Recently Le & Mikolov described two log-linear models, called Paragraph Vector, that can be used to learn state-ofthe-art distributed representations of documents. Inspired by this work, we present Binary Paragraph Vector models: simple neural networks that learn short binary codes for fast information retrieval. We show that binary paragraph vectors outperform autoencoder-based binary codes, d...

Representing texts as fixed-length vectors is central to many language processing tasks. Most traditional methods build text representations based on the simple Bag-of-Words (BoW) representation, which loses the rich semantic relations between words. Recent advances in natural language processing have shown that semantically meaningful representations of words can be efficiently acquired by dis...

this study investigated the comparative effect of teaching idiomatic expressions through practicing them in conversation and paragraph writing on intermediate efl learners’ idiom learning. the participants were sorted out of a population of 134 intermediate students in zabansara language school in khorramabad based on their scores on a preliminary english test (pet) and an idiom test piloted in...

research on reading comprehension supports the contribution of the topic sentence to better understanding of the paragraph by efl readers. however, the level of language proficiency, which has recently been recognized as an interacting factor with many language processing tasks, has not been taken into account in previous research. therefore, the prupose of this study is to investigate the rela...

Learning latent representations from long text sequences is an important first step in many natural language processing applications. Recurrent Neural Networks (RNNs) have become a cornerstone for this challenging task. However, the quality of sentences during RNN-based decoding (reconstruction) decreases with the length of the text. We propose a sequence-to-sequence, purely convolutional and d...

Word2vec (Mikolov et al., 2013b) has proven to be successful in natural language processing by capturing the semantic relationships between different words. Built on top of single-word embeddings, paragraph vectors (Le and Mikolov, 2014) find fixed-length representations for pieces of text with arbitrary lengths, such as documents, paragraphs, and sentences. In this work, we propose a novel int...

This study investigated the comparative effect of teaching idiomatic expressions through practicing them in conversation and paragraph writing on intermediate EFL learners’ idiom learning. The participants were sorted out of a population of 134 intermediate students in Zabansara Language School in Khorramabad based on their scores on a Preliminary English Test (PET) and an idiom test piloted in...

We present a method for partitioning expository texts into coherent multi-paragraph units which reeect the subtopic structure of the texts. Using Chafe's Flow Model of discourse, we observe that subtopics are often expressed by the interaction of multiple simultaneous themes. We describe two fully-implemented algorithms that use only term repetition information to determine the extents of the s...