Analyzing various topic modeling approaches for Domain-Specific language model
作者： Disha Kaur Phull , G. Bharadwaja Kumar
DOI: 10.1109/NETACT.2017.8076743
关键词: Topic model 、 Semantics 、 Language model 、 Computer science 、 Latent semantic indexing 、 Modeling language 、 Natural language processing 、 Pachinko allocation 、 Hierarchical Dirichlet process 、 Latent Dirichlet allocation 、 Domain-specific language 、 Artificial intelligence 、 Machine translation 、 Search engine indexing
摘要: In recent times, topic modeling approaches for adaptive language have been extensively explored Natural Language Processing applications such as machine translation, speech recognition etc. model is extremely fragile in adapting towards the required domain, so it needs to be channeled an area or a producing optimal results. This paves need investigate various which are used infer knowledge from large corpora. this paper, we mileage techniques include Latent Semantic Indexing, Dirichlet Allocation and Hierarchical Process. process, baseline dynamically adapted different topics results analyzed these three approaches.
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