Natural Language Processing (NLP) driven categorisation and detection of discourse in historical US patents
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This article is inspired by the observation of a massive change of opinion on the part of economists regarding the question of the patent as a mechanism allowing an economically acceptable solution to be found to the so-called “imperfect appropriability” p ...
In this paper, we present an approach for topic-level video snippet-based extractive summarization, which relies on con tent-based recommendation techniques. We identify topic-level snippets using transcripts of all videos in the dataset and indexed these ...
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Recently, there has been a lot of effort to represent words in continuous vector spaces. Those representations have been shown to capture both semantic and syntactic information about words. However, distributed representations of phrases remain a challeng ...
Recent works on word representations mostly rely on predictive models. Distributed word representations (aka word embeddings) are trained to optimally predict the contexts in which the corresponding words tend to appear. Such models have succeeded in captu ...
Word embeddings resulting from neural language models have been shown to be a great asset for a large variety of NLP tasks. However, such architecture might be difficult and time-consuming to train. Instead, we propose to drastically simplify the word embe ...
This article decomposes the R&D-patent relationship at the industry level to shed light on the sources of the worldwide surge in patent applications. The empirical analysis is based on a unique data set that includes five patent indicators computed for 18 ...
Word embeddings resulting from neural language models have been shown to be successful for a large variety of NLP tasks. However, such architecture might be difficult to train and time-consuming. Instead, we propose to drastically sim- plify the word embed ...
In this thesis, we propose novel solutions to similarity learning problems on collaborative networks. Similarity learning is essential for modeling and predicting the evolution of collaborative networks. In addition, similarity learning is used to perform ...
Word embeddings resulting from neural lan- guage models have been shown to be successful for a large variety of NLP tasks. However, such architecture might be difficult to train and time-consuming. Instead, we propose to drastically simplify the word embed ...