BM25 Beyond Query-Document Similarity

Pembobotan Berdasarkan Tingkat Kesamaan Semantik pada Metode Fuzzy Semi-Supervised Co-Clustering untuk Pengelompokkan Dokumen Teks

Jurnal ULTIMATICS ◽

10.31937/ti.v6i2.333 ◽

2014 ◽

Vol 6 (2) ◽

pp. 46-51

Author(s):

Galang Amanda Dwi P. ◽

Gregorius Edwadr ◽

Agus Zainal Arifin

Keyword(s):

Supervised Learning ◽

Semantic Similarity ◽

The Other ◽

Classification Result ◽

Document Similarity ◽

The Matrix ◽

Index Terms ◽

Membership Value ◽

Degree Of Similarity

Nowadays, a large number of information can not be reached by the reader because of the misclassification of text-based documents. The misclassified data can also make the readers obtain the wrong information. The method which is proposed by this paper is aiming to classify the documents into the correct group. Each document will have a membership value in several different classes. The method will be used to find the degree of similarity between the two documents is the semantic similarity. In fact, there is no document that doesn’t have a relationship with the other but their relationship might be close to 0. This method calculates the similarity between two documents by taking into account the level of similarity of words and their synonyms. After all inter-document similarity values obtained, a matrix will be created. The matrix is then used as a semi-supervised factor. The output of this method is the value of the membership of each document, which must be one of the greatest membership value for each document which indicates where the documents are grouped. Classification result computed by the method shows a good value which is 90 %. Index Terms - Fuzzy co-clustering, Heuristic, Semantica Similiarity, Semi-supervised learning.

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Scalable Cross-lingual Document Similarity through Language-specific Concept Hierarchies

Proceedings of the 10th International Conference on Knowledge Capture - K-CAP '19 ◽

10.1145/3360901.3364444 ◽

2019 ◽

Cited By ~ 1

Author(s):

Carlos Badenes-Olmedo ◽

José Luis Redondo-García ◽

Oscar Corcho

Keyword(s):

Document Similarity ◽

Specific Concept ◽

Cross Lingual ◽

Concept Hierarchies

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Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Database ◽

10.1093/database/baz085 ◽

2019 ◽

Vol 2019 ◽

Author(s):

Peter Brown ◽

Aik-Choon Tan ◽

Mohamed A El-Esawi ◽

Thomas Liehr ◽

Oliver Blanck ◽

...

Keyword(s):

Literature Search ◽

Relevant Literature ◽

Biomedical Literature ◽

Medical Subject Headings ◽

Document Similarity ◽

Inverse Document Frequency ◽

Research Fields ◽

Experience Levels ◽

Document Frequency ◽

Systematic Biases

Abstract Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.

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