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Machine learning methods, especially deep learning models, have achieved impressive performance in various natural language processing tasks including sentiment analysis. However, deep learning models are more demanding for training data. Data augmentation techniques are widely used to generate new instances based on modifications to existing data or relying on external knowledge bases to address annotated data scarcity, which hinders the full potential of machine learning techniques. This paper presents our work using part‐of‐speech (POS) focused lexical substitution for data augmentation (PLSDA) to enhance the performance of machine learning algorithms in sentiment analysis. We exploit POS information to identify words to be replaced and investigate different augmentation strategies to find semantically related substitutions when generating new instances. The choice of POS tags as well as a variety of strategies such as semantic‐based substitution methods and sampling methods are discussed in detail. Performance evaluation focuses on the comparison between PLSDA and two previous lexical substitution‐based data augmentation methods, one of which is thesaurus‐based, and the other is lexicon manipulation based. Our approach is tested on five English sentiment analysis benchmarks: SST‐2, MR, IMDB, Twitter, and AirRecord. Hyperparameters such as the candidate similarity threshold and number of newly generated instances are optimized. Results show that six classifiers (SVM, LSTM, BiLSTM‐AT, bidirectional encoder representations from transformers [BERT], XLNet, and RoBERTa) trained with PLSDA achieve accuracy improvement of more than 0.6% comparing to two previous lexical substitution methods averaged on five benchmarks. Introducing POS constraint and well‐designed augmentation strategies can improve the reliability of lexical data augmentation methods. Consequently, PLSDA significantly improves the performance of sentiment analysis algorithms. [ABSTRACT FROM AUTHOR]

In recent years, cross‐modal retrieval has been a popular research topic in both fields of computer vision and natural language processing. There is a huge semantic gap between different modalities on account of heterogeneous properties. How to establish the correlation among different modality data faces enormous challenges. In this work, we propose a novel end‐to‐end framework named Dual Multi‐Angle Self‐Attention (DMASA) for cross‐modal retrieval. Multiple self‐attention mechanisms are applied to extract fine‐grained features for both images and texts from different angles. We then integrate coarse‐grained and fine‐grained features into a multimodal embedding space, in which the similarity degrees between images and texts can be directly compared. Moreover, we propose a special multistage training strategy, in which the preceding stage can provide a good initial value for the succeeding stage and make our framework work better. Very promising experimental results over the state‐of‐the‐art methods can be achieved on three benchmark datasets of Flickr8k, Flickr30k, and MSCOCO. [ABSTRACT FROM AUTHOR]

Information ordering is a nontrivial task in multi-document summarization (MDS), which typically relies on the traditional vector space model (VSM) notorious for semantic deficiency. In this article, we propose a novel event-enriched VSM to alleviate the problem by building event semantics into sentence representations. The mediation of event information between sentence and term, especially in the news domain, has an intuitive appeal as well as technical advantage in common sentence-level operations such as sentence similarity computation. Inspired by the block-style writing by humans, we base the sentence ordering algorithm on sentence clustering. To accommodate the complexity introduced by event information, we adopt a soft-to-hard clustering strategy on the event and sentence levels, using expectation-maximization clustering and K-means, respectively. For the purpose of cluster-based sentence ordering, the event-enriched VSM enables us to design an ordering algorithm to enhance event coherence computed between sentence and sentence-context pairs. Drawing on the findings of earlier research, we also incorporate topic continuity measures and time information into the scheme. We evaluate the performance of the model and its variants automatically and manually, with experimental results showing clear advantage of the event-based model over baseline and non-event-based models in information ordering for multi-document news summarization. We are confident that the event-enriched VSM has even greater potential in summarization and beyond, which awaits further research. © 2014 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]