Aspects Of Classification Research Articles

The Aspect and Tense Verb Categories and Their Relations

The paper aims at analyzing the problems of the verb categories: aspect and tense and their relations in the traditional aspect theory. The verb is one of the complicated parts of speech possessing several categories as aspect, tense, mood, voice, person, and number. The aspect category is one of the main verb categories. If verb category expresses action, the aspect category expresses its stages of internal development: process, result, fact, protracted and others. In spite of having the aspect category in their matrixes, some languages are considered to be temporal (Turkic) in spite of having a set of developed plans of expressions and plans of contents and some languages are considered to be aspectual (Slavic languages). All of these verb categories function in a single continuum of expression. It becomes the reason of some problems connected with aspect theory in general and the aspect category in particular. It is difficult subjectively to identify meaning, function of each discrete category of a verb because of single plane of expression. This reason affected the creation of an aspect category definition. According to traditional aspect theory, the aspect definition states, that aspect expresses ‘action occurs at the time or the distribution of the action in time’. On the basis of definition, the aspect category is created by tense category, when they are two independent categories with their discrete meanings. One concept is explained by another concept. It contradicts to logic laws. The main conclusion of the article is that the aspect category and tense category have two different discrete meanings and the role of tense category is not correctly defined in the aspect category, which leads to such division of languages as aspectual and temporal. All languages have the aspect category initially, because it is included in the matrix of a verb.

Aspect Aware Learning for Aspect Category Sentiment Analysis

Aspect category sentiment analysis (ACSA) is an underexploited subtask in aspect level sentiment analysis. It aims to identify the sentiment of predefined aspect categories. The main challenge in ACSA comes from the fact that the aspect category may not occur in the sentence in most of the cases. For example, the review “ they have delicious sandwiches ” positively talks about the aspect category “ food ” in an implicit manner. In this article, we propose a novel aspect aware learning (AAL) framework for ACSA tasks. Our key idea is to exploit the interaction between the aspect category and the contents under the guidance of both sentiment polarity and predefined categories. To this end, we design a two-way memory network for integrating AAL into the framework of sentiment classification. We further present two algorithms to incorporate the potential impacts of aspect categories. One is to capture the correlations between aspect terms and the aspect category like “sandwiches” and “food.” The other is to recognize the aspect category for sentiment representations like “food” for “delicious.” We conduct extensive experiments on four SemEval datasets. The results reveal the essential role of AAL in ACSA by achieving the state-of-the-art performance.

Aspect Category Extraction for Sentiment Analysis using Multivariate Filter Method of Feature Selection

Aspect-oriented sentiment analysis is done in two phases like aspect term identification from review and determining related opinion. To carry out this analysis, features play an important role to determine the accuracy of the model. Feature extraction and feature selection techniques contribute to increase the classification accuracy. Feature selection strategies reduce computation time, improve prediction performance, and provides a higher understanding of the information in machine learning and pattern recognition applications etc. This work specifically focuses on aspect extraction from restaurant review dataset but can also be used for other datasets. In this system, we proposed a multivariate filter strategy of feature selection which works on lemma features. This method helps to select relevant features and avoid redundant ones. Initially, the extracted features undergo preprocessing and then the “term-frequency matrix” is generated which contains the occurrence count of features with respect to aspect category. In the next phase, different feature selection strategies are applied which includes selecting features based on correlation, weighted term frequency and weighted term frequency with the correlation coefficient. The performance of weighted term frequency with correlation coefficient approach is compared with the existing system and shows significant improvement in F1 score.

Upaya Meningkatkan Hasil Belajar Kimia dengan Metode Discovery Melalui Kegiatan Laboratorium Siswa Kelas XI SMA Negeri 2 Baubau Tahun Pelajaran 2018/2019

This research aims to improve student learning outcomes in chemistry with discovery methods through laboratory activities on the concept of colloidal systems. This research was carried out at SMA Negeri 2 Baubau from September to October 2018. The method used in this study was classroom action research with a sample of 33 students who were taught with the discovery method through laboratory activities. Classroom Action Research conducted consists of two research cycles with stages in each cycle including planning, implementing, observing, and reflecting. The research instruments used were observation sheets, questionnaires, learning achievement tests, and the results of teacher and student interviews.
 The results of this study indicate that it has reached the criteria that have become the indicator of success as indicated by increasing the category of aspects of active student participation in learning in each cycle. Likewise with the test of learning outcomes an increase in the average value in the first cycle of 68.09 increased to 74.81 and there are no more students who scored less than 60.00. Similarly, the results of student interviews that respond positively to the learning process using discovery methods through laboratory activities. From these results, it can be concluded that learning by using discovery method learning through laboratory activities can improve student chemistry learning outcomes.

M58 LINKING VARIABILITY IN GUT MICROBIOME AND LARGE-SCALE BRAIN NETWORK CONNECTIVITY

Exclosures are established with the objective of rehabilitating degraded lands and restoring of woody vegetation. Various studies have been conducted to evaluate the success of exclosure on restoring woody species diversity. However, works focusing on the effect of topographic factors on woody species diversity are scarce. Understanding the factors that determine woody species diversity is important for management purposes. Therefore, this paper analyzes the effect of altitude, slope, and aspect as topographic variables on woody species diversity in Dawsura exclosure in northern Ethiopia. Data on species identity, abundance, slope, elevation and aspect were recorded from 58 sampling plots. Different diversity indices were used to analyze the data and one-way ANOVA and linear regression was conducted. There were a total of 34 woody species represented 15 families, of which 62% and 38% were trees and shrubs respectively. Altitude (r2 = 0.63, p = 0.000 and r = 0.794, p < 0.01) and slope (r2 = 0.57, p = 0.002 and r = 0.68, p < 0.01) correlated significantly and positively with Shannon diversity, whereas aspect (r2 = 0.12, p = 0.378 and r = 0.27, p > 0.05) did not correlate significantly with Shannon diversity. Woody species diversity at moderate (1.44) and high (1.85) altitudes was significantly different from that of low (0.86) altitude areas (p = 0.0013). Furthermore, significantly higher woody species diversity was recorded at steep slope (1.88) and moderately steep slope (1.62) areas as compared to the gentle slope (0.95) areas. No significant variation was observed in woody species diversity among the aspect categories (p > 0.05). The study concludes that woody species diversity is largely regulated by slope and altitude than aspect in the exclosure. We suggest other environmental and anthropogenic variables should be taken into consideration in future studies on woody species diversity.

Attention-based Sentiment Reasoner for aspect-based sentiment analysis

Aspect-based sentiment analysis (ABSA) is a powerful way of predicting the sentiment polarity of text in natural language processing. However, understanding human emotions and reasoning from text like a human continues to be a challenge. In this paper, we propose a model, named Attention-based Sentiment Reasoner (AS-Reasoner), to alleviate the problem of how to capture precise sentiment expressions in ABSA for reasoning. AS-Reasoner assigns importance degrees to different words in a sentence to capture key sentiment expressions towards a specific aspect, and transfers them into a sentiment sentence representation for reasoning in the next layer. To obtain appropriate importance degree values for different words in a sentence, two attention mechanisms we designed: intra attention and global attention. Specifically, intra attention captures the sentiment similarity between any two words in a sentence to compute weights and global attention computes weights by a global perspective. Experiments on all four English and four Chinese datasets show that the proposed model achieves state-of-the-art accuracy and macro-F1 results for aspect term level sentiment analysis and obtains the best accuracy for aspect category level sentiment analysis. The experimental results also indicate that AS-Reasoner is language-independent.

Hvordan valideres tværsproglige kategorier? Aspekt som eksempel

The precise status of cross-linguistic categories remains a contested issue in contemporary linguistics. This paper proposes a solution based on a functional rethinking of classic concepts in Danish structural linguistics. It is argued that the level of content substance constitutes the necessary starting point for typological categories (such as tense and aspect), distinct from but presupposed by structural categories. This theory is compared to Haspelmath’s influential notion of “comparative concepts”. Comparative concepts arise as subjective stipulations (as claimed by Haspelmath), but we argue that they can be evaluated and potentially validated as cross-linguistically real substance descriptions. A validation procedure based on semantic mapping is proposed and illustrated in an analysis of Comrie’s (1976) category of aspect, which is argued – in contrast to what is generally assumed – not to constitute a cross-linguistically valid semantic domain. We conclude, however, that the category of aspect does constitute a different type of generalization: a generalization over cross-linguistically frequent distinctions.

Proposing a Multilevel Approach for the Management of Uncertainties in Exploratory Projects

Managers of exploratory projects might face uncertainties over long timeframes at different levels (e.g., project, portfolio, organization, and network). Although literature offers some guidance on how to deal with uncertainties (mainly at the project level), there is a need for more empirical ground and theoretical development of a systemic approach to the management of uncertainties. To fill this gap, this article employs a multiple case approach in two established firms, investigating six exploratory projects. As main contributions, we identified new categories of uncertainties (primitive, structural, and elementary) and aspects related to managing these uncertainties.

Emotions Identification by Using Unsupervised Aspect Category Based Sentiment Classification

The social media is growing at an astonishing rate; this has resulted in increased online communications. The online communication contains feedbacks, comments, and reviews that are posted on the internet by users. To analyze such data, the paper represents the Aspect-based unsupervised method that applies association rule mining on customer reviews aims to algorithmically identify product aspects, their corresponding opinions from a collection of opinionated reviews. This framework involves four main subtasks: Product aspect identification, Sentiment expression identification, Emotion Detection, Comparison of Products. This paper also represents a Comparative study of sentiment analysis techniques including machine learning technique and lexicon based technique. The comparisons are majorly drawn based on features such as techniques, data source, data scope, and limitations. The proposed framework performs well with F1-Score 76.426%.

Os problemas no ensino e na aprendizagem dos tempos passados do modo indicativo da língua portuguesa: o caso dos alunos polacos

The aim of this paper is to demonstrate certain problems occurring in teaching and learning past tenses in Portuguese by Polish students. The study will present primarily some questions related to the category of tense and category of aspect in the Polish and Portuguese languages. The basic differences between both systems, which make the acquisition of the Portuguese past tenses by Polish students difficult, will also be underlined. At the same time, the analysis of some of the grammars and books for learning Portuguese as Foreign Language will be conducted. The aim of the analysis will be to verify if the material regarding the past tenses allows the students to systematize their knowledge on the topic.

Aspects of Aspect: Phasic and Episodic Dimensions of Verbs

The present study sets out to analyze aspectuality and coercion in Persian from a new perspective. With regard to the transcendental aspectual distinction between perfectivity, characterized by boundedness and heterogeneity, and imperfectivity, specified by uniformity and homogeneity (Langacker, 2008), it is argued that the heterogeneity of verbs may be assessed according to their phasic and episodic variables. In other words, in contrast to homogeneous verbs, which lack any kind of boundedness, heterogeneous verbs may occur either in a bounded phasic domain or in a bounded episodic domain. Concerning phasic-episodic features, this study presents a new model of lexical aspect that can differentiate five aspectual categories. The paper also scrutinizes the combinations of different verbs with different sentential operators in order to explain various kinds of type-shifting triggered by different operators. Thereby, two procedures of phasic coercion and episodic coercion are introduced which are responsible for modifying the phasic and episodic features of verbs in order to resolve the semantic conflicts between verbs and sentential operators. These procedures modify the phasic/episodic attributes of verbs according to the viewing frames evoked by interpretative operators.

FEATURES OF RESEARCHING THE CONCEPT OF CONTROLLING OF AN ENTERPRISE’S SUSTAINABLE DEVELOPMENT

The relevance of the research topic is explained by the fact that the existence of various approaches to the assessment of the level of sustainability of an enterprise’s development predetermines possible difficulties when enterprises choose a methodology and criteria for estimation of this category. Thus, the purpose of this article is the research of categorical aspects of the sustainability of an enterprise’s development and a characteristic of the principles and approaches adopted in relation to the term “controlling of the sustainable development of an enterprise”. In this article, the sustainable development of an enterprise is considered in terms of several aspects. A contentious issue is the following: what kind of sustainability does the controlling system of an enterprise provide? In this regard, analysis of the content of the aspects of each type of sustainability is performed. In addition, an assumption is made that controlling ensures the sustainable development of an enterprise by performing the following functions: preventive, adaptive, coordinating, analytical, organizational, instrumental, informational, and prognostic; the content and economic directionality of each function are disclosed. In the course of characterization of the effective controlling indicator of the sustainability of an enterprise’s development, the main principles that should guide the development of this indicator are identified; its main criteria are highlighted. The research notes that a complex assessment of the sustainability of the development of enterprises in modern conditions requires the necessity of considering the sustainability of the economic system from financial and economic aspects. Based on the results of the research, a conclusion is made that the creation of criteria for assessing the indicator of controlling of sustainable development of an enterprise will significantly increase the efficiency of the controlling system at industrial enterprises and therefore is more than necessary.

Exploiting Coarse-to-Fine Task Transfer for Aspect-Level Sentiment Classification

Aspect-level sentiment classification (ASC) aims at identifying sentiment polarities towards aspects in a sentence, where the aspect can behave as a general Aspect Category (AC) or a specific Aspect Term (AT). However, due to the especially expensive and labor-intensive labeling, existing public corpora in AT-level are all relatively small. Meanwhile, most of the previous methods rely on complicated structures with given scarce data, which largely limits the efficacy of the neural models. In this paper, we exploit a new direction named coarse-to-fine task transfer, which aims to leverage knowledge learned from a rich-resource source domain of the coarse-grained AC task, which is more easily accessible, to improve the learning in a low-resource target domain of the fine-grained AT task. To resolve both the aspect granularity inconsistency and feature mismatch between domains, we propose a Multi-Granularity Alignment Network (MGAN). In MGAN, a novel Coarse2Fine attention guided by an auxiliary task can help the AC task modeling at the same finegrained level with the AT task. To alleviate the feature false alignment, a contrastive feature alignment method is adopted to align aspect-specific feature representations semantically. In addition, a large-scale multi-domain dataset for the AC task is provided. Empirically, extensive experiments demonstrate the effectiveness of the MGAN.

MNCN: A Multilingual Ngram-Based Convolutional Network for Aspect Category Detection in Online Reviews

The advent of the Internet has caused a significant growth in the number of opinions expressed about products or services on e-commerce websites. Aspect category detection, which is one of the challenging subtasks of aspect-based sentiment analysis, deals with categorizing a given review sentence into a set of predefined categories. Most of the research efforts in this field are devoted to English language reviews, while there are a large number of reviews in other languages that are left unexplored. In this paper, we propose a multilingual method to perform aspect category detection on reviews in different languages, which makes use of a deep convolutional neural network with multilingual word embeddings. To the best of our knowledge, our method is the first attempt at performing aspect category detection on multiple languages simultaneously. Empirical results on the multilingual dataset provided by SemEval workshop demonstrate the effectiveness of the proposed method1.

KEMAMPUAN MENULIS TEKS DRAMA PADA SISWA KELAS VIII SMP NEGERI 25 BANDAR LAMPUNG

This study contains the ability to write drama text in grade VIII students in the odd semester of SMP Negeri 25 Bandar Lampung. What is the level of ability of VIII grade students of SMP Negeri 25 Bandar Lampung in writing drama texts. This study aims to find out and describe the level of students' ability to write drama texts that are viewed in terms of several aspects, namely aspects of the plot, character and characterization, style of language, background, point of view and theme.The method used is descriptive qualitative by using percentages, namely by analyzing aspects, namely aspects of flow, character and characterization, style of language, background, perspective and theme. The ability of the drama text of SMP Negeri 25 Bandar Lampung in the aspects of the background and angle of understanding in the category is quite seen from the percentage of students who have an average value of 62.8% and the ability to write drama texts for SMP Negeri 25 Bandar Lampung in the theme aspects category is less seen from the percentage of students who have an average value of 60%.

Cabazitaxel in the treatment of castration-resistant cancer: hitting the balance of efficacy and safety

Cabazitaxel, an antineoplastic agent from the third generation taxan group, has demonstrated efficacy in the treatment of metastatic castration-resistant prostate cancer (mCRPC) refractory to docetaxel. This article is devoted to a critical analysis of studies on the use of cabazitaxel in this category of patients and key aspects of management of treatment-related toxicity. The authors also reviewed possible scenarios for the use of cabazitaxel in the sequential therapy of mCRPC, including androgen receptor signalling inhibitors and systemic radiotherapy.

Parallelized root cause analysis using cause-related aspect formulation technique (CRAFT)

Aspect-based opinion mining aims to provide results that aid in effective business decision making. Identifying the aspects, their major and minor causes proves to be the major challenge in this domain. This paper presents a cause-related aspect formulation technique (CRAFT) to perform opinion mining. The CRAFT model incorporates an enhanced aspect extraction module, ontology creation based on aspect and aspect categories, aspect and aspect category metadata repository creation and maintenance and a decision tree-based parallelized boosted ensemble. The proposed CRAFT model is implemented in Spark to incorporate parallelism in the architecture. The process of ontology creation and metadata repository creation aids in effective identification of both implicit and explicit aspects. Experiments were conducted using a customer review benchmark dataset incorporating reviews about five varied products. Comparisons were performed with state-of-the-art models CNN+LP, Popscu and TF-RBM. Comparisons indicate improved performances ranging up to 4% in terms of precision, up to 18% in terms of recall and up to 11% on F1 Scores, indicating the effectiveness of the proposed CRAFT model.

Aspects of categorization of families in Lithuania

Aspects of categorization of families in Lithuania

A Review on Detecting a specific aspect category for sentiment analysis using association rule mining scheme

A Review on Detecting a specific aspect category for sentiment analysis using association rule mining scheme

Aspect Category Detection Based on Attention Mechanism and Bi-Directional LSTM

Online reviews play an increasingly important role in users' purchase decisions. E-commerce websites provide massive user reviews, but it is hard for individuals to make full use of the information. Therefore, it is an urgent task to classify, analyze and summarize the massive comments. In this paper, a model based on attention mechanism and bi-directional long short-term memory (BLSTM) is used to identify the categories of these review objects for the classification of the reviews. The model first uses BLSTM to train the review in the form of word vectors; then according to the part-of-speech, the output vectors of the BLSTM are given corresponding weights. The weights as prior knowledge can guide the learning of attention mechanism to enhance the classification accuracy; finally, the attention mechanism is used to capture category-related important features which are used for category determination. Experiments on the SemEval data set show that our model outperforms the state-of-the-art methods on aspect category detection.