Fake News Detection Through Multi-Perspective Speaker Profiles

Automatic fake news detection is an important, yet very challenging topic. Traditional methods using lexical features have only very limited success. This paper proposes a novel method to incorporate speaker profiles into an attention based LSTM model for fake news detection. Speaker profiles contribute to the model in two ways. One is to include them in the attention model. The other includes them as additional input data. By adding speaker profiles such as party affiliation, speaker title, location and credit history, our model outperforms the state-of-the-art method by 14.5% in accuracy using a benchmark fake news detection dataset. This proves that speaker profiles provide valuable information to validate the credibility of news articles

Evaluation of pulmonary sequestration with multidetector computed tomography angiography in a select cohort of patients: A retrospective study

OBJECTIVES: This study aimed to evaluate the role of multidetector computed tomography angiography in diagnosing patients with pulmonary sequestration. METHODS: We retrospectively analyzed the computed tomography studies and clinical materials of 43 patients who had undergone preoperative multidetector computed tomography angiography in our hospital and had pathologically proven pulmonary sequestration. Each examination of pulmonary sequestration was reviewed for type, location, parenchymal changes, arterial supply and venous drainage on two-dimensional and three-dimensional computed tomography images. RESULTS: Multidetector computed tomography successfully detected all pulmonary sequestrations in the 43 patients (100%). This included 40 patients (93.0%) with intralobar sequestration and 3 patients (7.0%) with extralobar sequestration. The locations of pulmonary sequestration were left lower lobe (28 cases, 70% of intralobar sequestrations), right lower lobe (12 cases, 30% of intralobar sequestrations) and costodiaphragmatic sulcus (3 cases). Cases of sequestered lung presented as mass lesions (37.2%), cystic lesions (32.6%), pneumonic lesions (16.3%), cavitary lesions (9.3%) and bronchiectasis (4.6%). The angioarchitecture of pulmonary sequestration, including feeding arteries from the thoracic aorta (86.1%), celiac truck (9.3%), abdominal aorta (2.3%) and left gastric artery (2.3%) and venous drainage into inferior pulmonary veins (86.0%) and the azygos vein system (14.0%), was visualized on multidetector computed tomography. Finally, the multidetector computed tomography angiography results of the sequestered lungs and angioarchitectures were surgically confirmed in all the patients. CONCLUSIONS: As a noninvasive modality, multidetector computed tomography angiography is helpful for making diagnostic decisions regarding pulmonary sequestration with high confidence and for visualizing the related parenchymal characteristics, arterial supply, and venous drainage features to help plan surgical strategies

Synaesthesia in Chinese: A corpus-based study on gustatory adjectives in Mandarin

This study adopted a corpus-based approach to examine the synaesthetic metaphors of gustatory adjectives in Mandarin. Based on the distribution of synaesthetic uses in the corpus, we found that: (1) the synaesthetic metaphors of Mandarin gustatory adjectives exhibited directionality; (2) the directionality of Mandarin synaesthetic gustatory adjectives showed both commonality and specificity when compared with the attested directionality of gustatory adjectives in English, which calls for a closer re-examination of the claim of cross-lingual universality of synaesthetic tendencies; and (3) the distribution and directionality of Mandarin synaesthetic gustatory adjectives could not be predicted by a single hypothesis, such as the embodiment-driven approach or the biological association-driven approach. Thus, linguistic synaesthesia was constrained by both the embodiment principle and the biological association mechanism

Inferring Affective Meanings of Words from Word Embedding

Affective lexicon is one of the most important resource in affective computing for text. Manually constructed affective lexicons have limited scale and thus only have limited use in practical systems. In this work, we propose a regression-based method to automatically infer multi-dimensional affective representation of words via their word embedding based on a set of seed words. This method can make use of the rich semantic meanings obtained from word embedding to extract meanings in some specific semantic space. This is based on the assumption that different features in word embedding contribute differently to a particular affective dimension and a particular feature in word embedding contributes differently to different affective dimensions. Evaluation on various affective lexicons shows that our method outperforms the state-of-the-art methods on all the lexicons under different evaluation metrics with large margins. We also explore different regression models and conclude that the Ridge regression model, the Bayesian Ridge regression model and Support Vector Regression with linear kernel are the most suitable models. Comparing to other state-of-the-art methods, our method also has computation advantage. Experiments on a sentiment analysis task show that the lexicons extended by our method achieve better results than publicly available sentiment lexicons on eight sentiment corpora. The extended lexicons are publicly available for access

Improving attention model based on cognition grounded data for sentiment analysis

Attention models are proposed in sentiment analysis and other classification tasks because some words are more important than others to train the attention models. However, most existing methods either use local context based information, affective lexicons, or user preference information. In this work, we propose a novel attention model trained by cognition grounded eye-tracking data. First,a reading prediction model is built using eye-tracking data as dependent data and other features in the context as independent data. The predicted reading time is then used to build a cognition grounded attention layer for neural sentiment analysis. Our model can capture attentions in context both in terms of words at sentence level as well as sentences at document level. Other attention mechanisms can also be incorporated together to capture other aspects of attentions, such as local attention, and affective lexicons. Results of our work include two parts. The first part compares our proposed cognition ground attention model with other state-of-the-art sentiment analysis models. The second part compares our model with an attention model based on other lexicon based sentiment resources. Evaluations show that sentiment analysis using cognition grounded attention model outperforms the state-of-the-art sentiment analysis methods significantly. Comparisons to affective lexicons also indicate that using cognition grounded eye-tracking data has advantages over other sentiment resources by considering both word information and context information. This work brings insight to how cognition grounded data can be integrated into natural language processing (NLP) tasks