Previous studies have shown that health reports in social media, such as DailyStrength and Twitter, have potential for monitoring health conditions (e.g. adverse drug reactions, infectious diseases) in particular communities. However, in order for a machine to understand and make inferences on these health conditions, the ability to recognise when laymen's terms refer to a particular medical concept (i.e. text normalisation) is required. To achieve this, we propose to adapt an existing phrase-based machine translation (MT) technique and a vector representation of words to map between a social media phrase and a medical concept. We evaluate our proposed approach using a collection of phrases from tweets related to adverse drug reactions. Our experimental results show that the combination of a phrase-based MT technique and the similarity between word vector representations outperforms the baselines that apply only either of them by up to 55 .
Traditional approaches for creating word vector representations treated words as atomic units @cite_7 @cite_9 . For instance, the one-hot representation used a vector with a length of the size of the vocabulary, where one dimension is on, to represent a particular word @cite_9 . Recently, techniques for learning high-quality word vector representations (i.e. distributed word representations) that could capture the semantic similarity between words, such as continuous bags of words (CBOW) @cite_7 and global vectors (GloVe) @cite_12 , have been proposed. Indeed, these distributed word representations have been effectively applied in different systems that achieve state-of-the-art performances for several NLP tasks, such as MT @cite_4 and named entity recognition @cite_13 . In this work, beside using word vector representations to measure the similarity between translated Twitter phrases and medical concepts, we use the similarity between word vector representations of the original Twitter phrase and a medical concept to augment the adapted phrase-based MT technique.
@cite_4 @cite_7 @cite_9 @cite_13 @cite_12
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Vector-based word representations can help to improve a document classifier.The information of word2vec vectors and bags of words are very complementary.The combination of word2vec and BOW word representations obtains the best results.Word2vec is much more stable than bag of words models in cross-domain experiments. In this paper we show how a vector-based word representation obtained via word2vec can help to improve the results of a document classifier based on bags of words. Both models allow obtaining numeric representations from texts, but they do it very differently. The bag of words model can represent documents by means of widely dispersed vectors in which the indices are words or groups of words. word2vec generates word level representations building vectors that are much more compact, where indices implicitly contain information about the context of word occurrences. Bags of words are very effective for document classification and in our experiments no representation using only word2vec vectors is able to improve their results. However, this does not mean that the information provided by word2vec is not useful for the classification task. When this information is used in combination with the bags of words, the results are improved, showing its complementarity and its contribution to the task. We have also performed cross-domain experiments in which word2vec has shown much more stable behavior than bag of words models. Continuous word representations, trained on large unlabeled corpora are useful for many natural language processing tasks. Popular models to learn such representations ignore the morphology of words, by assigning a distinct vector to each word. This is a limitation, especially for languages with large vocabularies and many rare words. In this paper, we propose a new approach based on the skipgram model, where each word is represented as a bag of character n-grams. A vector representation is associated to each character n-gram, words being represented as the sum of these representations. Our method is fast, allowing to train models on large corpora quickly and allows to compute word representations for words that did not appear in the training data. We evaluate our word representations on nine different languages, both on word similarity and analogy tasks. By comparing to recently proposed morphological word representations, we show that our vectors achieve state-of-the-art performance on these tasks. Vector-space word representations have been very successful in recent years at improving performance across a variety of NLP tasks. However, common to most existing work, words are regarded as independent entities without any explicit relationship among morphologically related words being modeled. As a result, rare and complex words are often poorly estimated, and all unknown words are represented in a rather crude way using only one or a few vectors. This paper addresses this shortcoming by proposing a novel model that is capable of building representations for morphologically complex words from their morphemes. We combine recursive neural networks (RNNs), where each morpheme is a basic unit, with neural language models (NLMs) to consider contextual information in learning morphologicallyaware word representations. Our learned models outperform existing word representations by a good margin on word similarity tasks across many datasets, including a new dataset we introduce focused on rare words to complement existing ones in an interesting way. Learning distributed word representations has been a popular method for various natural language processing applications such as word analogy and similarity, document classification and sentiment analysis. However, most existing word embedding models only exploit a shallow slide window as the context to predict the target word. Because the semantic of each word is also influenced by its global context, as the distributional models usually induced the word representations from the global co-occurrence matrix, the window-based models are insufficient to capture semantic knowledge. In this paper, we propose a novel hybrid model called mixed word embedding (MWE) based on the well-known word2vec toolbox. Specifically, the proposed MWE model combines the two variants of word2vec, i.e., SKIP-GRAM and CBOW, in a seamless way via sharing a common encoding structure, which is able to capture the syntax information of words more accurately. Furthermore, it incorporates a global text vector into the CBOW variant so as to capture more semantic information. Our MWE preserves the same time complexity as the SKIP-GRAM. To evaluate our MWE model efficiently and adaptively, we study our model on linguistic and application perspectives with both English and Chinese dataset. For linguistics, we conduct empirical studies on word analogies and similarities. The learned latent representations on both document classification and sentiment analysis are considered for application point of view of this work. The experimental results show that our MWE model is very competitive in all tasks as compared with the state-of-the-art word embedding models such as CBOW, SKIP-GRAM, and GloVe.