Conceptual Graphs Based Approach for Subjective Answers Evaluation

2017 ◽

Vol 5 (2) ◽

pp. 1-21

Author(s):

Goonjan Jain ◽

D.K. Lobiyal

Keyword(s):

Language Processing ◽

Evaluation System ◽

Pearson Correlation ◽

Similarity Measures ◽

Conceptual Graphs ◽

Pearson Correlation Coefficient ◽

Evaluation Systems ◽

Automated Evaluation ◽

Processing Techniques ◽

Objective Type

Automated evaluation systems for objective type tests already exist. However, it is challenging to make an automated evaluation system for subjective type tests. Therefore, focus of this paper is on evaluation of simple text based subjective answers using Natural Language Processing techniques. A student's answer is evaluated by comparing it with a model answer of the question. Model answers cannot exactly match with the students' answers due to variability in writing. Therefore, researchers create conceptual graphs for both student as well as model answer and compute similarity between these graphs using techniques of graph similarity measures. Based on the similarity, marks are assigned to an answer. Lastly, in this manuscript authors compare the results obtained by human graders and the proposed system using Pearson correlation coefficient. Also, comparison has been drawn between the results of proposed system with other existing evaluation systems. The experimental evaluation of the proposed system shows promising results.

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Building Related Words in Indonesian and English Translation of Al-Qur’an Vocabulary Based on Distributional Similarity

Jurnal Teknologi Informasi dan Terapan ◽

10.25047/jtit.v7i1.135 ◽

2020 ◽

Vol 7 (1) ◽

pp. 46-53

Author(s):

Rahmad Geri Kurniawan ◽

Moch. Arif Bijaksana

Keyword(s):

Natural Language Processing ◽

Language Processing ◽

English Translation ◽

Gold Standard ◽

Pearson Correlation ◽

Similarity Measures ◽

Lessons Learned ◽

Final Project ◽

Long Time ◽

System Output

The Qur'an is the Muslim holy book as the main source and guide, consisting of 114 surahs, 30 juz and has 6200 fewer verses in it. The search for relationships or arrangements of meaning between words in the Qur'an takes a long time to find and summarize. Obtained from the dictionary, encyclopedia, or thesaurus of the Al-Qur'an vocabulary, which contains each word entry has links with other words. This final project discusses the interrelations and semantic correspondence between words in the Qur'an, which supports to help find inter-related words in it, using linking with distributions that involve important parts in the word embedding. Measurement of the relevance of the word measurement with semantic similarity which is one of the lessons learned in Natural Language Processing (NLP). Extraordinary similarity measures the proximity of a word vector using cosine similarity. The process of converting words in the form of vectors using the fasttext which is the development of the Word2vec algorithm. The dataset is used for translations of the word Al-Qur'an in English and Indonesian. This entry becomes an input into the system then produces a score that represents the interrelationship between words. Evaluation of system output results is to perform performance calculations using Pearson correlation involving the gold standard.

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A Natural Language Processing Approach to Measuring Treatment Adherence and Consistency Using Semantic Similarity

AERA Open ◽

10.1177/23328584211028615 ◽

2021 ◽

Vol 7 ◽

pp. 233285842110286

Author(s):

Kylie L. Anglin ◽

Vivian C. Wong ◽

Arielle Boguslav

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Semantic Similarity ◽

Language Processing ◽

Intervention Implementation ◽

Proof Of Concept ◽

Coaching Intervention ◽

Processing Techniques ◽

Teacher Coaching ◽

The Impact

Though there is widespread recognition of the importance of implementation research, evaluators often face intense logistical, budgetary, and methodological challenges in their efforts to assess intervention implementation in the field. This article proposes a set of natural language processing techniques called semantic similarity as an innovative and scalable method of measuring implementation constructs. Semantic similarity methods are an automated approach to quantifying the similarity between texts. By applying semantic similarity to transcripts of intervention sessions, researchers can use the method to determine whether an intervention was delivered with adherence to a structured protocol, and the extent to which an intervention was replicated with consistency across sessions, sites, and studies. This article provides an overview of semantic similarity methods, describes their application within the context of educational evaluations, and provides a proof of concept using an experimental study of the impact of a standardized teacher coaching intervention.

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Detecting Malicious Windows Commands Using Natural Language Processing Techniques

Innovative Security Solutions for Information Technology and Communications - Lecture Notes in Computer Science ◽

10.1007/978-3-030-12942-2_13 ◽

2019 ◽

pp. 157-169

Author(s):

Muhammd Mudassar Yamin ◽

Basel Katt

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Processing Techniques

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Identification of spam comments using natural language processing techniques

2014 IEEE 10th International Conference on Intelligent Computer Communication and Processing (ICCP) ◽

10.1109/iccp.2014.6936976 ◽

2014 ◽

Cited By ~ 6

Author(s):

Cristina Radulescu ◽

Mihaela Dinsoreanu ◽

Rodica Potolea

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Processing Techniques

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Using Natural Language Processing Techniques for Stock Return Predictions

SSRN Electronic Journal ◽

10.2139/ssrn.2940564 ◽

2017 ◽

Cited By ~ 1

Author(s):

Ming Li Chew ◽

Sahil Puri

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Stock Return ◽

Processing Techniques

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Identifying and intercepting health misinformation on Reddit dermatology forums with artificially intelligent bots using natural language processing (Preprint)

10.2196/preprints.20975 ◽

2021 ◽

Author(s):

Monique B. Sager ◽

Aditya M. Kashyap ◽

Mila Tamminga ◽

Sadhana Ravoori ◽

Christopher Callison-Burch ◽

...

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

The United States ◽

Test Accuracy ◽

Limited Data ◽

Test Environment ◽

Data Set ◽

Inappropriate Care ◽

Processing Techniques

BACKGROUND Reddit, the fifth most popular website in the United States, boasts a large and engaged user base on its dermatology forums where users crowdsource free medical opinions. Unfortunately, much of the advice provided is unvalidated and could lead to inappropriate care. Initial testing has shown that artificially intelligent bots can detect misinformation on Reddit forums and may be able to produce responses to posts containing misinformation. OBJECTIVE To analyze the ability of bots to find and respond to health misinformation on Reddit’s dermatology forums in a controlled test environment. METHODS Using natural language processing techniques, we trained bots to target misinformation using relevant keywords and to post pre-fabricated responses. By evaluating different model architectures across a held-out test set, we compared performances. RESULTS Our models yielded data test accuracies ranging from 95%-100%, with a BERT fine-tuned model resulting in the highest level of test accuracy. Bots were then able to post corrective pre-fabricated responses to misinformation. CONCLUSIONS Using a limited data set, bots had near-perfect ability to detect these examples of health misinformation within Reddit dermatology forums. Given that these bots can then post pre-fabricated responses, this technique may allow for interception of misinformation. Providing correct information, even instantly, however, does not mean users will be receptive or find such interventions persuasive. Further work should investigate this strategy’s effectiveness to inform future deployment of bots as a technique in combating health misinformation. CLINICALTRIAL N/A

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Recent Advances in Conversational Intelligent Tutoring Systems

AI Magazine ◽

10.1609/aimag.v34i3.2485 ◽

2013 ◽

Vol 34 (3) ◽

pp. 42-54 ◽

Cited By ~ 54

Author(s):

Vasile Rus ◽

Sidney D’Mello ◽

Xiangen Hu ◽

Arthur Graesser

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Intelligent Tutoring Systems ◽

Intelligent Tutoring ◽

Individual Student ◽

Learning Progressions ◽

Tutoring Systems ◽

Recent Advances ◽

Processing Techniques

We report recent advances in intelligent tutoring systems with conversational dialogue. We highlight progress in terms of macro and microadaptivity. Macroadaptivity refers to a system’s capability to select appropriate instructional tasks for the learner to work on. Microadaptivity refers to a system’s capability to adapt its scaffolding while the learner is working on a particular task. The advances in macro and microadaptivity that are presented here were made possible by the use of learning progressions, deeper dialogue and natural language processing techniques, and by the use of affect-enabled components. Learning progressions and deeper dialogue and natural language processing techniques are key features of DeepTutor, the first intelligent tutoring system based on learning progressions. These improvements extend the bandwidth of possibilities for tailoring instruction to each individual student which is needed for maximizing engagement and ultimately learning.

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