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CiteSeerX — Indexing Spoken Audio By LSA And SOMS
https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.34.8518#:~:text=This%20paper%20presents%20an%20indexing%20system%20for%20spoken,display%20the%20semantic%20structures%20of%20the%20document%20collection.
Indexing Audio Documents by using Latent Semantic …
https://www.sciencedirect.com/science/article/pii/B9780444502704500292
Methods for enhancing the indexing of spoken documents by using latent semantic analysis and self- organizing maps are presented, motivated and tested. The idea is to extract extra in- formation from the structure of the document collection and use it for more accurate indexing by generating new index terms and stochastic index weights.
Indexing Audio Documents by Using Latent Semantic …
https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.45.1927
Methods for enhancing the indexing of spoken documents by using latent semantic analysis and self-organizing maps are presented, motivated and tested. The idea is to extract extra information from the structure of the document collection and use it for more accurate indexing by generating new index terms and stochastic index weights.
Indexing Audio Documents by Using Latent Semantic …
https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.43.9300
Methods for enhancing the indexing of spoken documents by using latent semantic analysis and selforganizing maps are presented, motivated and tested. The idea is to extract extra information from the structure of the document collection and use it for more accurate indexing by generating new index terms and stochastic index weights.
Indexing Audio Documents by using Latent Semantic Analysis ...
https://infoscience.epfl.ch/record/82536
This paper describes an important application for state-of-art automatic speech recognition, natural language processing and information retrieval systems. Methods for enhancing the indexing of spoken documents by using latent semantic analysis and self-organizing maps are presented, motivated and tested. The idea is to extract extra information from the structure of …
CiteSeerX — Citation Query Latent semantic indexing by ...
https://citeseerx.ist.psu.edu/showciting?doi=10.1.1.360.3490
Indexing Audio Documents by using Latent Semantic Analysis and SOM by Mikko Kurimo - KOHONEN MAPS , 1999 This paper describes an important application for state-of-art automatic speech recognition, natural language processing and information retrieval systems.
CiteSeerX — Indexing Spoken Audio By LSA And SOMS
https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.34.8518
This paper presents an indexing system for spoken audio documents. The framework is indexing and retrieval of broadcast news. The proposed indexing system applies latent semantic analysis (LSA) and self-organizing maps (SOM) to map the documents into a semantic vector space and to display the semantic structures of the document collection.
Fast latent semantic indexing of spoken documents by …
https://www.researchgate.net/publication/3858606_Fast_latent_semantic_indexing_of_spoken_documents_by_using_self-organizing_maps
For indexing, the documents are presented as vectors of word counts, whose dimensionality is rapidly reduced by random mapping (RM). The obtained vectors are projected into the latent semantic...
Indexing by latent semantic analysis - Stanford University
https://web.stanford.edu/class/linguist289/lsi.pdf
terms and documents based on the latent semantic structure is used for indexing and retrieval.’ The particular “latent semantic indexing” (LSI) analysis that we have tried uses singular-value decomposition. We take a large matrix of term-document association data and construct a “semantic” space wherein terms and documents
Document Summarization Using Latent Semantic …
https://towardsdatascience.com/document-summarization-using-latent-semantic-indexing-b747ef2d2af6
Summarization using Latent Semantic Analysis. The LSI gives the weightage of the documents belonging to different topics. Summarization is done by selecting the top N documents from each topic depending on the weightage. This gives a summarization where we get the higher weighted documents from each of the topics for the summary.
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