[PDF] Top 20 Text Representations for Patent Classification

... the patent domain and both experi- enced problems with long, complex noun phrases consisting of sequences of words that can function as adjective/adverb or noun and that are not interrupted by function words that ... See full document

... We always use a development set to evaluate f (x) during learning and report the final result on an unseen test set. We summarize the hyperparam- eters selected by our method, and the accuracies achieved (on test data) ... See full document

... a small scale dataset based on TAC-KBP Slot Filling Track. We compare our results against some state-of-the-art methods (Zeng et al., 2014; Zhou et al., 2016) of SemEval- 2010 Task8, and our model achieves a bet- ter ... See full document

... for text categorization; one of their outcomes was that, in many cases, the IDF factor is not significant for the categorization task, leading to no improvement of the ...specific classification tasks, such ... See full document

... tributed representations of variable-length text sequences in multiple languages simultane- ...rive representations of multi-word sequences as weighted sums of individual word vec- tors, our model ... See full document

... These classification systems are too fine to realistically achieve sufficiently high accuracy by automated classifiers so some efforts have focused on using them for the preclassification stage, where ... See full document

... a text and is helpful in many NLP tasks. Learning latent representations of discourse is an attractive alternative to acquiring expen- sive labeled discourse ...for text classification that ... See full document

... We divided the dataset into training sets, validation sets, and test sets by 8:1:1. That is, 80% data were used for training the word2vec and classifier. In con- structing the word vector model, the size of word vectors ... See full document

... training text, asymptotic effi- ciency of logistic regression will start to work in its favor over the finite sample advantages of a generative classifier (Ng and Jordan, 2002; Taddy, ...distributed ... See full document

... Sector Classification, is split-name, where each token has the same embedding regardless whether it is used in a proper-name or a common- noun ...Sector Classification, and sup- ports the notion that ... See full document

... con- text representation and 300 dimensional word em- beddings, which are again initialized with the pre- trained embeddings in the previous section keep- ing them ... See full document

... Some try to select more useful contextual infor- mation to expand and enrich the original text, e.g. using large unlabeled corpora, such as Wikipedi- a (Banerjee et al., 2007) and WordNet (Hu et al., 2009). A ... See full document

... words to represent documents. Clustering words into meaningful groups based on some measure of similarity to represent text is not a new concept. One of the classic approaches is term clustering in an unsupervised ... See full document

... word representations to directly produce different types of analyses traditionally assigned to down- stream ...from text, but recent work challenges many of these assump- ...input representations, ... See full document

... four text classification datasets with binary and tf-idf representations and using multinomial na¨ıve Bayes, logistic regression, and support vector ma- ... See full document

... Inspired by the above works, and recent suc- cesses of adversarial learning (Goodfellow et al., 2014; Ganin et al., 2016), we propose a novel ap- proach for privacy-preserving learning of unbiased representations. ... See full document

... We experiment with two types of references to evaluate the candidate summary: firstly, we com- pare the candidate summary against the original passage directly, and secondly, we extract key sen- tences from the original ... See full document

... entity-specific representations which are dy- namically updated. Text is generated con- ditioned on the data input and entity mem- ory representations using hierarchical atten- tion at each time ... See full document

... In this paper we have represented two aspects used in mobile phishing detection. Identity extraction gives only warning about phishing webpage or URL extracted from screenshots taken. Smishing detection is done through ... See full document

... Some papers have attempted to integrate the WordNet (Varelas et al., 2005) lexical thesaurus to expand queries with synonymous terms (Zhang et al., 2009). Veeramachaneni (2010) focused on unsupervised learning for ... See full document