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Archival Research01:40

Archival Research

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Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer various research questions. This type of research approach is known as archival research. Archival research relies on looking at past records or data sets to look for interesting patterns or relationships. For example, a researcher might access the academic records of all individuals who enrolled in college within the past ten years and...
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Proteomics01:33

Proteomics

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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
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Surveys02:16

Surveys

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Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally. Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.
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NMR Spectroscopy of Aromatic Compounds01:14

NMR Spectroscopy of Aromatic Compounds

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Aromatic compounds can be identified or analyzed using proton NMR and carbon‐13 NMR. Typically, aromatic hydrogens or hydrogens directly bonded to the aromatic rings are strongly deshielded by the aromatic ring current. Therefore, they absorb in the range of 6.5–8.0 ppm in proton NMR spectra. For instance, aromatic hydrogens directly bonded to the benzene ring absorb at 7.3 ppm. However, aromatic hydrogens of larger rings absorb farther upfield or downfield than the ideal range.
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Data Collection by Observations01:08

Data Collection by Observations

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Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
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Data Collection I

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Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of...
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自然言語処理のためのオープンソースのアラビア語研究論文データセット

Tahani M Almutairi1, Shireen R Saifuddin2, Reem M Alotaibi2

  • 1Department of Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia. tjuhaidelalmutairi@stu.kau.edu.sa.

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PubMed
まとめ

アラビア語研究論文データセット (ARPD) は,学術研究と自然言語処理 (NLP) のタスクをサポートするために作成されました. ARPDのデータセットは,分類とクラスタリングの有望な結果を示し,従来の方法よりも優れています.

キーワード:
アカデミック・コープス学術データセットアラビア語コーパスアラビア語データベースアラビア語データセットアラビア語検索研究論文

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科学分野:

  • 自然言語処理 (NLP)
  • 応用言語学
  • データマイニング
  • 情報の検索
  • 機械翻訳

背景:

  • 既存のアラビア語コーポラは 主にソーシャルメディアやニュースから得られていて 学術的な研究に欠けています
  • NLPや機械翻訳などの応用を進めるのに 専門的なデータセットが必要になってきています
  • アラビア語研究論文データセット (ARPD) が導入され,この空白を埋める.

研究 の 目的:

  • ARPDの作成の裏にある方法論を紹介し,説明する.
  • アラビア語の学術研究に特化した公開データセットを提供すること.
  • 分類とクラスタリングの作業におけるデータセットの有用性を評価する.

主な方法:

  • ARPDは7つの異なるクラスで構築されました.
  • データセットは複数のフォーマットで利用可能であり,研究者のアクセシビリティを最大限に高めています.
  • 実験では,古典的なクラスタリングアルゴリズム,バイオインスピレーションアルゴリズム (PSO,GWO),分類アルゴリズム (SVM) を評価した.

主要な成果:

  • バイオインスピレーションによるアルゴリズム (PSO,GWO) は,デイヴィス-ボウルディン指数で測定されたように,ARPDにおける古典的なクラスタリングアルゴリズムを上回った.
  • サポートベクトルマシン (SVM) アルゴリズムは,分類作業で最高精度 (89%~99%) を達成しました.
  • 他の分類アルゴリズムもARPDで高いパフォーマンスを示した.

結論:

  • アラビア語の学術研究を進めるための貴重な資源です.
  • このデータセットは,高度なNLPモデルの開発と評価を容易にする.
  • この発見は,アラビア語のAIアプリケーションを改善するための専門コーポラの可能性を強調しています.