DP, NP, or neither? Contours of an unresolved debate

Glossa’s Special Collection New perspectives on the NP/DP debate brings together syntactic analyses of various phenomena of complex nominals, shedding light on the central problem of their syntactic category label. In this paper, we survey arguments and analyses offered in the Special Collection, classifying their underlying assumptions and highlighting their relevance to syntactic theory more generally.

EXPRESS: Yes! I love my mother as much as myself!!! Self and mother-association effects in an Indian sample.

Information associated with the self is preferentially processed compared to others. However, cultural differences appear to exist in the way information is processed about those close to us like our mothers. In eastern compared to western cultures, information about mother seems to be processed as well as our self. However, it is not clear whether this lack of difference is due to familiarity or would extend to processing arbitrary perceptual information associated with different categorical labels. The current study employs a perceptual association paradigm in which category labels like self, mother and none are associated with arbitrary shapes to study self vs mother processing in an Indian sample. We hypothesized that there would be no difference between self and mother processing given the familial and collectivistic tendencies in India. Participants performed a matching task between shape and a pre-assigned category label, with self, mother, and none as categories in Experiment 1A and self, friend, and none as categories in Experiment 1B. Analysis of RT, accuracies and signal detection theoretic measures showed that information about mother is processed as well as self in Experiment 1A, but this effect is not present with friend in Experiment 1B. Moreover, participants’ processing for the self-associated information gets attenuated depending upon the other close person category used in the task (friend vs mother) indicating that self-information processing is dynamically dependent on the categorical contexts in which such processing takes place. Our findings have implications for understanding the processing of self-associated information across cultures and contexts.

Identification of Miao Embroidery in Southeast Guizhou Province of China Based on Convolution Neural Network

Miao embroidery of the southeast area of Guizhou province in China is a kind of precious intangible cultural heritage, as well as national costume handcrafts and textiles, with delicate patterns that require exquisite workmanship. There are various skills to make Miao embroidery; therefore, it is difficult to distinguish the categories of Miao embroidery if there is a lack of sufficient knowledge about it. Furthermore, the identification of Miao embroidery based on existing manual methods is relatively low and inefficient. Thus, in this work, a novel method is proposed to identify different categories of Miao embroidery by using deep convolutional neural networks (CNNs). Firstly, we established a Miao embroidery image database and manually assigned an accurate category label of Miao embroidery to each image. Then, a pre-trained deep CNN model is fine-tuned based on the established database to learning a more robust deep model to identify the types of Miao embroidery. To evaluate the performance of the proposed deep model for the application of Miao embroidery categories recognition, three traditional non-deep methods, that is, bag-of-words (BoW), Fisher vector (FV), and vector of locally aggregated descriptors (VLAD) are employed and compared in the experiment. The experimental results demonstrate that the proposed deep CNN model outperforms the compared three non-deep methods and achieved a recognition accuracy of 98.88%. To our best knowledge, this is the first one to apply CNNs on the application of Miao embroidery categories recognition. Moreover, the effectiveness of our proposed method illustrates that the CNN-based approach might be a promising strategy for the discrimination and identification of different other embroidery and national costume patterns.

Explanations of an Unfamiliar Phenomenon Based on Categorical Names and Metaphors

A study by Giffin and colleagues (2017) found the effect of a verbal label on the explanation of an unfamiliar phenomenon: when a name is used, people's judgments are more likely to express the belief that the phenomenon has an objective cause. This effect was demonstrated in behavior descriptions of a mental disorder that was either labeled with the fictional name “depataphy” or left unlabeled. In the present study, we replicated this effect (N = 110) and added new conditions in order to assess whether another linguistic form, a metaphor, could cause the same effect. A separate group (N = 119) evaluated two conditions wherein, instead of a verbal label, we informed participants that the internal state of the person behaving abnormally can be compared to some other event (e.g., a fire). One condition (the so-called nonconventional metaphor) emphasized that this comparison is made by the character of the story himself, and the second condition emphasized other people with a similar behavioral disorder (the so-called conventional metaphor). According to our hypothesis, only the conventional metaphor could affect the formation of explanations, because the conventionality would give the metaphor the status of a category name. The hypothesis was partially confirmed: in the condition with a nonconventional metaphor no significant effect was found, and in the condition with a conventional metaphor it was found in the answers to only one question. The results of the study are generally consistent with the interpretation by Giffin and colleagues that judgments are primarily influenced by a category label rather than other linguistic forms.

Towards Describing Visual Explanation using Machine Learning

Current available visible explanation generating systems research to easily absolve a class prediction. Still, they may additionally point out visible parameters attribute which replicate a strong category prior, though the proof may additionally not clearly be in the pic. This is specifically regarding as alternatively such marketers fail in constructing have confidence with human users. We proposed our own version, which makes a speciality of the special places of house of the seen item, together predicts the category label & interprets why the expected label is proper for the image. The machine proposes to annotate the images automatically using the Markov cache model. To annotate images, principles are represented as states through the usage of Hidden Markov model. The model parameters were estimated as part of a set of images and manual annotations. This is a great collection of checks, albeit automatically, with the possibility a posteriori of the concepts presented in her.

Signals in the Waves of Surf: Category Development and Signals in Surf Music

The aim of this paper is to investigate to what extent the understanding of market categories changes over time and how this is reflected in the importance of different category signals in periods of category maturation and revival. We test the changing influence of different types of category signals on inclusion rates of surf music compilation albums, which represent the understanding of “surf music” from a market-based perspective. We find that “elaborate” signals to the category label of surf music showed to be important during both the stage of maturity and revival. However, restricted category signals using surf slang actually lost their importance over time. Finally, signaling surf-related locations had no effect in early times, but increased chances of inclusion during a revival. By addressing these changes over time in the importance of category signals, we add to recent studies on mechanisms of categorization during different stages of category development.

Categorizing Multi-Label Product Questionnaires through SVM Based Click stream

In this paper, Question Categorization (QC) has been studied most primarily in order to understand customers' search intention. In both of these searches, the items in the question list relate to the category label belonging to the taxonomy tree that is being examined. Despite this, search queries about the product usually vary depending on what is vague, and introduce new products over time, seasonal trends and narrow. Traditional supervised approaches to E-Commerce QC are not possible due to the high volume of traffic and high cost for manual annotation in E-Commerce search engines. Here, clickstream data is utilized to determine the effectiveness of a channel's marketplace. So, using the customer's click concept, to collect large-scale question categorization data, this paper uses unsupervised methods that means SVM algorithm is mainly used in this system. Here the data is in the multiclass and multi-label classifier is used to classify them. This paper gets on a large multi-label data set with specific and individual queries from a specific category. In this paper, a comparison of different sophisticated text classifiers is viewed. This paper calculates the micro-F1 scores of top and leaf, which are considered to be a linear SVM-ensemble.