexical-semantic information is not sufficient to observe asymmetric patterns. These findings demonstrate that property combination involves replacing a specific semantic feature of the head noun with that of the modifier noun, whereas relational combination involves completing the semantic feature of the head noun with the whole modifier concept. We discuss the implications of these findings, research limitations, and future research directions.

scholarly journals A database of semantic features for chosen concepts (Attested in 8- to 10-year-old Czech pupils)

2017 ◽  
Vol 18 (1) ◽  
pp. 71-97
Author(s):  
Kristýna Konečná ◽  
Kristýna Vaňkátová
Keyword(s):  
School Children ◽  
Conceptual Knowledge ◽  
Academic Community ◽  
Semantic Feature ◽  
English Speakers ◽  
Future Research ◽  
Semantic Features ◽  
Research Papers ◽  
Semantic Categories ◽  
Insight Into

Abstract In this paper, a database of semantic features is presented. 104 nominal concepts from 13 semantic categories were described by young Czech school children. They were asked to respond to the question “what is it, what does it mean?” by listing different kinds of properties for concepts in writing. Their responses were broken down into semantic features and the database was prepared using a set of pre-established rules. The method of database design, with an emphasis on the way features were recorded, is described in detail within this article. The data were statistically analysed and interpreted and the results along with database usage methodologies are discussed. The goal of this research is to produce a complex database to be used in future research relating to semantic features and therefore it has been published online for use by the wider academic community. At present, databases have been published based on data gathered from adult English and Czech speakers; however participation in this study was limited specifically to young Czech-speaking children. Thus, this database is characteristically unique as it provides important insight into this specific age and language group’s conceptual knowledge. The research is inspired primarily by research papers concerning semantic feature production obtained from adult English speakers (McRae, de Sa, and Seidenberg, 1997; McRae, Cree, Seidenberg, and McNorgan, 2005; Vinson and Vigliocco, 2008).

scholarly journals The Neural Underpinnings of Processing Newly Taught Semantic Information: The Role of Retrieval Practice

2021 ◽  
pp. 1-17
Author(s):  
Eileen Haebig ◽  
Laurence B. Leonard ◽  
Patricia Deevy ◽  
Jennifer Schumaker ◽  
Jeffrey D. Karpicke ◽  
...  
Keyword(s):  
Preschool Children ◽  
Semantic Information ◽  
Retrieval Practice ◽  
Word Meaning ◽  
Semantic Feature ◽  
Semantic Features ◽  
Word Form ◽  
Spaced Retrieval ◽  
Children’S Learning ◽  
Children's Learning

Purpose Recent behavioral studies have demonstrated the effectiveness of implementing retrieval practice into learning tasks for children. Such approaches have revealed that repeated spaced retrieval (RSR) is particularly effective in promoting children's learning of word form and meaning information. This study further examines how retrieval practice enhances learning of word meaning information at the behavioral and neural levels. Method Twenty typically developing preschool children were taught novel words using an RSR learning schedule for some words and an immediate retrieval (IR) learning schedule for other words. In addition to the label, children were taught two arbitrary semantic features for each item. Following the teaching phase, children's learning was tested using recall tests. In addition, during the 1-week follow-up, children were presented with pictures and an auditory sentence that correctly labeled the item but stated correct or incorrect semantic information. Event-related brain potentials (ERPs) were time locked to the onset of the words noting the semantic feature. Children provided verbal judgments of whether the semantic feature was correctly paired with the item. Results Children recalled more labels and semantic features for items that had been taught in the RSR learning schedule relative to the IR learning schedule. ERPs also differentiated the learning schedules. Mismatching label–meaning pairings elicited an N400 and late positive component (LPC) for both learning conditions; however, mismatching RSR pairs elicited an N400 with an earlier onset and an LPC with a longer duration, relative to IR mismatching label–meaning pairings. These ERP timing differences indicated that the children were more efficient in processing words that were taught in the RSR schedule relative to the IR learning schedule. Conclusions Spaced retrieval practice promotes learning of both word form and meaning information. The findings lay the necessary groundwork for better understanding of processing newly learned semantic information in preschool children. Supplemental Material https://doi.org/10.23641/asha.15063060

scholarly journals Feature distinctiveness effects in language acquisition and lexical processing: Insights from megastudies

2019 ◽  
Author(s):  
Cynthia S. Q. Siew
Keyword(s):  
Language Acquisition ◽  
Lexical Decision ◽  
Semantic Processing ◽  
Lexical Processing ◽  
Semantic Feature ◽  
Decision Task ◽  
Semantic Features ◽  
Decision Performance ◽  
Semantic Decision ◽  
Visual Lexical Decision

Semantic features are central to many influential theories of word meaning and semantic memory, but new methods of quantifying the information embedded in feature production norms are needed to advance our understanding of semantic processing and language acquisition. This paper capitalized on databases of semantic feature production norms and age-of-acquisition ratings, and megastudies including the English Lexicon Project and the Calgary Semantic Decision Project, to examine the influence of feature distinctiveness on language acquisition, visual lexical decision, and semantic decision. A feature network of English words was constructed such that edges in the network represented feature distance, or dissimilarity, between words (i.e., Jaccard and Manhattan distances of probability distributions of features elicited for each pair of words), enabling us to quantify the relative feature distinctiveness of individual words relative to other words in the network. Words with greater feature distinctiveness tended to be acquired earlier. Regression analyses of megastudy data revealed that Manhattan feature distinctiveness inhibited performance on the visual lexical decision task, facilitated semantic decision performance for concrete concepts, and inhibited semantic decision performance for abstract concepts. These results demonstrate the importance of considering the structural properties of words embedded in a semantic feature space in order to increase our understanding of semantic processing and language acquisition.

scholarly journals An Efficient Module for Instance Segmentation Based on Multi-Level Features and Attention Mechanisms

2021 ◽  
Vol 11 (3) ◽  
pp. 968
Author(s):  
Yingchun Sun ◽  
Wang Gao ◽  
Shuguo Pan ◽  
Tao Zhao ◽  
Yahui Peng
Keyword(s):  
Feature Extraction ◽  
Spatial Structure ◽  
Semantic Feature ◽  
Semantic Features ◽  
Segmentation Method ◽  
Spatial Dimensions ◽  
Feature Pyramid ◽  
Multi Level ◽  
High Level ◽  
Instance Segmentation

Recently, multi-level feature networks have been extensively used in instance segmentation. However, because not all features are beneficial to instance segmentation tasks, the performance of networks cannot be adequately improved by synthesizing multi-level convolutional features indiscriminately. In order to solve the problem, an attention-based feature pyramid module (AFPM) is proposed, which integrates the attention mechanism on the basis of a multi-level feature pyramid network to efficiently and pertinently extract the high-level semantic features and low-level spatial structure features; for instance, segmentation. Firstly, we adopt a convolutional block attention module (CBAM) into feature extraction, and sequentially generate attention maps which focus on instance-related features along the channel and spatial dimensions. Secondly, we build inter-dimensional dependencies through a convolutional triplet attention module (CTAM) in lateral attention connections, which is used to propagate a helpful semantic feature map and filter redundant informative features irrelevant to instance objects. Finally, we construct branches for feature enhancement to strengthen detailed information to boost the entire feature hierarchy of the network. The experimental results on the Cityscapes dataset manifest that the proposed module outperforms other excellent methods under different evaluation metrics and effectively upgrades the performance of the instance segmentation method.

The effects of emotionality and lexical category on L2 word processing in different tasks: Evidence from late Chinese–English bilinguals

2021 ◽  
pp. 174702182110418
Author(s):  
Xiaogen Liao ◽  
Chuanbin Ni
Keyword(s):  
Stroop Task ◽  
Word Processing ◽  
English Word ◽  
Task Type ◽  
Future Research ◽  
Decision Task ◽  
Emotional Stroop ◽  
Emotional Words ◽  
Emotional Stroop Task ◽  
Lexical Categories

Although it has been well established that emotional content influences language comprehension, the effects of emotionality on L2 (second language: English) word processing require further clarification. Notably, most previous studies unsystematically mixed words of different lexical categories, although they often showed processing differences. Here, using the same set of tightly matched negative, positive, and neutral words across three lexical categories (i.e., nouns, verbs, adjectives), we examined the effects of emotionality and lexical category on L2 word processing by conducting three experiments. In these experiments, three groups of late Chinese–English bilinguals performed three tasks: the emotional Stroop task (Experiment 1), the lexical decision task (Experiment 2), and the emotional categorisation task (Experiment 3), respectively. Overall, our data suggested that emotionality and lexical category exerted no influence on L2 word processing in the emotional Stroop task, but acted interactively to influence it in the other two tasks. The results evidenced that the processing of L2 emotional words was sensitive to task type. Therefore, we conclude that future research on L2 word processing should fully consider the emotionality, lexical category, and task type.

scholarly journals Stem Alternation in Tłı̨chǫ Yatıì Classificatory Verbs: A Cognitive Semantic Account

2020 ◽  
Author(s):  
Hussein Al-Bataineh
Keyword(s):  
Semantic Feature ◽  
The Other ◽  
Semantic Features ◽  
Semantic Account ◽  
Documentary Sources ◽  
Feature Geometry ◽  
Cognitive Semantic ◽  
Field Notes

This paper investigates the phenomenon of ‘classificatory verbs,’ i.e., a set of motion and positional verbs that show stem alternation depending on the semantic features of one of their arguments. The data is drawn mainly from Tłı̨chǫ Yatıì Multimedia Dictionary, Nicholas Welch’s field notes, and other documentary sources of the language. Tłı̨chǫ classificatory verbs are presented and analyzed in detail. The paper argues that Tłı̨chǫ Yatıì classificatory verbs belong to four semantic subclasses and that these subclasses show a decreasing degree of stem alternations related to argument classification. The inconsistency in stem alternation is triggered by the presence or absence of some semantic features that determine the number of stem allomorphs. Locative verbs are affected by the [COMFORT] feature, and the other three sets are influenced by [TRANSFER], [INITIAL AGENTIVE] and [FINAL AGENTIVE] features. Moreover, the paper outlines a semantic feature geometry that accounts for the observed regularities in classificatory verb stems and their possible variations intra- and cross-linguistically.

scholarly journals Chinese Event Extraction Based on Attention and Semantic Features: A Bidirectional Circular Neural Network

2018 ◽  
Vol 10 (10) ◽  
pp. 95 ◽  
Author(s):  
Yue Wu ◽  
Junyi Zhang
Keyword(s):  
Neural Network ◽  
Attention Mechanism ◽  
Semantic Feature ◽  
Word Embedding ◽  
Event Extraction ◽  
Semantic Features ◽  
Improve Performance

Chinese event extraction uses word embedding to capture similarity, but suffers when handling previously unseen or rare words. From the test, we know that characters may provide some information that we cannot obtain in words, so we propose a novel architecture for combining word representations: character–word embedding based on attention and semantic features. By using an attention mechanism, our method is able to dynamically decide how much information to use from word or character level embedding. With the semantic feature, we can obtain some more information about a word from the sentence. We evaluate different methods on the CEC Corpus, and this method is found to improve performance.

scholarly journals The Polysemy of “chɔ̂ɔp” in Thai: A Pragmatically Motivated Phenomenon

MANUSYA ◽  
2009 ◽  
Vol 12 (3) ◽  
pp. 75-82
Author(s):  
Kandaporn Jaroenkitboworn
Keyword(s):  
Point Of View ◽  
Semantic Feature ◽  
Transitional Period ◽  
Word Class ◽  
Semantic Features ◽  
Social Standing ◽  
Part Of Speech ◽  
Characteristic Behavior ◽  
Actual Use ◽  
Pragmatic Motivation

This paper analyzes the word chɔ̂ɔp in Thai, which normally signifies three different meanings, namely ‘to be right’, ‘to like’ and ‘often’. The result of the analysis shows that it is more likely that the polysemy of chɔ̂ɔp arises from pragmatic motivation. Pragmatic motivation, which covers factors such as speakers’ attitude, intention, point of view, behavior and social standing, can affect actual use of language. Pragmatically, the word chɔ̂ɔp that means ‘to be right’ can easily lead to an action of agreement. In other words, when we regard something right; we tend to agree on it without argument. This attitude is related to another meaning of chɔ̂ɔp in the way that the degree of agreeability is strengthened into the meaning ‘to like’, or even ‘to love’ and ‘to enjoy’ sometimes. Also, when we like something, or even love and enjoy some activity, this kind of feeling can motivate us to do it again and again and thus we come to have a characteristic behavior. This typical behavior can consequently cause semantic features like [habitual] and [iterative] to occur. With the semantic feature [iterative], the word chɔ̂ɔp then has yet another meaning as ‘often’. This paper also discusses the grammaticalization of the word chɔ̂ɔp from a verb which means ‘to like’ into an adverb of frequency that means ‘often’ i.e. there is a change of word class or part of speech. It was found that there are many cases of chɔ̂ɔp that appear syntactically and semantically ambiguous, or, in other words they are in a transitional period of word class change. This paper indicates that such an ambiguity or incipient grammaticalization is motivated by the speaker’s attitude and point of view.

Vocabulary skill: single-case assessment of automaticity of word recognition in a timed lexical decision task

1995 ◽  
Vol 11 (2) ◽  
pp. 121-136 ◽  
Author(s):  
Norman Segalowitz ◽  
Vivien Watson ◽  
Sidney Segalowitz
Keyword(s):  
Word Recognition ◽  
Lexical Decision ◽  
Lexical Decision Task ◽  
Response Times ◽  
Single Case ◽  
English Word ◽  
Decision Task ◽  
Vocabulary Assessment ◽  
Processing Mechanisms ◽  
Assessment Research

This study illustrates, in the context of vocabulary assessment research, a procedure for analysing a single subject's variability of response times (RTs) in a simple, timed lexical decision task. Following the interpretation developed in Segalowitz and Segalowitz (1993) for RT variability as reflection of the automatic/controlled nature of underlying processing mechanisms, it was possible to draw conclusions about the extent to which second language English word recognition in this subject was subserved by automatic as opposed to controlled processes. The study also examined the development of automaticity in word recognition skill for a small, selected vocabulary as a function of reading experience during a three-week testing period. The general implications of this methodology for assessing vocabulary skill in a single case are discussed.

Semantic Models and Translating

Target ◽  
1994 ◽  
Vol 6 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Paul Kussmaul
Keyword(s):  
Word Meaning ◽  
Semantic Feature ◽  
Feature Analysis ◽  
Semantic Features ◽  
Word Meanings ◽  
The Core ◽  
Semantic Models ◽  
Structural Semantics ◽  
Semantic Feature Analysis

Abstract This paper examines the relevance of three semantic models for translation. Structural semantics, more specifically semantic feature analysis, has given rise to the maxim that we should translate "bundles of semantic features". Prototype semantics suggests that word-meanings have cores and fuzzy edges which are influenced by culture. For translation this means that we do not necessarily translate bundles of features but have to decide whether to focus on the core or the fuzzy edges of the meaning of a particular word. Scenesand-frames semantics suggests that word meaning is influenced by context and the situation we are in. Word-meaning is thus not static but dynamic, and it is this dynamism which should govern our decisions as translators.

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