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About 10,000 papers for “contextual features”

CONTEXTUAL FEATURES FOR IMPLEMENTING EPISTEMIC MODALITY

Н. Дьячок · Journal “Ukrainian sense” · 2021

Background. The article deals with the consideration that a speaker is the central figure for determination of modality as it is the speech subject that establishes the relations between an object and its predicated sign in an utterance. The subject of our research is epistemic modality, which is referred to a subjective type of modality; it is considered as the connection between a subject and an attribute. This connection is evaluated rationally by a speaker as his/her subjective mental construction rather than objective characteristic of the reality.
 The research objective is to demonstrate the alternative ways for expressing epistemic modality. The objective can be achieved by completing the following tasks: 1) analysis of theoretical material according to the research topic; 2) differentiation of the modality types in terms of certain features; 3) identification of traditional means to express epistemic modality; 4) determination of a contextual way of its implementation.
 The article objective requires application of several methods such as comparative contextual analysis and general text analysis. These techniques allow us to draw conclusions regarding the means of expressing epistemic modality as reliable as possible. A method of continuous sampling was used to accumulate actual material.
 Results. Attention should be also paid to the expression of epistemic modality by means of the context. It is no doubt that the abovementioned traditional and non-traditional modal markers can function within it; however, there are certain contexts where there are no markers of that kind at all. It means that, on the one hand, their presence is not obligatory for the modality implementation; on the other hand, the context becomes a field to cultivate some new – potential – means for expressing one or another attitude of a speaker / writer to the utterance and the surrounding reality.
 Conclusions. Epistemic modality of certainty / uncertainty in the English language is expressed with the help of thematic series of verbs united by the semantic invariant-meaning of the verb to know, modal words and short adjectives. Epistemic modality expressing a meaning of opinion – assumption in the English language is represented by the semantic invariant-meaning of the verb to think. Doubtful evaluation is explicated with the help of verbs and modal words united by the seme of assumption. Formal implementation of epistemic modality goes beyond the methods called the traditional ones. Attitude of a speaker to the reality, apart from the representatives proved by the linguistic science, can be explicated contextually, i.e. it is the context without use of certain verbs, adverbs etc. that is capable of indirect representation of a certain-type epistemics. Epistemic modality can be conveyed contextually as well. This type of its expression has blurred boundaries as opposed to the strictly identified markers.

Cited by 0Open access

Relevant contextual features in XML retrieval

Georgina Ramírez, Arjen P. de Vries · 2006

We present and analyze results of a collaborative user study carried out by the Interactive Track at INEX 2005 [4]. The overall goal of the Interactive Track is to investigate the behavior of users when interacting with components of XML documents. In this paper we use the data collected from this user study to investigate dependencies between several contextual features and the structural characteristics of the relevant components (e.g. type and number of relevant elements). The three contextual features analyzed are the user's familiarity with the topic, the request's type, and the user's motivation to perform the search. The dependencies found indicate that XML retrieval systems should also benefit from the use of contextual information.

Cited by 14

C2 - Pedestrian - Track - Contextual Risk Features

Thiago R. P. M. Rúbio · Zenodo (CERN European Organization for Nuclear Research) · 2021

Contextual Risk features for Pedestrian in Track (controlled environment) Episodes collected during the Cycle 2 of the SIMUSAFE project.

Cited by 0Open access

Enriched team forming with contextual features

María Paula Arias-Báez, Ángela Carrillo-Ramos · 2012

A team is defined as a set of people with complementary skills, shared responsibilities and common goals that achieve them together through information sharing and collaboration. However, personal characteristics can be very difficult to manage and it damages the collaborative work, there are many problems during its execution that can negatively compromise common goals. For this reason, it is essential to analyze how the formation of teams in a collaborative environment can be more effective: choose the right people with the skills, knowledge and behaviors needed to collaborate and to jointly achieve the goal in a given context. This article presents MATEO (an acronym for Making Adapted TEams Oriented to collaboration), a generic system that adapts the process of forming teams according to team needs, the context and the characteristics of the participants.

Cited by 2

Contextual Graph Matching with Correlated Gaussian Features

Mohammad Hassan Ahmad Yarandi, Luca Ganassali · arXiv (Cornell University) · 2026

We investigate contextual graph matching in the Gaussian setting, where both edge weights and node features are correlated across two networks. We derive precise information-theoretic thresholds for exact recovery, and identify conditions under which almost exact recovery is possible or impossible, in terms of graph and feature correlation strengths, the number of nodes, and feature dimension. Interestingly, whereas an all-or-nothing phase transition is observed in the standard graph-matching scenario, the additional contextual information introduces a richer structure: thresholds for exact and almost exact recovery no longer coincide. Our results provide the first rigorous characterization of how structural and contextual information interact in graph matching, and establish a benchmark for designing efficient algorithms.

Cited by 0Open access

Contextual Activation of Features of Combined Concepts

Zachary Estes, Sam Glucksberg · 2022

We examine how context affects the accessibility of features of combined concepts. A ‘contrast hypothesis’ suggests that contrasting a to-be-verified feature in the context hinders its later verification. Results of Experiment 1 instead support a priming hypothesis whereby features are differentially activated by contexts. Experiment 2 demonstrates that this priming effect is positive rather than negative, even when feature verification follows a contextual combined concept that is inconsistent with the to-be-verified feature. We conclude that context can differentially activate features of combined concepts, and that it may do so by way of semantic priming.

Cited by 2

Contextual Graph Matching with Correlated Gaussian Features

Mohammad Hassan Ahmad Yarandi, Luca Ganassali · arXiv (Cornell University) · 2026

We investigate contextual graph matching in the Gaussian setting, where both edge weights and node features are correlated across two networks. We derive precise information-theoretic thresholds for exact recovery, and identify conditions under which almost exact recovery is possible or impossible, in terms of graph and feature correlation strengths, the number of nodes, and feature dimension. Interestingly, whereas an all-or-nothing phase transition is observed in the standard graph-matching scenario, the additional contextual information introduces a richer structure: thresholds for exact and almost exact recovery no longer coincide. Our results provide the first rigorous characterization of how structural and contextual information interact in graph matching, and establish a benchmark for designing efficient algorithms.

Cited by 0Open access

CONTEXTUAL FEATURES OF MODALITY IN WRITTEN DISCOURSE

Ahadova Hulkaroy · Zenodo (CERN European Organization for Nuclear Research) · 2024

In our developed world, it is becoming significant to investigate different features of language aspects and analyzing its representation in written discourse. The current thesis statement delves into contextual features of modality in written discourse. The general notion about modality and its usage is being overviewed through textual contexts.

Cited by 0Open access

The Contextual Features in Schema-Agnostic Environment

Muhammad Ali Norozi · Duo Research Archive (University of Oslo) · 2014

Relevance scoring and estimation deals with both finding the relevant set of answers and ordering them according to the degree of their relevance to the user-intent. The traditional information retrieval (IR) systems successfully find and order the relevant documents and leave them to the users, who then have to locate the relevant information embedded somewhere within the document. In contrast, estimating relevance in semi-structured retrieval means not only retrieving and ordering the relevant documents but also locating the relevant information within the document as well. When it comes to semi-structured retrieval, the traditional IR style retrieval is simply insufficient. The main focus of this thesis is estimating relevance in a schema-agnostic environment. Here, “schema-agnostic” means that the schema or the structure exists explicitly within the documents but the user does not or need not know that schema. In such an environment, the structure is generally defined loosely, which means: (a) it can evolve over time, (b) it can constitute a large part of the data, and (c) it might exist seamlessly within the document. The natural question that comes into mind is, why is such a structure there at all? The structure in a schemaagnostic environment is there to be used by retrieval systems for several useful tasks. This thesis is about unveiling the capabilities of the structural constructs within semi-structured documents in schema-agnostic settings. Structural constructs can form what we call the structural context of the relevant item. A structural context builds up the internal and external contextual features of a semi-structured document. These contextual features help with a series of tasks. The work presented in this thesis contributes towards understanding and utilizing the contextual features in the retrieval of focused information in schema-agnostic settings. During the course of this study we have identified, implemented and experimented with several intuitive types of contextual features in semi-structured retrieval settings. Contextualization is the generic process of utilizing features in the structural context of the retrievable units in relevance scoring. The proposed retrieval approaches, based mainly on contextual features, exhibited notable improvements in retrieval effectiveness, during empirical analyses. The evaluations and empirical analyses are performed in several tasks, spread across different phases of this study. The tasks are performed by looking at different aspects and challenges of the semi-structured retrieval domain. The following tasks are performed at different phases of this study: ad-hoc tasks, granulation tasks, and standard tasks offered by INitiative for the Evaluation of Xml retrieval (INEX). The contributions of this thesis are also grouped by these tasks.

Cited by 0Open access

Classification of News Category Using Contextual Features

M Jahnavi, K Chandana, Priyanka C. Nair et al. · 2024

This study focuses on the crucial task of news category classification using a dataset consisting of 175,995 instances spanning across 30 distinct categories. The system utilizes a preprocessing pipeline to collect intricate linguistic features. Pre-trained word embeddings such as GloVe, FastText and Word2Vec are utilized to gather semantic information. The dataset is balanced using SMOTE analysis, and semantic information is gathered using pre-trained word embeddings. The study investigates various machine learning algorithms such as K-Nearest Neighbors, Random Forest, XGBoost, Decision Tree, Logistic Regression, Gaussian Naive Bayes, and AdaBoost. The primary emphasis lies in attaining exceptional precision, F1 score, and comprehending the interpretability of the models’ predictions. The study also highlights the need of model interpretability, utilizing LIME. The framework for real-world news classification employs a variety of embeddings, with KNN standing out as particularly effective. It improves precision and efficiency, especially when combined with Word2Vec embeddings, demonstrating machine learning’s potential in a wide range of news categories.

Cited by 3

Combining contextual features for word sense disambiguation

Hoa Trang Dang, Martha Palmer · 2002

In this paper we present a maximum entropy Word Sense Disambiguation system we developed which performs competitively on SENSEVAL-2 test data for English verbs. We demonstrate that using richer linguistic contextual features significantly improves tagging accuracy, and compare the system's performance with human annotator performance in light of both fine-grained and coarse-grained sense distinctions made by the sense inventory.

Cited by 29Open access

Intrinsic and contextual features in object recognition

David Schlangen, Elan Barenholtz · Journal of Vision · 2015

The context in which an object is found can facilitate its recognition. Yet, it is not known how effective this contextual information is relative to the object's intrinsic visual features, such as color and shape. To address this, we performed four experiments using rendered scenes with novel objects. In each experiment, participants first performed a visual search task, searching for a uniquely shaped target object whose color and location within the scene was experimentally manipulated. We then tested participants' tendency to use their knowledge of the location and color information in an identification task when the objects' images were degraded due to blurring, thus eliminating the shape information. In Experiment 1, we found that, in the absence of any diagnostic intrinsic features, participants identified objects based purely on their locations within the scene. In Experiment 2, we found that participants combined an intrinsic feature, color, with contextual location in order to uniquely specify an object. In Experiment 3, we found that when an object's color and location information were in conflict, participants identified the object using both sources of information equally. Finally, in Experiment 4, we found that participants used whichever source of information-either color or location-was more statistically reliable in order to identify the target object. Overall, these experiments show that the context in which objects are found can play as important a role as intrinsic features in identifying the objects.

Cited by 7Open access

LEARNING DENSE CONTEXTUAL FEATURES FOR SEMANTIC SEGMENTATION

Hacer Yalım Keleş, Long Ang Lim · Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering · 2020

Semantic segmentation, which is one of the key problems in computer vision, has been applied in various application domains such as autonomous driving, robot navigation, or medical imagery, to name a few. Recently, deep learning, especially deep neural networks, have shown significant performance improvement over conventional semantic segmentation methods. In this paper, we present a novel encoder-decoder type deep neural network-based method, namely XSeNet, that can be trained end-to-end in a supervised manner. We adapt ResNet-50 layers as the encoder and design a cascaded decoder that composes of the stack of the X-Modules, which enables the network to learning dense contextual information and having wider field-of-view. We evaluate our method using CamVid dataset, and experimental results reveal that our method can segment most part of the scene accurately and even outperforms previous state-of-the art methods.

Cited by 0Open access

Contextual and Non-Contextual Features Extraction and a Selection Method for Microcalcifications Detection

A. Vega-Corona, Moises Sanchez-Garcia, Mario Gonzalez-Romo et al. · 2006

In this paper a method to extract and build patterns to model microcalcifications from digitized mammography is presented. The proposed method consist in a combination of two steps, in the first one, a feature extraction method is applied using multiscale wavelet image processing, and is combined with a Self Organizing Neural Network to solve the segmentation image problem. In the second one, a feature selection method is considered applying a Generalized Regression Neural Networks (GRNN) in order to obtain an optimal and a short input vector for classifier design proposes. Optimal results were obtained and compared with others.

Cited by 8

Fake Review Detection Using Behavioral and Contextual Features

Jay Kumar · arXiv (Cornell University) · 2020

User reviews reflect significant value of product in the world of e-market. Many firms or product providers hire spammers for misleading new customers by posting spam reviews. There are three types of fake reviews, untruthful reviews, brand reviews and non-reviews. All three types mislead the new customers. A multinomial organization "Yelp" is separating fake reviews from non-fake reviews since last decade. However, there are many e-commerce sites which do not filter fake and non-fake reviews separately. Automatic fake review detection is focused by researcher for last ten years. Many approaches and feature set are proposed for improving classification model of fake review detection. There are two types of dataset commonly used in this research area: psuedo fake and real life reviews. Literature reports low performance of classification model real life dataset if compared with pseudo fake reviews. After investigation behavioral and contextual features are proved important for fake review detection Our research has exploited important behavioral feature of reviewer named as "reviewer deviation". Our study comprises of investigating reviewer deviation with other contextual and behavioral features. We empirically proved importance of selected feature set for classification model to identify fake reviews. We ranked features in selected feature set where reviewer deviation achieved ninth rank. To assess the viability of selected feature set we scaled dataset and concluded that scaling dataset can improve recall as well as accuracy. Our selected feature set contains a contextual feature which capture text similarity between reviews of a reviewer. We experimented on NNC, LTC and BM25 term weighting schemes for calculating text similarity of reviews. We report that BM25 outperformed other term weighting scheme.

Cited by 6Open access