利用类标签提高基于嵌入的文本分类性能
Exploiting Class Labels to Boost Performance on Embedding-based Text Classification
摘要：
文本分类是处理文本数据最常见的任务之一，有助于从大规模数据集进行研究。不同种类的嵌入最近已经成为文本分类的事实标准。这些嵌入有能力捕获从大型外部集合中出现的单词推断出的含义。虽然它们是由外部集合构建的，但它们不知道手头的分类数据集中单词的分布特征，最重要的是包括训练数据中单词在类中的分布。为了充分利用这些嵌入作为特征并提高使用它们的分类器的性能，我们引入了一个加权方案，词频类别比（TF-CR），它可以在计算词嵌入时对高频，类别独占词进行更高的加权。我们在8个数据集上的实验表明了TF-CR的有效性，在大多数情况下，与著名的加权方案TF-IDF和KLD以及没有加权方案相比，TF-CR的性能得分得到了提高。
英文摘要：
Text classification is one of the most frequent tasks for processing textual data, facilitating among others research from large-scale datasets. Embeddings of different kinds have recently become the de facto standard as features used for text classification. These embeddings have the capacity to capture meanings of words inferred from occurrences in large external collections. While they are built out of external collections, they are unaware of the distributional characteristics of words in the classification dataset at hand, including most importantly the distribution of words across classes in training data. To make the most of these embeddings as features and to boost the performance of classifiers using them, we introduce a weighting scheme, Term Frequency-Category Ratio (TF-CR), which can weight high-frequency, category-exclusive words higher when computing word embeddings. Our experiments on eight datasets show the effectiveness of TF-CR, leading to improved performance scores over the well-known weighting schemes TF-IDF and KLD as well as over the absence of a weighting scheme in most cases.
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。
