Weakly supervised POS tagging without disambiguation

Weakly supervised part-of-speech (POS) tagging is to learn to predict the POS tag for a given word in context by making use of partial annotated data instead of the fully tagged corpora. Weakly supervised POS tagging would benefit various natural language processing applications in such languages where tagged corpora are mostly unavailable. In this article, we propose a novel framework for weakly supervised POS tagging based on a dictionary of words with their possible POS tags. In the constrained error-correcting output codes (ECOC)-based approach, a unique L-bit vector is assigned to each POS tag. The set of bitvectors is referred to as a coding matrix with value { 1, -1}. Each column of the coding matrix specifies a dichotomy over the tag space to learn a binary classifier. For each binary classifier, its training data is generated in the following way: each pair of words and its possible POS tags are considered as a positive training example only if the whole set of its possible tags falls into the positive dichotomy specified by the column coding and similarly for negative training examples. Given a word in context, its POS tag is predicted by concatenating the predictive outputs of the L binary classifiers and choosing the tag with the closest distance according to some measure. By incorporating the ECOC strategy, the set of all possible tags for each word is treated as an entirety without the need of performing disambiguation. Moreover, instead of manual feature engineering employed in most previous POS tagging approaches, features for training and testing in the proposed framework are automatically generated using neural language modeling. The proposed framework has been evaluated on three corpora for English, Italian, and Malagasy POS tagging, achieving accuracies of 93.21%, 90.9%, and 84.5% individually, which shows a significant improvement compared to the state-of-the-art approaches

Interpretable relevant emotion ranking with event-driven attention

Multiple emotions with different intensities are often evoked by events described in documents. Oftentimes, such event information is hidden and needs to be discovered from texts. Unveiling the hidden event information can help to understand how the emotions are evoked and provide explainable results. However, existing studies often ignore the latent event information. In this paper, we proposed a novel interpretable relevant emotion ranking model with the event information incorporated into a deep learning architecture using the event-driven attentions. Moreover, corpus-level event embeddings and document-level event distributions are introduced respectively to consider the global events in corpus and the document-specific events simultaneously. Experimental results on three real-world corpora show that the proposed approach performs remarkably better than the state-of-the-art emotion detection approaches and multi-label approaches. Moreover, interpretable results can be obtained to shed light on the events which trigger certain emotions

The transcriptional response of Arcobacter butzleri to cold shock

Arcobacter (A.) butzleri is an emerging zoonotic pathogen associated with gastrointestinal diseases, such as abdominal cramps and diarrhea, and is widely detected in animals, showing a high prevalence in poultry and seafood. The survival and adaptation of A. butzleri to cold temperatures remains poorly studied, although it might be of interest for food safety considerations. To address this, growth patterns of eight A. butzleri isolates were determined at 8 °C for 28 days. A. butzleri isolates showed strain‐dependent behavior: six isolates were unculturable after day 18, one exhibited declining but detectable cell counts until day 28 and one grew to the stationary phase level. Out of 13 A. butzleri cold shock‐related genes homologous to Escherichia coli, 10 were up‐regulated in response to a temperature downshift to 8 °C, as demonstrated by reverse transcription‐quantitative PCR. Additionally, we compared these data with the cold‐shock response in E. coli. Overall, we provide a deeper insight into the environmental adaptation capacities of A. butzleri, which we find shares similarities with the E. coli cold‐shock response

The Relationship between Serum Osteocalcin Concentration and Glucose Metabolism in Patients with Type 2 Diabetes Mellitus

To study the correlations between serum osteocalcin and glucose metabolism in patients with type 2 diabetes, 66 cases were collected to determine total osteocalcin, undercarboxylated osteocalcin, fasting blood glucose, fasting insulin, and HbA1c. Osteocalcin concentrations were compared between groups of different levels of HbA1c, and parameters of glucose metabolism were compared between groups of different levels of total osteocalcin and undercarboxylated osteocalcin. The relationship between osteocalcin and parameters of glucose metabolism was also analyzed. We found that the total osteocalcin concentration of high-HbA1c group was significantly lower than that of low-HbA1c group. The fasting blood glucose of low-total-osteocalcin group was significantly higher than that of high-total-osteocalcin group in male participants, while the fasting blood glucose of low-undercarboxylated-osteocalcin group was significantly higher than that of high-undercarboxylated-osteocalcin group in all participants and in male participants. Total osteocalcin was inversely correlated with HbA1c, and undercarboxylated osteocalcin was inversely correlated with fasting blood glucose. However, no significant correlation was found between osteocalcin and HOMA-IR. Total osteocalcin was an independent related factor of HbA1c level. In summary, decreased serum total osteocalcin and undercarboxylated osteocalcin are closely related to the exacerbation of glucose metabolism disorder but have no relations with insulin resistance

Salmonella Infection on Arabidopsis Seedlings Requires Both Host and Pathogen Factors

Human enteric pathogen Salmonella contaminates raw produce and triggers significant economic loss and illness. Under a natural environment, Salmonella resides in soil and enters the interior of plants without causing disease or eliciting symbiotic growth. Upon being consumed by humans, complex virulence mechanisms are elicited by the specific intestine conditions, such as high temperature and humidity and lead to profound infection. The lack of effective prevention and drug treatment are largely attributed to the unclear mechanistic understanding on Salmonella association with environmental media, and in vivo host and pathogen factors required for persistent infection. We have explored the potential of deploying the model plant organism Arabidopsis thaliana to tackle this fundamental yet clinically challenging question, as Arabidopsis possesses many advantages as a model system, including enriched genomic resources, powerful genetic tools, low maintenance cost and a large collection of individual gene deletion mutants. Our preliminary data demonstrated Arabidopsis seedlings under liquid culture conditions mimicking the intestine environment were infected and killed by salmonella within 2 days upon inoculation. The Arabidopsis system possesses well-developed genetic information and the resources to study host factors required for infection on very short time scales, thus complementing traditional animal genetic studies. We aim to define the pathogen factors required for this infection. By merging the fields of extremely powerful Arabidopsis genetics and bacterial genetics/genomics, we hope to provide insight into possible new paradigms for addressing salmonella-mediated food born infection

Hidden topic–emotion transition model for multi-level social emotion detection

With the fast development of online social platforms, social emotion detection, focusing on predicting readers’ emotions evoked by news articles, has been intensively investigated. Considering emotions as latent variables, various probabilistic graphical models have been proposed for emotion detection. However, the bag-of-words assumption prohibits those models from capturing the inter-relations between sentences in a document. Moreover, existing models can only detect emotions at either the document-level or the sentence-level. In this paper, we propose an effective Bayesian model, called hidden Topic–Emotion Transition model, by assuming that words in the same sentence share the same emotion and topic and modeling the emotions and topics in successive sentences as a Markov chain. By doing so, not only the document-level emotion but also the sentence-level emotion can be detected simultaneously. Experimental results on the two public corpora show that the proposed model outperforms state-of-the-art approaches on both document-level and sentence-level emotion detection