scholarly journals Comparative Study on Feature-Based Scoring Using Vector Space Modelling System

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Tarandeep Singh Walia ◽  
Tarek Frikha ◽  
Omar Cheikhrouhou ◽  
Habib Hamam
Keyword(s):  
Scoring System ◽  
State Of The Art ◽  
Feature Analysis ◽  
Similarity Function ◽  
Semantic Features ◽  
Automated Scoring ◽  
Rule Based ◽  
Feature Based ◽  
Semantic Feature Analysis ◽  
Better Than

This paper shows the importance of automated scoring (AS) and that it is better than human graders in terms of degree of reproducibility. Considering the potential of the automated scoring system, there is further a need to refine and develop the existing system. The paper goes through the state of the art. It presents the results concerning the problems of existing systems. The paper also presents the semantic features that are indispensable in the scoring system as they have complete content. Moreover, in the present research, a huge deviation has been exhibited by the system which has been shown later in performance analysis of the study, and this clearly indicates the novelty and improved results of the system. It explains the algorithms included in the methodology of this proposed system. The novelty of our work consists in the use of its own similarity function and its notation mechanism. It does not use the cosine similarity function between two vectors. This paper describes and develops a more accurate system which employs a statistical method for scoring. This system adopts and integrates rule-based semantic feature analysis.

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

Quantum-based automated short answer scoring system

2018 ◽  
Vol 32 (33) ◽  
pp. 1850403
Author(s):  
Tarandeep Singh Walia ◽  
Gurpreet Singh Josan ◽  
Amarpal Singh
Keyword(s):  
Statistical Method ◽  
Hybrid System ◽  
Scoring System ◽  
Scoring Systems ◽  
Automated Scoring ◽  
Rule Based ◽  
Short Answer ◽  
Human Assessment ◽  
Examination Process

Answer Scoring is defined as an act of assigning a score to an answer by a human grader. This scoring technique is costly and requires deep logical efforts and it depends on less-than-perfect human assessment. However, the Automated Scoring (AS) System has its importance in providing the student with a score as well as feedback within seconds. This paper describes an AS system in which scores are assigned to essays automatically based upon predefined algorithms. Most of the educational sectors carry out an important examination process, i.e. to examine and assess the capabilities of the student based on his/her given answers. To accomplish this process, the human graders can apply this Automated Answer Scoring system. The paper goes through the existing techniques for automated answer scoring systems and then goes on to explain the newly developed system in which scoring is done by the statistical method adopting and integrating rule-based semantic quantum-based features analysis resulting in more accuracy. It is in a way a hybrid system suitable for short answer type scoring. It also presents the methodology and architecture of AS.

scholarly journals Distant Supervision for Relation Extraction with Sentence Selection and Interaction Representation

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Tiantian Chen ◽  
Nianbin Wang ◽  
Hongbin Wang ◽  
Haomin Zhan
Keyword(s):  
Large Scale ◽  
Semantic Information ◽  
State Of The Art ◽  
Relation Extraction ◽  
Semantic Features ◽  
Distant Supervision ◽  
Word Level ◽  
Proposed Model ◽  
Relation Prediction ◽  
Better Than

Distant supervision (DS) has been widely used for relation extraction (RE), which automatically generates large-scale labeled data. However, there is a wrong labeling problem, which affects the performance of RE. Besides, the existing method suffers from the lack of useful semantic features for some positive training instances. To address the above problems, we propose a novel RE model with sentence selection and interaction representation for distantly supervised RE. First, we propose a pattern method based on the relation trigger words as a sentence selector to filter out noisy sentences to alleviate the wrong labeling problem. After clean instances are obtained, we propose the interaction representation using the word-level attention mechanism-based entity pairs to dynamically increase the weights of the words related to entity pairs, which can provide more useful semantic information for relation prediction. The proposed model outperforms the strongest baseline by 2.61 in F1-score on a widely used dataset, which proves that our model performs significantly better than the state-of-the-art RE systems.

The relationship between verbal and gestural contributions in conversation

Gesture ◽  
2009 ◽  
Vol 9 (3) ◽  
pp. 312-336 ◽  
Author(s):  
Jennifer Gerwing ◽  
Meredith Allison
Keyword(s):  
Spatial Information ◽  
Semantic Feature ◽  
Feature Analysis ◽  
Semantic Features ◽  
Essential Information ◽  
Categorical Information ◽  
Complex Picture ◽  
Almost All ◽  
The Relationship ◽  
Semantic Feature Analysis

Gestures and their concurrent words are often said to be meaningfully related and co-expressive. Research has shown that gestures and words are each particularly suited to conveying different kinds of information. In this paper, we describe and compare three methods for investigating the relationship between gestures and words: (1) an analysis of deictic expressions referring to gestures, (2) an analysis of the redundancy between information presented in words vs. in gestures, and (3) an analysis of the semantic features represented in words and gestures. We also apply each of these three methods to one set of data, in which 22 pairs of participants used words and gestures to design the layout of an apartment. Each of the three analyses revealed a different picture of the complementary relationship between gesture and speech. According to the deictic analysis, participant speakers marked only a quarter of their gestures as providing essential information that was missing from the speech, but the redundancy analysis indicated that almost all gestures contributed information that was not in the words. The semantic feature analysis showed that participants conveyed spatial information in their gestures more often than in their words. A follow-up analysis showed that participants contributed categorical information (i.e., the name of each room) in their words. Of the three methods, the semantic feature analysis yielded the most complex picture of the data, and it served to generate additional analyses. We conclude that although analyses of deictic expressions and redundancy are useful for characterizing gesture use in differing conditions, the semantic feature method is best for exploring the complementary, semantic relationship between gesture and speech.

scholarly journals The Automatic Detection of Dataset Names in Scientific Articles

Data ◽  
2021 ◽  
Vol 6 (8) ◽  
pp. 84
Author(s):  
Jenny Heddes ◽  
Pim Meerdink ◽  
Miguel Pieters ◽  
Maarten Marx
Keyword(s):  
Open Access ◽  
Gold Standard ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Rule Based ◽  
Named Entity ◽  
Gold Standard Dataset ◽  
The Many ◽  
Better Than

We study the task of recognizing named datasets in scientific articles as a Named Entity Recognition (NER) problem. Noticing that available annotated datasets were not adequate for our goals, we annotated 6000 sentences extracted from four major AI conferences, with roughly half of them containing one or more named datasets. A distinguishing feature of this set is the many sentences using enumerations, conjunctions and ellipses, resulting in long BI+ tag sequences. On all measures, the SciBERT NER tagger performed best and most robustly. Our baseline rule based tagger performed remarkably well and better than several state-of-the-art methods. The gold standard dataset, with links and offsets from each sentence to the (open access available) articles together with the annotation guidelines and all code used in the experiments, is available on GitHub.

scholarly journals Clinical Concept Extraction with Lexical Semantics to Support Automatic Annotation

2021 ◽  
Vol 18 (20) ◽  
pp. 10564
Author(s):  
Asim Abbas ◽  
Muhammad Afzal ◽  
Jamil Hussain ◽  
Taqdir Ali ◽  
Hafiz Syed Muhammad Bilal ◽  
...  
Keyword(s):  
Deep Learning ◽  
Clinical Decision Support Systems ◽  
Data Driven ◽  
Semantic Features ◽  
Automatic Annotation ◽  
Learning Approaches ◽  
Rule Based ◽  
Concept Extraction ◽  
Clinical Narrative ◽  
Better Than

Extracting clinical concepts, such as problems, diagnosis, and treatment, from unstructured clinical narrative documents enables data-driven approaches such as machine and deep learning to support advanced applications such as clinical decision-support systems, the assessment of disease progression, and the intelligent analysis of treatment efficacy. Various tools such as cTAKES, Sophia, MetaMap, and other rules-based approaches and algorithms have been used for automatic concept extraction. Recently, machine- and deep-learning approaches have been used to extract, classify, and accurately annotate terms and phrases. However, the requirement of an annotated dataset, which is labor-intensive, impedes the success of data-driven approaches. A rule-based mechanism could support the process of annotation, but existing rule-based approaches fail to adequately capture contextual, syntactic, and semantic patterns. This study intends to introduce a comprehensive rule-based system that automatically extracts clinical concepts from unstructured narratives with higher accuracy and transparency. The proposed system is a pipelined approach, capable of recognizing clinical concepts of three types, problem, treatment, and test, in the dataset collected from a published repository as a part of the I2b2 challenge 2010. The system’s performance is compared with that of three existing systems: Quick UMLS, BIO-CRF, and the Rules (i2b2) model. Compared to the baseline systems, the average F1-score of 72.94% was found to be 13% better than Quick UMLS, 3% better than BIO CRF, and 30.1% better than the Rules (i2b2) model. Individually, the system performance was noticeably higher for problem-related concepts, with an F1-score of 80.45%, followed by treatment-related concepts and test-related concepts, with F1-scores of 76.06% and 55.3%, respectively. The proposed methodology significantly improves the performance of concept extraction from unstructured clinical narratives by exploiting the linguistic and lexical semantic features. The approach can ease the automatic annotation process of clinical data, which ultimately improves the performance of supervised data-driven applications trained with these data.

scholarly journals Towards Evaluating Narrative Quality In Student Writing

2018 ◽  
Vol 6 ◽  
pp. 91-106 ◽  
Author(s):  
Swapna Somasundaran ◽  
Michael Flor ◽  
Martin Chodorow ◽  
Hillary Molloy ◽  
Binod Gyawali ◽  
...  
Keyword(s):  
Scoring System ◽  
State Of The Art ◽  
Student Writing ◽  
Narrative Writing ◽  
Automated Scoring ◽  
Linguistic Features

This work lays the foundation for automated assessments of narrative quality in student writing. We first manually score essays for narrative-relevant traits and sub-traits, and measure inter-annotator agreement. We then explore linguistic features that are indicative of good narrative writing and use them to build an automated scoring system. Experiments show that our features are more effective in scoring specific aspects of narrative quality than a state-of-the-art feature set.

Phonomotor Versus Semantic Feature Analysis Treatment for Anomia in 58 Persons With Aphasia: A Randomized Controlled Trial

2019 ◽  
Vol 62 (12) ◽  
pp. 4464-4482 ◽  
Author(s):  
Diane L. Kendall ◽  
Megan Oelke Moldestad ◽  
Wesley Allen ◽  
Janaki Torrence ◽  
Stephen E. Nadeau
Keyword(s):  
Randomized Controlled Trial ◽  
Response Latency ◽  
Controlled Trial ◽  
Semantic Knowledge ◽  
Semantic Feature ◽  
Feature Analysis ◽  
Group Differences ◽  
Randomized Controlled ◽  
Confrontation Naming ◽  
Semantic Feature Analysis

Purpose The ultimate goal of anomia treatment should be to achieve gains in exemplars trained in the therapy session, as well as generalization to untrained exemplars and contexts. The purpose of this study was to test the efficacy of phonomotor treatment, a treatment focusing on enhancement of phonological sequence knowledge, against semantic feature analysis (SFA), a lexical-semantic therapy that focuses on enhancement of semantic knowledge and is well known and commonly used to treat anomia in aphasia. Method In a between-groups randomized controlled trial, 58 persons with aphasia characterized by anomia and phonological dysfunction were randomized to receive 56–60 hr of intensively delivered treatment over 6 weeks with testing pretreatment, posttreatment, and 3 months posttreatment termination. Results There was no significant between-groups difference on the primary outcome measure (untrained nouns phonologically and semantically unrelated to each treatment) at 3 months posttreatment. Significant within-group immediately posttreatment acquisition effects for confrontation naming and response latency were observed for both groups. Treatment-specific generalization effects for confrontation naming were observed for both groups immediately and 3 months posttreatment; a significant decrease in response latency was observed at both time points for the SFA group only. Finally, significant within-group differences on the Comprehensive Aphasia Test–Disability Questionnaire ( Swinburn, Porter, & Howard, 2004 ) were observed both immediately and 3 months posttreatment for the SFA group, and significant within-group differences on the Functional Outcome Questionnaire ( Glueckauf et al., 2003 ) were found for both treatment groups 3 months posttreatment. Discussion Our results are consistent with those of prior studies that have shown that SFA treatment and phonomotor treatment generalize to untrained words that share features (semantic or phonological sequence, respectively) with the training set. However, they show that there is no significant generalization to untrained words that do not share semantic features or phonological sequence features.

Design, Development and Comparison of Heuristic Driven Algorithms Based on the Crossed Domain Products’ Reviews for User’s Summarization

2020 ◽  
Vol 13 (5) ◽  
pp. 884-892
Author(s):  
Sartaj Ahmad ◽  
Ashutosh Gupta ◽  
Neeraj Kumar Gupta
Keyword(s):  
Decision Making ◽  
Online Shopping ◽  
Recent Time ◽  
Design Development ◽  
Different Types ◽  
Feature Based ◽  
Use Of Internet ◽  
Day By Day ◽  
Better Than

Background: In recent time, people love online shopping but before any shopping feedbacks or reviews always required. These feedbacks help customers in decision making for buying any product or availing any service. In the country like India this trend of online shopping is increasing very rapidly because awareness and the use of internet which is increasing day by day. As result numbers of customers and their feedbacks are also increasing. It is creating a problem that how to read all reviews manually. So there should be some computerized mechanism that provides customers a summary without spending time in reading feedbacks. Besides big number of reviews another problem is that reviews are not structured. Objective: In this paper, we try to design, implement and compare two algorithms with manual approach for the crossed domain Product’s reviews. Methods: Lexicon based model is used and different types of reviews are tested and analyzed to check the performance of these algorithms. Results: Algorithm based on opinions and feature based opinions are designed, implemented, applied and compared with the manual results and it is found that algorithm # 2 is performing better than algorithm # 1 and near to manual results. Conclusion: Algorithm # 2 is found better on the different product’s reviews and still to be applied on other product’s reviews to enhance its scope. Finally, it will be helpful to automate existing manual process.

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