Early activation of category information in visual word recognition

2006 ◽  
Vol 1 (1) ◽  
pp. 35-58 ◽  
Author(s):  
Kenneth I. Forster
Keyword(s):  
Decision Process ◽  
Physical Object ◽  
Semantic Feature ◽  
Semantic Features ◽  
Activation Model ◽  
Semantic Categorization ◽  
Category Information ◽  
Semantic Fields ◽  
Per Se ◽  
Feature Unit

In semantic categorization, nonwords that are neighbors of exemplars (e.g., turple in an animal categorization task) cause interference, but neighbors of nonexemplars (e.g., tabric) do not. This can be explained in a cascaded activation model in which the decision process selectively monitors activation in a category-relevant semantic feature unit. However, it is shown that this is true only for some categories. With the broad category “Physical Object”, interference is produced by nonwords based on both exemplars (e.g., himmer) and nonexemplars (e.g., travity). However, no interference is produced when the category is changed to “Animal”. This shows that only some semantic feature units can be monitored. It is proposed that what is being monitored are not in fact semantic features per se, but rather links to semantic fields defined on the basis of patterns of lexical co-occurrence.

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.

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.

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.

Effects of Semantic Feature Type, Diversity, and Quantity on Semantic Feature Analysis Treatment Outcomes in Aphasia

2020 ◽  
pp. 1-15
Author(s):  
William S. Evans ◽  
Rob Cavanaugh ◽  
Michelle L. Gravier ◽  
Alyssa M. Autenreith ◽  
Patrick J. Doyle ◽  
...  
Keyword(s):  
Treatment Outcomes ◽  
Treatment Response ◽  
Semantic Feature ◽  
Feature Analysis ◽  
Semantic Features ◽  
Feature Generation ◽  
Baseline Design ◽  
Improve Treatment ◽  
Future Work ◽  
Semantic Feature Analysis

Purpose Semantic feature analysis (SFA) is a naming treatment found to improve naming performance for both treated and semantically related untreated words in aphasia. A crucial treatment component is the requirement that patients generate semantic features of treated items. This article examined the role feature generation plays in treatment response to SFA in several ways: It attempted to replicate preliminary findings from Gravier et al. (2018), which found feature generation predicted treatment-related gains for both trained and untrained words. It examined whether feature diversity or the number of features generated in specific categories differentially affected SFA treatment outcomes. Method SFA was administered to 44 participants with chronic aphasia daily for 4 weeks. Treatment was administered to multiple lists sequentially in a multiple-baseline design. Participant-generated features were captured during treatment and coded in terms of feature category, total average number of features generated per trial, and total number of unique features generated per item. Item-level naming accuracy was analyzed using logistic mixed-effects regression models. Results Producing more participant-generated features was found to improve treatment response for trained but not untrained items in SFA, in contrast to Gravier et al. (2018). There was no effect of participant-generated feature diversity or any differential effect of feature category on SFA treatment outcomes. Conclusions Patient-generated features remain a key predictor of direct training effects and overall treatment response in SFA. Aphasia severity was also a significant predictor of treatment outcomes. Future work should focus on identifying potential nonresponders to therapy and explore treatment modifications to improve treatment outcomes for these individuals. Supplemental Material https://doi.org/10.23641/asha.12462596

The Cluster Configuration of the Semantic Space of Polysituational Juridical Verbs with the Subject «Judge» in English

2020 ◽  
pp. 101-115
Author(s):  
Anton Silnitsky
Keyword(s):  
Cluster Analysis ◽  
Semantic Space ◽  
Semantic Feature ◽  
Negative Evaluation ◽  
Semantic Features ◽  
Structural Elements ◽  
Social Sphere ◽  
Quantitative Linguistics ◽  
Cluster Configuration ◽  
The Subject

The article is dedicated to the analysis of the semantic space of polysituational juridical verbs with the subject «judge» in English. The theoretical part of the research combines some aspects of «verb-centric» conception and «quantitative linguistics». A polisituational verb» implies a complex verbal situation consisting of several simple situations. The notion of a «juridical verb» correlates with a juridical social sphere. The article substantiates diagnostic semantic features which constitute the structural elements of the semantic space. The semantic features are organized into semantic plans. One semantic plan («chronostructural») being «categorical» integrates the features «basic situation» and «background situation». The «subcategorical» semantic plans are: «teleological » (the features: «definite» and «indefinite» situations), «temporal» (the features: «retrospective» and «prospective» situations), «motivational» («strong» and «weak» situations), «adject-legal» («base», «sanctions» and «processual» law), «adject-functional» («instrumental» and «purpose-oriented» law), «adjectsubstantive» («material» and «person» adject), «interactive» («convergent», «divergent » and «invergent»), «hierarchical» («dominant» and «subordinate» subject), «axiological» («positive» and «negative» evaluation). Each subcategorical semantic feature (exept for temporal plan features) correlates with one of the categorical («basic situation» or «background situation»). The actant «judge» is modeled by means of the following features within a basic situation: «processual» law, «instrumental» law and «dominant» subject. By means of cluster analysis the semantic features were grouped into two clusters («Divergent» and «Convergent-invergent») in correlation with an accusatorial or a justificatory-undifferentiated sentence. The differential (most relevant) semantic characteristics within the basic situation are the features of interactive and motivational plans, within the background situation they constitute the features of adject-legal, adject-functional and interactive plans.

INTEGRAL AND DIFFERENTIAL SEMANTIC FEATURES OF LEXICAL AND PHRASEOLOGICAL UNITS OF THE SEMANTIC FIELD “BAD PHYSICAL CONDITION”

2020 ◽  
Vol XIII (XIII) ◽  
pp. 109-114
Author(s):  
O.P. Motina ◽  
Keyword(s):  
Physical Condition ◽  
Component Analysis ◽  
Semantic Feature ◽  
Semantic Features ◽  
Semantic Field

The article presents the results of the component analysis of lexical and phraseological units of the semantic field "bad physical condition" in the English and Russian languages. An integral semantic feature and differential semes of unit meanings are identified, which makes it possible to single out four subfields within the considered semantic field.

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).

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