Requirements engineers have the responsibility for classifying software requirements into functional and nonfunctional variants. As software architects need quality requirements to be known to get their job done, mach...
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ISBN:
(纸本)9783031752001;9783031752018
Requirements engineers have the responsibility for classifying software requirements into functional and nonfunctional variants. As software architects need quality requirements to be known to get their job done, machine learning is employed to speed up and add consistency to the process of identifying and categorizing requirements so that effort may be spent more effectively. We experimented with the effects of different machine learning algorithms, as well as different pre-processing and feature selection techniques. It was determined that, for this application, stop words should not be removed and that performing lemmatization on words provides the most effective features for classification. Furthermore, after finalizing our choices of pre-processing techniques and algorithm to use, we proposed a modification to the Extensive Feature Selector by gathering the most distinctive words in each category and using a list of those as our main features. By using a threshold of 0.013, we obtained an F1 score of 0.787, which is an improvement on the base Enhanced Feature Selector's F1 score of 0.761 with the same number of word features.
Capturing market sentiments and supporting well-informed financial decision-making depend on the developing field of Financial Sentiment Analysis (FSA). naturallanguageprocessing (NLP) has made significant strides i...
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Ontology alignment, a critical process in the Semantic Web for detecting relationships between different ontologies, has traditionally focused on identifying so-called "simple" 1-to-1 relationships through c...
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The rapid evolution of web applications has made personalization and intuitive user experiences increasingly essential. The aim of this work is to provide a tailored large language model (LLM) architecture allowing co...
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Large language models (LLMs) are powerful but resource-intensive, limiting accessibility. HITgram addresses this gap by offering a lightweight platform for n-gram model experimentation, ideal for resource-constrained ...
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