A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Filters
A Joint Neural Model for Patent Classification and Rationale Identification
2019
Transactions of the Japanese society for artificial intelligence
Preserved Fulltext
The proposed method outperformed baseline methods in terms of the rationale identification, implying that incorporating rationales in training is particularly useful in identifying rationales. ...
It was first trained for F-term annotation, and then fine-tuned using the ground-truth rationales to discriminate rationales from non-rationales. ...
Using the k nearest neighbor method and BM25 in the patent document categorization subtask at NTCIR-5, in Proceedings of NTCIR-5 Workshop Meeting (2005) ( 3) Recurrent Neural Network (RNN) 1 1 F F ...
doi:10.1527/tjsai.f-ic1
fatcat:xq4od66nmjgalorultr4zltio4
Automated Patent Categorization and Guided Patent Search using IPC as Inspired by MeSH and PubMed
2013
Journal of Biomedical Semantics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
Acknowledgments 3 Acknowledgments The work presented in this thesis was carried out in equal parts at Roche Diagnostics GmbH in Penzberg and at the Biotechnology Center (BIOTEC) in Dresden. ...
Our methods use both keywords and class codes that the user enters to retrieve additional relevant keywords and classes that are then suggested to the user. ...
such as Naive Bayes and k-nearest
neighbor. ...
doi:10.1186/2041-1480-4-s1-s3
pmid:23734562
pmcid:PMC3632996
fatcat:mqdxpiitgzbx3akonwwzfq2edq
Content Facets For Individual Information Needs In Media
2018
Zenodo
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Third, the best features have successfully been used to classify traditional and social media content in both types of content facets. ...
The amount of content published in traditional media is huge and steadily growing. Additionally, social media gained momentum since people use blogs to share comments and opinions to current events. ...
k Nearest neighbor Algorithm The k Nearest neighbor (k-NN) algorithm [Aha et al., 1991] is a text classification algorithm that takes into consideration the k nearest neighbors of an item in the vector ...
doi:10.5281/zenodo.1196397
fatcat:udr3736ejbek5lzl34tu4g4ppq
Content Facets For Individual Information Needs In Media
2018
Zenodo
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Third, the best features have successfully been used to classify traditional and social media content in both types of content facets. ...
The amount of content published in traditional media is huge and steadily growing. Additionally, social media gained momentum since people use blogs to share comments and opinions to current events. ...
k Nearest neighbor Algorithm The k Nearest neighbor (k-NN) algorithm [Aha et al., 1991] is a text classification algorithm that takes into consideration the k nearest neighbors of an item in the vector ...
doi:10.5281/zenodo.1195993
fatcat:ce3ljnthjfhkpir3y4atnnlicy
Multimodal Legal Information Retrieval
2018
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
GoNTogle (Bikakis et al., 2010) uses weighted k Nearest Neighbor (kNN) classifier for document annotation and retrieval. ...
TABLE 5 . 5 1: Evaluation on Choi's Dataset using P k error metric. ...
doi:10.6092/unibo/amsdottorato/8634
fatcat:afij2o54njfe7cqwgpdzyvioba