scholarly journals Guided Attention Network for Concept Extraction

2021 ◽  
Author(s):  
Songtao Fang ◽  
Zhenya Huang ◽  
Ming He ◽  
Shiwei Tong ◽  
Xiaoqing Huang ◽  
...  
Keyword(s):  
Conditional Random Field ◽  
The Other ◽  
Context Word ◽  
Attention Networks ◽  
Large Margin ◽  
Concept Extraction ◽  
Aggregate Information ◽  
Textual Data ◽  
Structured Information ◽  
Novel Model

Concept extraction aims to find words or phrases describing a concept from massive texts. Recently, researchers propose many neural network-based methods to automatically extract concepts. Although these methods for this task show promising results, they ignore structured information in the raw textual data (e.g., title, topic, and clue words). In this paper, we propose a novel model, named Guided Attention Concept Extraction Network (GACEN), which uses title, topic, and clue words as additional supervision to provide guidance directly. Specifically, GACEN comprises two attention networks, one of them is to gather the relevant title and topic information for each context word in the document. The other one aims to model the implicit connection between informative words (clue words) and concepts. Finally, we aggregate information from two networks as input to Conditional Random Field (CRF) to model dependencies in the output. We collected clue words for three well-studied datasets. Extensive experiments demonstrate that our model outperforms the baseline models with a large margin, especially when the labeled data is insufficient.

scholarly journals A recommendations model with multiaspect awareness and hierarchical user-product attention mechanisms

2020 ◽  
Vol 17 (3) ◽  
pp. 849-865
Author(s):  
Zhongqin Bi ◽  
Shuming Dou ◽  
Zhe Liu ◽  
Yongbin Li
Keyword(s):  
State Of The Art ◽  
Weight Vector ◽  
User Preferences ◽  
The Other ◽  
Attention Networks ◽  
Proposed Model ◽  
Network Methods ◽  
Public Datasets ◽  
Novel Model ◽  
Attention Weight

Neural network methods have been trained to satisfactorily learn user/product representations from textual reviews. A representation can be considered as a multiaspect attention weight vector. However, in several existing methods, it is assumed that the user representation remains unchanged even when the user interacts with products having diverse characteristics, which leads to inaccurate recommendations. To overcome this limitation, this paper proposes a novel model to capture the varying attention of a user for different products by using a multilayer attention framework. First, two individual hierarchical attention networks are used to encode the users and products to learn the user preferences and product characteristics from review texts. Then, we design an attention network to reflect the adaptive change in the user preferences for each aspect of the targeted product in terms of the rating and review. The results of experiments performed on three public datasets demonstrate that the proposed model notably outperforms the other state-of-the-art baselines, thereby validating the effectiveness of the proposed approach.

Global North-Global South Relations Around a Notional U.S.-Mexico “Border Wall”

2020 ◽  
pp. 296-344
Author(s):  
Shalin Hai-Jew
Keyword(s):  
Image Analysis ◽  
Global South ◽  
The Other ◽  
Data Word ◽  
Textual Data ◽  
Mexico Border ◽  
Global North ◽  
Word Search ◽  
The U.S ◽  
Image Analyses

In the present political moment, “border walls” between the U.S. and Mexico have become a flashpoint, representing binaries like governed / ungoverned spaces, security / insecurity, morality / immorality, respect / disrespect for human rights, human unity / disunity, North / South, haves / have-nots, citizens / non-citizens, Republicans / Democrats, conservatives / liberals, patriots / traitors, nationalists / internationalists (or globalists), and others. This work explores some of the thematic Global North – Global South implications of a notional “border wall” based on social imagery (in a multi-loop image analysis approach). This work questions how the “other” may be viewed through the limiting slats of a fence or windows in a wall. In addition to the image analyses, topic-related textual data will also be studied from various sources: academia, journalism, and social media (including mass search correlations, big data word search, related tags networks, and #hashtag network analysis).

Discriminant Criteria for Pattern Classification

2011 ◽  
pp. 30-57
Author(s):  
David Zhang ◽  
Fengxi Song ◽  
Yong Xu ◽  
Zhizhen Liang
Keyword(s):  
Feature Extraction ◽  
Pattern Classification ◽  
The Other ◽  
Linear Projection ◽  
Linear Discriminant ◽  
Large Margin ◽  
The Third ◽  
Fisher Discriminant ◽  
Squared Error ◽  
Fisher Discriminant Criterion

As mentioned in Chapter II, there are two kinds of LDA approaches: classification- oriented LDA and feature extraction-oriented LDA. In most chapters of this session of the book, we focus our attention on the feature extraction aspect of LDA for SSS problems. On the other hand,, with this chapter we present our studies on the pattern classification aspect of LDA for SSS problems. In this chapter, we present three novel classification-oriented linear discriminant criteria. The first one is large margin linear projection (LMLP) which makes full use of the characteristic of the SSS problems. The second one is the minimum norm minimum squared-error criterion which is a modification of the minimum squared-error discriminant criterion. The third one is the maximum scatter difference which is a modification of the Fisher discriminant criterion.

Opportunities and Opportunity Cost in Preparing Millennium Teachers

2011 ◽  
pp. 1571-1572
Author(s):  
Michael Hartoonian ◽  
Vivian Johnson
Keyword(s):  
Opportunity Cost ◽  
Teacher Educators ◽  
Human Life ◽  
Ways Of Knowing ◽  
The Other ◽  
Social Phenomena ◽  
Content Areas ◽  
Moral Framework ◽  
Structured Information

The millennium teacher is presented with two powerful conceptions. One is expressed in Pogo’s observation that “…we always seem to be confronted with insurmountable opportunity.” The other is presented best in T.S. Eliot’s poem, “Choruses To The Rock.” • Where is the knowledge we have lost in information? • Where is the wisdom we have lost in knowledge? Both of these conceptions have direct implications for teacher educators and their work with the next generation of teachers. “Insurmountable opportunity” is the reality for teachers who have not developed the ability to make “enlightened choices.” Enlightened decision making in a sea of opportunity requires the foundational understanding that information, knowledge, and wisdom represent different ways of knowing. Information is one dimensional. It is linear or horizontal, fragmented, and quite useless in and of itself. Knowledge is structured information; it shows relationships between and among bits of information. Knowledge is best represented by theories about natural and social phenomena; it is created basically within content areas, and it tends to be field-specific. Wisdom is the organic application of information and knowledge to human dilemmas, desires, and dreams. Wisdom is that quality of thought and imagination that ties us to our cultural heritage and gives us the ability to find and build the moral framework upon which human life is defined and within which meaning resides.

scholarly journals Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks

2017 ◽  
Author(s):  
Jun Xiao ◽  
Hao Ye ◽  
Xiangnan He ◽  
Hanwang Zhang ◽  
Fei Wu ◽  
...  
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Feature Interaction ◽  
Model Parameters ◽  
Learning Approach ◽  
Attention Networks ◽  
Feature Interactions ◽  
Factorization Machine ◽  
Real World Datasets ◽  
Novel Model

Factorization Machines (FMs) are a supervised learning approach that enhances the linear regression model by incorporating the second-order feature interactions. Despite effectiveness, FM can be hindered by its modelling of all feature interactions with the same weight, as not all feature interactions are equally useful and predictive. For example, the interactions with useless features may even introduce noises and adversely degrade the performance. In this work, we improve FM by discriminating the importance of different feature interactions. We propose a novel model named Attentional Factorization Machine (AFM), which learns the importance of each feature interaction from data via a neural attention network. Extensive experiments on two real-world datasets demonstrate the effectiveness of AFM. Empirically, it is shown on regression task AFM betters FM with a 8.6% relative improvement, and consistently outperforms the state-of-the-art deep learning methods Wide&Deep [Cheng et al., 2016] and DeepCross [Shan et al., 2016] with a much simpler structure and fewer model parameters. Our implementation of AFM is publicly available at: https://github.com/hexiangnan/attentional_factorization_machine

scholarly journals Optimization Model Based on Multivariable Grey Model and Its Application to Energy Consumption in China

2021 ◽  
Author(s):  
Kai Xu ◽  
Xilin Luo ◽  
Xinyu Pang
Keyword(s):  
Energy Consumption ◽  
Natural Gas ◽  
Prediction Model ◽  
Prediction Models ◽  
Clean Energy ◽  
Energy Development ◽  
The Other ◽  
The Novel ◽  
Grey Prediction ◽  
Novel Model

Abstract Currently, the energy development in China is in a critical period of transformation and reform, facing unprecedented opportunities and challenges. Accurate energy consumption forecast is conducive to promoting the diversification of energy development and utilization, and ensuring the healthy and rapid development of China's economy. Based on the existing multivariable grey prediction model, a nonlinear multivariable grey prediction model with parameter optimization is established in this paper, which used the genetic algorithms to find the optimal parameters, and the modelling steps are obtained. Then, the novel model takes the oil natural gas, coal and clean energy in China as the research objects, and the results are compared with the other four grey prediction models. The novel model has higher simulation and prediction accuracy, which is better than the other four grey prediction models. Finally, the novel model is used to predict those four energy consumption forecasts in China from 2020 to 2024. The results show that various energy consumption will further increase, while the fastest growing is clean energy and natural gas, which provides effective information for the Chinese government to formulate energy economic policies.

Rhythmic Prose in Plutarch’s Lives

2018 ◽  
Author(s):  
G. O. Hutchinson
Keyword(s):  
Word Order ◽  
Large Scale ◽  
The Other ◽  
Context Word

From this chapter the book goes more deeply into one productive and central author: Plutarch. The basis for the book is a scansion of all of Plutarch’s Lives, not just the end of sentences but every small phrase: 2,297 pages, almost 100,000 phrases. For the other half of Plutarch’s work, the philosophical and related writings, the end of every sentence has been scanned. There thus exists a much more abundant body of material for the consideration of Plutarch than for any other Greek author. It is possible to advance from scansion into interpretation. With many detailed examples, and with the help of ancient comments, a connection is indicated between rhythm and emphasis, or attention; thus a rhythmic close draws attention to the words that create it. This can be independently confirmed from context, word-order, etc., and is demonstrated on a large scale in the book as a whole.

scholarly journals Christians as Levites: Rethinking Early Christian Attitudes toward War and Bloodshed via Origen, Tertullian, and Augustine

2019 ◽  
Vol 112 (04) ◽  
pp. 491-516
Author(s):  
Daniel H. Weiss
Keyword(s):  
Roman Empire ◽  
Hebrew Bible ◽  
The Other ◽  
Ethical Framework ◽  
Strong Assumption ◽  
Early Christian ◽  
Textual Data ◽  
Attitudes Toward War

AbstractThis article seeks to break the scholarly deadlock regarding attitudes toward war and bloodshed held by early Christian thinkers. I argue that, whereas previous studies have attempted to fit early Christian stances into one or another “unitary-ethic” framework, the historical-textual data can be best accounted for by positing that many early Christian writers held to a “dual-ethic” orientation. In the latter, certain actions would be viewed as forbidden for Christians but as legitimate for non-Christians in the Roman Empire. Moreover, this dual-ethic stance can be further illuminated by viewing it in connection with the portrayal in the Hebrew Bible of the relation between Levites and the other Israelite tribes. This framing enables us to gain a clearer understanding not only of writers like Origen and Tertullian, who upheld Christian nonviolence while simultaneously praising Roman imperial military activities, but also of writers such as Augustine, whose theological-ethical framework indicates a strong assumption of a dual-ethic stance in his patristic predecessors.

scholarly journals Incorporating Word Significance into Aspect-Level Sentiment Analysis

2019 ◽  
Vol 9 (17) ◽  
pp. 3522
Author(s):  
Refuoe Mokhosi ◽  
ZhiGuang Qin ◽  
Qiao Liu ◽  
Casper Shikali
Keyword(s):  
Sentiment Analysis ◽  
Attention Mechanism ◽  
Context Word ◽  
Decay Factor ◽  
Language Interpretation ◽  
Psychological Evidence ◽  
Working Together ◽  
Relevant Context ◽  
Incremental Interpretation ◽  
Novel Model

Aspect-level sentiment analysis has drawn growing attention in recent years, with higher performance achieved through the attention mechanism. Despite this, previous research does not consider some human psychological evidence relating to language interpretation. This results in attention being paid to less significant words especially when the aspect word is far from the relevant context word or when an important context word is found at the end of a long sentence. We design a novel model using word significance to direct attention towards the most significant words, with novelty decay and incremental interpretation factors working together as an alternative for position based models. The interpretation factor represents the maximization of the degree each new encountered word contributes to the sentiment polarity and a counter balancing stretched exponential novelty decay factor represents decaying human reaction as a sentence gets longer. Our findings support the hypothesis that the attention mechanism needs to be applied to the most significant words for sentiment interpretation and that novelty decay is applicable in aspect-level sentiment analysis with a decay factor β = 0.7 .

Complex Terminology Extraction Model from Unstructured Web Text Based Linguistic and Statistical Knowledge

2012 ◽  
Vol 2 (3) ◽  
pp. 1-18 ◽  
Author(s):  
Fethi Fkih ◽  
Mohamed Nazih Omri
Keyword(s):  
Conditional Random Fields ◽  
Conditional Random Field ◽  
Linguistic Knowledge ◽  
Terminology Extraction ◽  
Statistical Knowledge ◽  
Textual Data ◽  
Text Content ◽  
Extraction Model ◽  
Source Of Information

Textual data remain the most interesting source of information in the web. In the authors’ research, they focus on a very specific kind of information namely “complex terms”. Indeed, complex terms are defined as semantic units composed of several lexical units that can describe in a relevant and exhaustive way the text content. In this paper, they present a new model for complex terminology extraction (COTEM), which integrates linguistic and statistical knowledge. Thus, the authors try to focus on three main contributions: firstly, they show the possibility of using a linear Conditional Random Fields (CRF) for complex terminology extraction from a specialized text corpus. Secondly, prove the ability of a Conditional Random Field to model linguistic knowledge by incorporating grammatical observations in the CRF’s features. Finally, the authors present the benefits gained by the integration of statistical knowledge on the quality of the terminology extraction.

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