Named entity recognition (NER) is one fundamental task in natural language processing, which is typically addressed by neural condition random field (CRF) models, regarding the task as a sequence labeling problem. Sentence-level information has been shown positive for the task. Equipped with sophisticated neural structures such as long-short term memory network (LSTM), implicit sentence-level global information can be exploited fully, and has also been demonstrated effective in previous studies. In this work, we propose a new method for better learning of these sentence-level features in an explicit manner. Concretely, we suggest an auxiliary task, namely sentence-level named type prediction (i.e., determining whether a sentence includes a certain kind of named type), to supervise the feature representation learning globally. We conduct experiments on six benchmark datasets of various languages to evaluate our method. The results show that our final model is highly effective, resulting in significant improvements and leading to highly competitive results on all datasets.

Emojis are frequently used to express moods, emotions, and feelings in social media. There has been much research on emojis and sentiments. However, existing methods mainly face two limitations. First, they treat emojis as binary indicator features and rely on handcrafted features for emoji-based sentiment analysis. Second, they consider the sentiment of emojis and texts separately, not fully exploring the impact of emojis on the sentiment polarity of texts. In this article, we investigate a sentiment analysis model based on bidirectional long short-term memory, and the model has two advantages compared with the existing work. First, it does not need feature engineering. Second, it utilizes the attention approach to model the impact of emojis on text. An evaluation on 10,042 manually labeled Sina Weibo showed that our model achieves much better performance compared with several strong baselines. To facilitate the related research, our corpus will be publicly available at https://github.com/yx100/emoji.

MOTIVATION: Disease named entities play a central role in many areas of biomedical research, and automatic recognition and normalization of such entities have received increasing attention in biomedical research communities. Existing methods typically used pipeline models with two independent phases: (i) a disease named entity recognition (DER) system is used to find the boundaries of mentions in text and (ii) a disease named entity normalization (DEN) system is used to connect the mentions recognized to concepts in a controlled vocabulary. The main problems of such models are: (i) there is error propagation from DER to DEN and (ii) DEN is useful for DER, but pipeline models cannot utilize this. METHODS: We propose a transition-based model to jointly perform disease named entity recognition and normalization, casting the output construction process into an incremental state transition process, learning sequences of transition actions globally, which correspond to joint structural outputs. Beam search and online structured learning are used, with learning being designed to guide search. Compared with the only existing method for joint DEN and DER, our method allows non-local features to be used, which significantly improves the accuracies. RESULTS: We evaluate our model on two corpora: the BioCreative V Chemical Disease Relation (CDR) corpus and the NCBI disease corpus. Experiments show that our joint framework achieves significantly higher performances compared to competitive pipeline baselines. Our method compares favourably to other state-of-the-art approaches. AVAILABILITY AND IMPLEMENTATION: Data and code are available at https://github.com/louyinxia/jointRN. CONTACT: [email protected].

Extracting adverse drug events (a.k.a. drug-induced diseases or drug side effects) from the raw text has been widely studied in the biomedical area. It is usually assumed that entities of drugs and diseases are given by a separated named entity recognition model. In this paper, we propose a joint model to extract drugs, diseases and adverse drug events simultaneously. In our model, the structured perceptron is leveraged for training and multiple-beam search algorithm is used for decoding. The search algorithm is transition-based, i.e., an input sentence is processed in left-to-right order and predefined actions transit the sentence from one state to another which corresponds to a predicted result about drugs, diseases and adverse drug events in that sentence. Experimental results show that our joint approach obtains comparable performance compared with the baseline or state-of-the-art approaches and achieves 55.20% precision, 47.97% recall and 51.14% F1-measure in extraction of adverse drug events. We demonstrate that the joint approach is effective and can be easily extended to other entity-relation extraction systems such as protein-protein interactions and gene-disease relations. To facilitate the related research, our code is available online at: https://github.com/foxlf823/ade.

We propose a transition-based model for joint word segmentation, POS tagging and text normalization.Different from previous methods, the model can be trained on standard text corpora, overcoming the lack of annotated microblog corpora.To evaluate our model, we develop an annotated corpus based on microblogs.Experimental results show that our joint model can help improve the performance of word segmentation on microblogs, giving an error reduction in segmentation accuracy of 12.02%, compared to the traditional approach.