Association for Computational Linguistics. In Proceedings of the 2nd Workshop on Representation Learning for NLP, pages 121–130, Vancouver, Canada. | WS SIG: SIGREP Publisher: Association for Computational Linguistics Note: Pages: 121–130 Language: URL: DOI: 10.18653/v1/W17-2615 Bibkey: grzegorczyk-kurdziel-2017-binary Cite (ACL): Karol Grzegorczyk and Marcin Kurdziel. Anthology ID: W17-2615 Volume: Proceedings of the 2nd Workshop on Representation Learning for NLP Month: August Year: 2017 Address: Vancouver, Canada Venues: RepL4NLP This model can be used to rapidly retrieve a short list of highly relevant documents from a large document collection. Finally, we present a model that simultaneously learns short binary codes and longer, real-valued representations. Results from these experiments indicate that binary paragraph vectors can capture semantics relevant for various domain-specific documents. We also evaluate their precision in transfer learning settings, where binary codes are inferred for documents unrelated to the training corpus. We show that binary paragraph vectors outperform autoencoder-based binary codes, despite using fewer bits. Inspired by this work, we present Binary Paragraph Vector models: simple neural networks that learn short binary codes for fast information retrieval. Then we will have the exactly same results of word vector and paragraph vector if we feed into the same data.Abstract Recently Le & Mikolov described two log-linear models, called Paragraph Vector, that can be used to learn state-of-the-art distributed representations of documents. To summarize, the following is what we need to do to exclude randomness thoroughly:Ģ. But notice that single thread will tremendously slow down the training. Hence, if we set the number of worker as 1, it will run in single thread and have the exact same order of feeding data in each time of training. LimitUpper = workers * batchSize * 2 // threads get data from the queue through Val sequencer = new AsyncSequencer(erator, this.stopWords) // each threads are pointing to the same sequencer initialize a sequencer to provide data to threads Since every threads can come randomly, in every time of training, each thread can get different sequences to train. This LinkedBlockingQueue gets sequences from the iterator of training text, and provides these sequences to each threads.
syn0 = Nd4j.rand(new int Queue that provides sequences to every thread to train Here is the place where the seed takes effect. Hence, if we set seed as 0, we will get exact same initialization every time. We know that before training, the weights of model and vector representation will be initialized randomly, and the randomness is controlled by seed. Where the randomness comes The initialization of weights and matrix If you want to take look on the other package, go to gensim’s doc2vec, which has the same method of implementation. I will use DL4j’s implementation of paragraph vectors to show the code.
#Paragraph vector code how to#
Code can talk itself, let’s take a look on where the randomness comes and how to eliminate it thoroughly.
This is because of the randomness introduced in the training time. If you got any chance to train word vectors yourself, you may find that the model and the vector representation is different across every training even you feed into same training data.
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