Neural-based Word Sense Disambiguation with Knowledge-base Fusion


Neural-based Word Sense Disambiguation with Knowledge-base Fusion – The recently proposed task-based evaluation and recognition systems, such as the word sense recognition approach, or the word pair-based evaluation framework, have been shown to benefit from semantic information such as speaker attributes and sentence-level lexical resources. We present a learning based evaluation framework for a combination of these two tasks, which use semantic information for the evaluation of each task. We propose the evaluation framework as a novel semantic evaluation model, which learns to recognize a phrase, using its speaker attributes and sentence-level lexical resources. Additionally, we extend the evaluation model to classify phrase pairs as a sequence of phrase pairs (as opposed to a list of phrase pairs), which allows us to use semantic resources for this task. Our evaluation results show that the recognition, recognizing, and ranking of phrase pairs are significantly improved.

This chapter proposes the task of generating a query-answer pair, such that a given query is a good match with a response. Such an algorithm is very expensive, so we propose a general algorithm called a query-answer pair algorithm (QA) that is based on a combination of word embedding and word recognition. Our algorithm has several advantages and many advantages beyond the standard query-answer pair approach. Firstly, it is faster to compute the person query pairs, i.e., we compute the correct answer pair from a query of the query. The proposed algorithm compares the two queries, and selects the query answer that matches the query. Therefore, we can calculate the person query pairs from a query-answer pair by hand. Secondly, we can use the query answer pairs to compute the query’s match with the person, which corresponds with a more accurate and complete query answer. Finally, it is easy to implement our algorithm, with the help of the query-answer pair (QA), to perform the same task using natural language processing.

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Neural-based Word Sense Disambiguation with Knowledge-base Fusion

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    Predicting the person through word embeddingThis chapter proposes the task of generating a query-answer pair, such that a given query is a good match with a response. Such an algorithm is very expensive, so we propose a general algorithm called a query-answer pair algorithm (QA) that is based on a combination of word embedding and word recognition. Our algorithm has several advantages and many advantages beyond the standard query-answer pair approach. Firstly, it is faster to compute the person query pairs, i.e., we compute the correct answer pair from a query of the query. The proposed algorithm compares the two queries, and selects the query answer that matches the query. Therefore, we can calculate the person query pairs from a query-answer pair by hand. Secondly, we can use the query answer pairs to compute the query’s match with the person, which corresponds with a more accurate and complete query answer. Finally, it is easy to implement our algorithm, with the help of the query-answer pair (QA), to perform the same task using natural language processing.


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