基于语义概念嵌入的文本相似度估计
Estimating Text Similarity based on Semantic Concept Embeddings
AI总结：
针对Word2V词嵌入在表层表示和歧义词处理上的不足，提出基于MultiNet语义网络的语义概念嵌入(CE)，结合传统词嵌入显著提升了营销目标群体预测准确率。
摘要：
由于易于使用且高准确率，Word2Vec (W2V) 词嵌入在词语、句子和整篇文档的语义表示以及语义相似度估计方面取得了巨大成功。然而，它们存在一个缺点，即直接从表层表示中提取，这不能充分代表人类思维过程，并且对于高度歧义的词语表现不佳。因此，我们提出了基于 MultiNet 语义网络 (SN) 形式化的语义概念嵌入 (CE)，它解决了这两个缺点。在营销目标群体分布任务上的评估表明，通过将传统词嵌入与语义 CE 相结合，可以提高预测目标群体的准确率。
英文摘要：
Due to their ease of use and high accuracy, Word2Vec (W2V) word embeddings enjoy great success in the semantic representation of words, sentences, and whole documents as well as for semantic similarity estimation. However, they have the shortcoming that they are directly extracted from a surface representation, which does not adequately represent human thought processes and also performs poorly for highly ambiguous words. Therefore, we propose Semantic Concept Embeddings (CE) based on the MultiNet Semantic Network (SN) formalism, which addresses both shortcomings. The evaluation on a marketing target group distribution task showed that the accuracy of predicted target groups can be increased by combining traditional word embeddings with semantic CEs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
