2015. Short text similarity with word embeddings. In 24th ACM International on Con- ference on Information and Knowledge Management. ACM, 1411-1420.Kenter T, Rijke M D. Short text similarity with word embeddings [C]// Proc of ACM International Conference on Information and Knowledge Management....
classification tasks, this paper studies text classification problems under the condition of few labelled samples and proposes a few-shot short-text classification method (Meta-FCS) that combines the advantages of text semantic vector representation, meta-learning, fine-tuning and vector similarity ...
and lowercasing all words. To reduce the size of the resulting vocabulary V , we also replace all digits with 0. The size of the word vocabulary V for experiments using TRAIN set is 17,023 with approximately 95% of words initialized using wor2vec embeddings and the remaining 5% words are...
Liu, W., Pang, J., Li, N.et al.Few-shot short-text classification with language representations and centroid similarity.Appl Intell53, 8061–8072 (2023). https://doi.org/10.1007/s10489-022-03880-y Issue Date
L. [9] try to use word vectors to represent the text and build neural language models for English language. However, the formations of Chinese word are different from English. In this paper, we build the model to train Chinese word embeddings. Cosine similarity is the most popular measure...
text similarity approaches only rely on the lexical matching while ignoring the semantic meaning of words. Recent advances in distributional semantic space have opened an alternative approach in utilizing high-quality word embeddings to aid the interpretation of text semantics. In this paper, we ...
中文长文本分类、短句子分类、多标签分类、两句子相似度(Chinese Text Classification of Keras NLP, multi-label classify, or sentence classify, long or short),字词句向量嵌入层(embeddings)和网络层(graph)构建基类,FastText,TextCNN,CharCNN,TextRNN, RCNN, DCNN, DPCNN, VDCNN, CRNN, Bert, Xlnet, Albert...
The model first represents each word in the text and the labels with contextual embeddings by BERT, and then uses a CNN layer to extract local semantic features of the underlying text. These features and the underlying text are embedded as the initial Datasets To the best of our knowledge, ...
(2018) compares different automatic grading algorithms used for Arabic free text answer questions. They identify string-based and corpus-based text similarity approaches. Sultan et al. (2016) utilize recent measures of lexical similarity and aggregation of word embeddings (Baroni et al. 2014) to ...
Word embeddings for user profiling in online social networks Comput. Sistemas, 21 (2) (2017), 10.13053/cys-21-2-2734 Google Scholar [17] Berendsen R., de Rijke M., Balog K., Bogers T., van den Bosch A. On the assessment of expertise profiles J. Amer. Soc. Inform. Sci. Technol....