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Journal articles

Academic literature on the topic 'Mean reciprocal rank'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 February 2022

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Journal articles on the topic "Mean reciprocal rank"

1

Avramidis, Eleftherios. "RankEval: Open Tool for Evaluation of Machine-Learned Ranking." Prague Bulletin of Mathematical Linguistics 100, no. 1 (2013): 63–72. http://dx.doi.org/10.2478/pralin-2013-0012.

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Abstract Recent research and applications for evaluation and quality estimation of Machine Translation require statistical measures for comparing machine-predicted ranking against gold sets annotated by humans. Additional to the existing practice of measuring segment-level correlation with Kendall tau, we propose using ranking metrics from the research field of Information Retrieval such as Mean Reciprocal Rank, Normalized Discounted Cumulative Gain and Expected Reciprocal Rank. These reward systems that predict correctly the highest ranked items than the one of lower ones. We present an open

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2

Demner-Fushman, Dina, Yassine Mrabet, and Asma Ben Abacha. "Consumer health information and question answering: helping consumers find answers to their health-related information needs." Journal of the American Medical Informatics Association 27, no. 2 (2019): 194–201. http://dx.doi.org/10.1093/jamia/ocz152.

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Abstract Objective Consumers increasingly turn to the internet in search of health-related information; and they want their questions answered with short and precise passages, rather than needing to analyze lists of relevant documents returned by search engines and reading each document to find an answer. We aim to answer consumer health questions with information from reliable sources. Materials and Methods We combine knowledge-based, traditional machine and deep learning approaches to understand consumers’ questions and select the best answers from consumer-oriented sources. We evaluate the

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3

Surdeanu, Mihai, Massimiliano Ciaramita, and Hugo Zaragoza. "Learning to Rank Answers to Non-Factoid Questions from Web Collections." Computational Linguistics 37, no. 2 (2011): 351–83. http://dx.doi.org/10.1162/coli_a_00051.

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This work investigates the use of linguistically motivated features to improve search, in particular for ranking answers to non-factoid questions. We show that it is possible to exploit existing large collections of question–answer pairs (from online social Question Answering sites) to extract such features and train ranking models which combine them effectively. We investigate a wide range of feature types, some exploiting natural language processing such as coarse word sense disambiguation, named-entity identification, syntactic parsing, and semantic role labeling. Our experiments demonstrat

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4

KASHEFI, OMID, MOHSEN SHARIFI, and BEHROOZ MINAIE. "A novel string distance metric for ranking Persian respelling suggestions." Natural Language Engineering 19, no. 2 (2012): 259–84. http://dx.doi.org/10.1017/s1351324912000186.

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AbstractSpelling errors in digital documents are often caused by operational and cognitive mistakes, or by the lack of full knowledge about the language of the written documents. Computer-assisted solutions can help to detect and suggest replacements. In this paper, we present a new string distance metric for the Persian language to rank respelling suggestions of a misspelled Persian word by considering the effects of keyboard layout on typographical spelling errors as well as the homomorphic and homophonic aspects of words for orthographical misspellings. We also consider the misspellings cau

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5

Crocetti, Elisabetta, Silvia Moscatelli, Jolien Van der Graaff, Monica Rubini, Wim Meeus, and Susan Branje. "The Interplay of Self–Certainty and Prosocial Development in the Transition from Late Adolescence to Emerging Adulthood." European Journal of Personality 30, no. 6 (2016): 594–607. http://dx.doi.org/10.1002/per.2084.

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The transition from late adolescence to emerging adulthood is a period of the life span that offers young people the possibility to consolidate their self–certainty and prosociality. Both aspects are of core importance for increasing personal and societal well–being. The purpose of this longitudinal study was twofold: (i) to examine patterns of change and stability in self–concept clarity and prosociality; and (ii) to unravel over time associations between these constructs in the transition from late adolescence to emerging adulthood. In addressing both aims, we explored the moderating effects

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6

Cai, Linqin, Sitong Zhou, Xun Yan, and Rongdi Yuan. "A Stacked BiLSTM Neural Network Based on Coattention Mechanism for Question Answering." Computational Intelligence and Neuroscience 2019 (August 21, 2019): 1–12. http://dx.doi.org/10.1155/2019/9543490.

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Deep learning is the crucial technology in intelligent question answering research tasks. Nowadays, extensive studies on question answering have been conducted by adopting the methods of deep learning. The challenge is that it not only requires an effective semantic understanding model to generate a textual representation but also needs the consideration of semantic interaction between questions and answers simultaneously. In this paper, we propose a stacked Bidirectional Long Short-Term Memory (BiLSTM) neural network based on the coattention mechanism to extract the interaction between questi

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7

Ayalew, Yirsaw, Barbara Moeng, and Gontlafetse Mosweunyane. "Experimental evaluation of ontology-based HIV/AIDS frequently asked question retrieval system." Health Informatics Journal 25, no. 4 (2018): 1434–50. http://dx.doi.org/10.1177/1460458218775147.

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This study presents the results of experimental evaluations of an ontology-based frequently asked question retrieval system in the domain of HIV and AIDS. The main purpose of the system is to provide answers to questions on HIV/AIDS using ontology. To evaluate the effectiveness of the frequently asked question retrieval system, we conducted two experiments. The first experiment focused on the evaluation of the quality of the ontology we developed using the OQuaRE evaluation framework which is based on software quality metrics and metrics designed for ontology quality evaluation. The second exp

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8

Savavibool, Nattapong, and Chaveevan Pechsiri. "Touristic Destinations’ Theme Determination for GIS Applications." Applied Mechanics and Materials 66-68 (July 2011): 2272–80. http://dx.doi.org/10.4028/www.scientific.net/amm.66-68.2272.

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This research aims to determine touristic destination’s theme (especially tourism activity theme) from the tourism web documents for geographic information system (GIS) applications, i.e. guiding the main interesting tourism activities to tourists. There are two major problems of the theme acquisition; tourism activity extraction and tourism activity generalization. Therefore, this research proposes of using Naïve Bayes Classifier to determine word co-occurrences between verbs and nouns with the tourism activity concept from web documents. Furthermore, this research also applies the fuzzy conc

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9

Khouri, Adilson Lopes, and Luciano Antonio Digiampietri. "Combining Artificial Intelligence, Ontology, and Frequency-based Approaches to Recommend Activities in Scientific Workflows." Revista de Informática Teórica e Aplicada 25, no. 1 (2018): 39. http://dx.doi.org/10.22456/2175-2745.75048.

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The number of activities provided by scientific workflow management systems is large, which requires scientists to know many of them to take advantage of the reusability of these systems. To minimize this problem, the literature presents some techniques to recommend activities during the scientific workflow construction. In this paper we specified and developed a hybrid activity recommendation system considering information on frequency, input and outputs of activities and ontological annotations. Additionally, this paper presents a modeling of activities recommendation as a classification pro

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10

Breja, Manvi, and Sanjay Kumar Jain. "Analyzing Linguistic Features for Answer Re-Ranking of Why-Questions." Journal of Cases on Information Technology 24, no. 3 (2022): 1–16. http://dx.doi.org/10.4018/jcit.20220701.oa10.

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Why-type non-factoid questions are ambiguous and involve variations in their answers. A challenge in returning one appropriate answer to user requires the process of appropriate answer extraction, re-ranking and validation. There are cases where the need is to understand the meaning and context of a document rather than finding exact words involved in question. The paper addresses this problem by exploring lexico-syntactic, semantic and contextual query-dependent features, some of which are based on deep learning frameworks to depict the probability of answer candidate being relevant for the q

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