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Salton, G.: ¬A new comparison between conventional indexing (MEDLARS) and automatic text processing (SMART) (1972)
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Losee, R.M.: ¬A Gray code based ordering for documents on shelves : classification for browsing and retrieval (1992)
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- Abstract
- A document classifier places documents together in a linear arrangement for browsing or high-speed access by human or computerised information retrieval systems. Requirements for document classification and browsing systems are developed from similarity measures, distance measures, and the notion of subject aboutness. A requirement that documents be arranged in decreasing order of similarity as the distance from a given document increases can often not be met. Based on these requirements, information-theoretic considerations, and the Gray code, a classification system is proposed that can classifiy documents without human intervention. A measure of classifier performance is developed, and used to evaluate experimental results comparing the distance between subject headings assigned to documents given classifications from the proposed system and the Library of Congress Classification (LCC) system
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- a
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Griffiths, A.; Luckhurst, H.C.; Willett, P.: Using interdocument similarity information in document retrieval systems (1986)
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Hoppe, A.: ¬Die systematischen Grundlagen für ein linguistisch orientiertes maschinelles Dokumentationsverfahren (1969)
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Driscoll, J.R.; Rajala, D.A.; Shaffer, W.H.: ¬The operation and performance of an artificially intelligent keywording system (1991)
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- Abstract
- Presents a new approach to text analysis for automating the key phrase indexing process, using artificial intelligence techniques. This mimics the behaviour of human experts by using a rule base consisting of insertion and deletion rules generated by subject-matter experts. The insertion rules are based on the idea that some phrases found in a text imply or trigger other phrases. The deletion rules apply to semantically ambiguous phrases where text presence alone does not determine appropriateness as a key phrase. The insertion and deletion rules are used to transform a list of found phrases to a list of key phrases for indexing a document. Statistical data are provided to demonstrate the performance of this expert rule based system
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Renouf, A.: Making sense of text : automated approaches to meaning extraction (1993)
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Salton, G.; Allen, J.; Buckley, C.; Singhal, A.: Automatic analysis, theme generation, and summarization of machine-readable data (1994)
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Silvester, J.P.: Computer supported indexing : a history and evaluation of NASA's MAI system (1998)
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Hlava, M.M.: Automatic indexing : a matter of degree (2002)
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Yusuff, A.: Automatisches Indexing and Abstracting : Grundlagen und Beispiele (2002)
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- Imprint
- Potsdam : Fachhochschule, FB A-B-D
-
Siebenkäs, A.; Markscheffel, B.: Conception of a workflow for the semi-automatic construction of a thesaurus for the German printing industry (2015)
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- Abstract
- During the BMWI granted project "Print-IT", the need of a thesaurus based uniform and consistent language for the German printing industry became evident. In this paper we introduce a semi-automatic construction approach for such a thesaurus and present a workflow which supports users to generate thesaurus typical information structures from relevant digitalized resources with the help of common IT-tools.
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- a
-
Ferber, R.: Automated indexing with thesaurus descriptors : a co-occurence based approach to multilingual retrieval (1997)
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- Abstract
- Indexing documents with descriptors from a multilingual thesaurus is an approach to multilingual information retrieval. However, manual indexing is expensive. Automazed indexing methods in general use terms found in the document. Thesaurus descriptors are complex terms that are often not used in documents or have specific meanings within the thesaurus; therefore most weighting schemes of automated indexing methods are not suited to select thesaurus descriptors. In this paper a linear associative system is described that uses similarity values extracted from a large corpus of manually indexed documents to construct a rank ordering of the descriptors for a given document title. The system is adaptive and has to be tuned with a training sample of records for the specific task. The system was tested on a corpus of some 80.000 bibliographic records. The results show a high variability with changing parameter values. This indicated that it is very important to empirically adapt the model to the specific situation it is used in. The overall median of the manually assigned descriptors in the automatically generated ranked list of all 3.631 descriptors is 14 for the set used to adapt the system and 11 for a test set not used in the optimization process. This result shows that the optimization is not a fitting to a specific training set but a real adaptation of the model to the setting
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-
Clavel, G.; Walther, F.; Walther, J.: Indexation automatique de fonds bibliotheconomiques (1993)
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- Abstract
- A discussion of developments to date in the field of computerized indexing, based on presentations given at a seminar held at the Institute of Policy Studies in Paris in Nov 91. The methods tested so far, based on a linguistic approach, whether using natural language or special thesauri, encounter the same central problem - they are only successful when applied to collections of similar types of documents covering very specific subject areas. Despite this, the search for some sort of universal indexing metalanguage continues. In the end, computerized indexing works best when used in conjunction with manual indexing - ideally in the hands of a trained library science professional, who can extract the maximum value from a collection of documents for a particular user population
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Ahmed, M.: Automatic indexing for agriculture : designing a framework by deploying Agrovoc, Agris and Annif (2023)
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- Abstract
- There are several ways to employ machine learning for automating subject indexing. One popular strategy is to utilize a supervised learning algorithm to train a model on a set of documents that have been manually indexed by subject matter using a standard vocabulary. The resulting model can then predict the subject of new and previously unseen documents by identifying patterns learned from the training data. To do this, the first step is to gather a large dataset of documents and manually assign each document a set of subject keywords/descriptors from a controlled vocabulary (e.g., from Agrovoc). Next, the dataset (obtained from Agris) can be divided into - i) a training dataset, and ii) a test dataset. The training dataset is used to train the model, while the test dataset is used to evaluate the model's performance. Machine learning can be a powerful tool for automating the process of subject indexing. This research is an attempt to apply Annif (http://annif. org/), an open-source AI/ML framework, to autogenerate subject keywords/descriptors for documentary resources in the domain of agriculture. The training dataset is obtained from Agris, which applies the Agrovoc thesaurus as a vocabulary tool (https://www.fao.org/agris/download).
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-
Asula, M.; Makke, J.; Freienthal, L.; Kuulmets, H.-A.; Sirel, R.: Kratt: developing an automatic subject indexing tool for the National Library of Estonia : how to transfer metadata information among work cluster members (2021)
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- Abstract
- Manual subject indexing in libraries is a time-consuming and costly process and the quality of the assigned subjects is affected by the cataloger's knowledge on the specific topics contained in the book. Trying to solve these issues, we exploited the opportunities arising from artificial intelligence to develop Kratt: a prototype of an automatic subject indexing tool. Kratt is able to subject index a book independent of its extent and genre with a set of keywords present in the Estonian Subject Thesaurus. It takes Kratt approximately one minute to subject index a book, outperforming humans 10-15 times. Although the resulting keywords were not considered satisfactory by the catalogers, the ratings of a small sample of regular library users showed more promise. We also argue that the results can be enhanced by including a bigger corpus for training the model and applying more careful preprocessing techniques.
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-
Hlava, M.M.K.: Machine-Aided Indexing (MAI) in a multilingual environemt (1992)
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- Abstract
- The Machine-Aided Indexing (MAI) program, developed by Access Innovations, Inc., is a semantic based, Boolean statement, rule interpreting application designed to operate in a multilingual environment. Use of MAI across several languages with controlled vocabularies for each language provides a consistency in indexing not available through any other mechanism
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-
Cunningham, P.; Veale, T.; Conway, A.: Knowledge acquisition for concept indexing in document retrieval (1992)
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- Abstract
- Describes TWIG, a system for knowledge acquisition from text for use in an intelligent document database system. Documents are scanned into the system and converted into a hypertext thus providing a richer environment for browsing and retrieval. The knowledge acquisition phase is blackboard based with the text analysis expertise partitioned into agents that communicate through the blackboard
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- a
-
Dow Jones unveils knowledge indexing system (1997)
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- Abstract
- Dow Jones Interactive Publishing has developed a sophisticated automatic knowledge indexing system that will allow searchers of the Dow Jones News / Retrieval service to get highly targeted results from a search in the service's Publications Library. Instead of relying on a thesaurus of company names, the new system uses a combination of that basic algorithm plus unique rules based on the editorial styles of individual publications in the Library. Dow Jones have also announced its acceptance of the definitions of 'selected full text' and 'full text' from Bibliodata's Fulltext Sources Online directory
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-
Vinyals, O.; Toshev, A.; Bengio, S.; Erhan, D.: ¬A picture is worth a thousand (coherent) words : building a natural description of images (2014)
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- Content
- "People can summarize a complex scene in a few words without thinking twice. It's much more difficult for computers. But we've just gotten a bit closer -- we've developed a machine-learning system that can automatically produce captions (like the three above) to accurately describe images the first time it sees them. This kind of system could eventually help visually impaired people understand pictures, provide alternate text for images in parts of the world where mobile connections are slow, and make it easier for everyone to search on Google for images. Recent research has greatly improved object detection, classification, and labeling. But accurately describing a complex scene requires a deeper representation of what's going on in the scene, capturing how the various objects relate to one another and translating it all into natural-sounding language. Many efforts to construct computer-generated natural descriptions of images propose combining current state-of-the-art techniques in both computer vision and natural language processing to form a complete image description approach. But what if we instead merged recent computer vision and language models into a single jointly trained system, taking an image and directly producing a human readable sequence of words to describe it? This idea comes from recent advances in machine translation between languages, where a Recurrent Neural Network (RNN) transforms, say, a French sentence into a vector representation, and a second RNN uses that vector representation to generate a target sentence in German. Now, what if we replaced that first RNN and its input words with a deep Convolutional Neural Network (CNN) trained to classify objects in images? Normally, the CNN's last layer is used in a final Softmax among known classes of objects, assigning a probability that each object might be in the image. But if we remove that final layer, we can instead feed the CNN's rich encoding of the image into a RNN designed to produce phrases. We can then train the whole system directly on images and their captions, so it maximizes the likelihood that descriptions it produces best match the training descriptions for each image.
Our experiments with this system on several openly published datasets, including Pascal, Flickr8k, Flickr30k and SBU, show how robust the qualitative results are -- the generated sentences are quite reasonable. It also performs well in quantitative evaluations with the Bilingual Evaluation Understudy (BLEU), a metric used in machine translation to evaluate the quality of generated sentences. A picture may be worth a thousand words, but sometimes it's the words that are most useful -- so it's important we figure out ways to translate from images to words automatically and accurately. As the datasets suited to learning image descriptions grow and mature, so will the performance of end-to-end approaches like this. We look forward to continuing developments in systems that can read images and generate good natural-language descriptions. To get more details about the framework used to generate descriptions from images, as well as the model evaluation, read the full paper here." Vgl. auch: https://news.ycombinator.com/item?id=8621658.
- Source
- http://googleresearch.blogspot.de/2014/11/a-picture-is-worth-thousand-coherent.html
-
Salton, G.; Wong, A.; Yang, C.S.: ¬A vector space model for automatic indexing (1975)
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