Fake information poses one of the major threats for society in the 21st century. Identifying misi... more Fake information poses one of the major threats for society in the 21st century. Identifying misinformation has become a key challenge due to the amount of fake news that is published daily. Yet, no approach is established that addresses the dynamics and versatility of fake news editorials. Instead of classifying content, we propose an evidence retrieval approach to handle fake news. The learning task is formulated as an unsupervised machine learning problem. For validation purpose, we provide the user with a set of news articles from reliable news sources supporting the hypothesis of the news article in query and the final decision is left to the user. Technically we propose a two-step process: (i) Aggregation-step: With information extracted from the given text we query for similar content from reliable news sources. (ii) Refining-step: We narrow the supporting evidence down by measuring the semantic distance of the text with the collection from step (i). The distance is calculate...
Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)
Word-based embedding approaches such as Word2Vec capture the meaning of words and relations betwe... more Word-based embedding approaches such as Word2Vec capture the meaning of words and relations between them, particularly well when trained with large text collections; however, they fail to do so with small datasets. Extensions such as fastText reduce the amount of data needed slightly, however, the joint task of learning meaningful morphology, syntactic and semantic representations still requires a lot of data. In this paper, we introduce a new approach to warm-start embedding models with morphological information, in order to reduce training time and enhance their performance. We use word embeddings generated using both word2vec and fastText models and enrich them with morphological information of words, derived from kernel principal component analysis (KPCA) of word similarity matrices. This can be seen as explicitly feeding the network morphological similarities and letting it learn semantic and syntactic similarities. Evaluating our models on word similarity and analogy tasks in English and German, we find that they not only achieve higher accuracies than the original skip-gram and fastText models but also require significantly less training data and time. Another benefit of our approach is that it is capable of generating a high-quality representation of infrequent words as, for example, found in very recent news articles with rapidly changing vocabularies. Lastly, we evaluate the different models on a downstream sentence classification task in which a CNN model is initialized with our embeddings and find promising results.
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Papers by Vishwani Gupta