Improving large-scale k-nearest neighbor text categorization with label autoencoders

In this paper, we introduce a multi-label lazy learning approach to deal with automatic semantic indexing in large document collections in the presence of complex and structured label vocabularies with high inter-label correlation. The proposed method is an evolution of the traditional k-Nearest Neighbors algorithm which uses a large autoencoder trained to map the large label space to a reduced size latent space and to regenerate the predicted labels from this latent space. We have evaluated our proposal in a large portion of the MEDLINE biomedical document collection which uses the Medical Subject Headings (MeSH) thesaurus as a controlled vocabulary. In our experiments we propose and evaluate several document representation approaches and different label autoencoder configurations.Ministerio de Ciencia e Innovación | Ref. PID2020-113230RB-C2

Overview of BioASQ 2021-MESINESP track. Evaluation of advance hierarchical classification techniques for scientific literature, patents and clinical trials

CLEF 2021 – Conference and Labs of the Evaluation Forum, September 21–24, 2021, Bucharest, Romania,There is a pressing need to exploit recent advances in natural language processing technologies, in particular language models and deep learning approaches, to enable improved retrieval, classification and ultimately access to information contained in multiple, heterogeneous types of documents. This is particularly true for the field of biomedicine and clinical research, where medical experts and scientists need to carry out complex search queries against a variety of document collections, including literature, patents, clinical trials or other kind of content like EHRs. Indexing documents with structured controlled vocabularies used for semantic search engines and query expansion purposes is a critical task for enabling sophisticated user queries and even cross-language retrieval. Due to the complexity of the medical domain and the use of very large hierarchical indexing terminologies, implementing efficient automatic systems to aid manual indexing is extremely difficult. This paper provides a summary of the MESINESP task results on medical semantic indexing in Spanish (BioASQ/ CLEF 2021 Challenge). MESINESP was carried out in direct collaboration with literature content databases and medical indexing experts using the DeCS vocabulary, a similar resource as MeSH terms. Seven participating teams used advanced technologies including extreme multilabel classification and deep language models to solve this challenge which can be viewed as a multi-label classification problem. MESINESP resources, we have released a Gold Standard collection of 243,000 documents with a total of 2179 manual annotations divided in train, development and test subsets covering literature, patents as well as clinical trial summaries, under a cross-genre training and data labeling scenario. Manual indexing of the evaluation subsets was carried out by three independent experts using a specially developed indexing interface called ASIT. Additionally, we have published a collection of large-scale automatic semantic annotations based on NER systems of these documents with mentions of drugs/medications (170,000), symptoms (137,000), diseases (840,000) and clinical procedures (415,000). In addition to a summary of the used technologies by the teams, this paperS

Natural language processing

Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems

Special Libraries, December 1964

Volume 55, Issue 10https://scholarworks.sjsu.edu/sla_sl_1964/1009/thumbnail.jp

Special Libraries, December 1964

Volume 55, Issue 10https://scholarworks.sjsu.edu/sla_sl_1964/1009/thumbnail.jp

Keywords given by authors of scientific articles in database descriptors

This paper analyses the keywords given by authors of scientific articles and the descriptors assigned to the articles in order to ascertain the presence of the keywords in the descriptors. 640 INSPEC, CAB abstracts, ISTA and LISA database records were consulted. After detailed comparisons it was found that keywords provided by authors have an important presence in the database descriptors studied, since nearly 25% of all the keywords appeared in exactly the same form as descriptors, with another 21% while normalized, are still detected in the descriptors. This means that almost 46% of keywords appear in the descriptors, either as such or after normalization. Elsewhere, three distinct indexing policies appear, one represented by INSPEC and LISA (indexers seem to have freedom to assign the descriptors they deem necessary); another is represented by CAB (no record has fewer than four descriptors and, in general, a large number of descriptors is employed; in contrast, in ISTA, a certain institutional code towards economy in indexing, since 84% of records contain only four descriptors

Bridging the Semantic Gap in Multimedia Information Retrieval: Top-down and Bottom-up approaches

Semantic representation of multimedia information is vital for enabling the kind of multimedia search capabilities that professional searchers require. Manual annotation is often not possible because of the shear scale of the multimedia information that needs indexing. This paper explores the ways in which we are using both top-down, ontologically driven approaches and bottom-up, automatic-annotation approaches to provide retrieval facilities to users. We also discuss many of the current techniques that we are investigating to combine these top-down and bottom-up approaches

Applying digital content management to support localisation

The retrieval and presentation of digital content such as that on the World Wide Web (WWW) is a substantial area of research. While recent years have seen huge expansion in the size of web-based archives that can be searched efficiently by commercial search engines, the presentation of potentially relevant content is still limited to ranked document lists represented by simple text snippets or image keyframe surrogates. There is expanding interest in techniques to personalise the presentation of content to improve the richness and effectiveness of the user experience. One of the most significant challenges to achieving this is the increasingly multilingual nature of this data, and the need to provide suitably localised responses to users based on this content. The Digital Content Management (DCM) track of the Centre for Next Generation Localisation (CNGL) is seeking to develop technologies to support advanced personalised access and presentation of information by combining elements from the existing research areas of Adaptive Hypermedia and Information Retrieval. The combination of these technologies is intended to produce significant improvements in the way users access information. We review key features of these technologies and introduce early ideas for how these technologies can support localisation and localised content before concluding with some impressions of future directions in DCM