Open domain targeted sentiment is the joint information extraction task that finds target mentions together with the sentiment towards each mention from a text corpus. The task is typically modeled as a sequence labeling problem, and solved using state-of-the-art labelers such as CRF. We empirically study the effect of word embeddings and automatic feature combinations on the task by extending a CRF baseline using neural networks, which have demonstrated large potentials for sentiment analysis. Results show that the neural model can give better results by significantly increasing the recall. In addition, we propose a novel integration of neural and discrete features, which combines their relative advantages, leading to significantly higher results compared to both baselines.

Background: Extracting biomedical entities and their relations from text has important applications on biomedical research. Previous work primarily utilized feature-based pipeline models to process this task. Many efforts need to be made on feature engineering when feature-based models are employed. Moreover, pipeline models may suffer error propagation and are not able to utilize the interactions between subtasks. Therefore, we propose a neural joint model to extract biomedical entities as well as their relations simultaneously, and it can alleviate the problems above. Results: Our model was evaluated on two tasks, i.e., the task of extracting adverse drug events between drug and disease entities, and the task of extracting resident relations between bacteria and location entities. Compared with the state-of-the-art systems in these tasks, our model improved the F1 scores of the first task by 5.1% in entity recognition and 8.0% in relation extraction, and that of the second task by 9.2% in relation extraction. Conclusions:The proposed model achieves competitive performances with less work on feature engineering. We demonstrate that the model based on neural networks is effective for biomedical entity and relation extraction. In addition, parameter sharing is an alternative method for neural models to jointly process this task. Our work can facilitate the research on biomedical text mining.

Neural networks have shown promising results for relation extraction. State-ofthe-art models cast the task as an end-toend problem, solved incrementally using a local classifier. Yet previous work using statistical models have demonstrated that global optimization can achieve better performances compared to local classification. We build a globally optimized neural model for end-to-end relation extraction, proposing novel LSTM features in order to better learn context representations. In addition, we present a novel method to integrate syntactic information to facilitate global learning, yet requiring little background on syntactic grammars thus being easy to extend. Experimental results show that our proposed model is highly effective, achieving the best performances on two standard benchmarks.

The large and complex gut microbiota in animals has profound effects on feed utilization and metabolism. Currently, gastrointestinal diseases due to dysregulated gut microbiota are considered important factors that limit growth of the captive forest musk deer population. Compared with captive forest musk deer, wild forest musk deer have a wider feeding range with no dietary limitations, and their gut microbiota are in a relatively natural state. However, no reports have compared the gut microbiota between wild and captive forest musk deer. To gain insight into the composition of gut microbiota in forest musk deer under different food-source conditions, we employed high-throughput 16S rRNA sequencing technology to investigate differences in the gut microbiota occurring between captive and wild forest musk deer. Both captive and wild forest musk deer showed similar microbiota at the phylum level, which consisted mainly of Firmicutes and Bacteroidetes, although significant differences were found in their relative abundances between both groups. α-Diversity results showed that no significant differences occurred in the microbiota between both groups, while β-diversity results showed that significant differences did occur in their microbiota compositions. In summary, our results provide important information for improving feed preparation for captive forest musk deer and implementing projects where captive forest musk deer are released into the wild.

Character-based and word-based methods are two main types of statistical models for Chinese word segmentation, the former exploiting sequence labeling models over characters and the latter typically exploiting a transition-based model, with the advantages that word-level features can be easily utilized. Neural models have been exploited for character-based Chinese word segmentation, giving high accuracies by making use of external character embeddings, yet requiring less feature engineering. In this paper, we study a neural model for word-based Chinese word segmentation, by replacing the manuallydesigned discrete features with neural features in a word-based segmentation framework. Experimental results demonstrate that word features lead to comparable performances to the best systems in the literature, and a further combination of discrete and neural features gives top accuracies.