All mixed up? Finding the optimal feature set for general readability prediction and its application to English and Dutch

Readability research has a long and rich tradition, but there has been too little focus on general readability prediction without targeting a specific audience or text genre. Moreover, though NLP-inspired research has focused on adding more complex readability features there is still no consensus on which features contribute most to the prediction. In this article, we investigate in close detail the feasibility of constructing a readability prediction system for English and Dutch generic text using supervised machine learning. Based on readability assessments by both experts and a crowd, we implement different types of text characteristics ranging from easy-to-compute superficial text characteristics to features requiring a deep linguistic processing, resulting in ten different feature groups. Both a regression and classification setup are investigated reflecting the two possible readability prediction tasks: scoring individual texts or comparing two texts. We show that going beyond correlation calculations for readability optimization using a wrapper-based genetic algorithm optimization approach is a promising task which provides considerable insights in which feature combinations contribute to the overall readability prediction. Since we also have gold standard information available for those features requiring deep processing we are able to investigate the true upper bound of our Dutch system. Interestingly, we will observe that the performance of our fully-automatic readability prediction pipeline is on par with the pipeline using golden deep syntactic and semantic information

Learning to Generate Posters of Scientific Papers

Researchers often summarize their work in the form of posters. Posters provide a coherent and efficient way to convey core ideas from scientific papers. Generating a good scientific poster, however, is a complex and time consuming cognitive task, since such posters need to be readable, informative, and visually aesthetic. In this paper, for the first time, we study the challenging problem of learning to generate posters from scientific papers. To this end, a data-driven framework, that utilizes graphical models, is proposed. Specifically, given content to display, the key elements of a good poster, including panel layout and attributes of each panel, are learned and inferred from data. Then, given inferred layout and attributes, composition of graphical elements within each panel is synthesized. To learn and validate our model, we collect and make public a Poster-Paper dataset, which consists of scientific papers and corresponding posters with exhaustively labelled panels and attributes. Qualitative and quantitative results indicate the effectiveness of our approach.Comment: in Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16), Phoenix, AZ, 201

A Framework to Categorize Shill and Normal Reviews by Measuring it’s Linguistic Features

Shill reviews detection has attracted significant attention from both business and research communities. Shill reviews are increasingly used to influence the reputation of products sold on websites in positive or negative manner. The spammers may create shill reviews which mislead readers to artificially promote or devalue some target products or services. Different methods which work according to linguistic features have been adopted and implemented effectively. Surprisingly, review manipulation was found on reputable e-commerce websites also. This is the reason why linguistic-feature based methods have gained more and more popularity. Lingual features of shill reviews are examined in this study and then a tool has been developed for extracting product features from the text used in the product review under analysis. Fake reviews, fake comments, fake blogs, fake social network postings and deceptive texts are some forms of shill reviews. By extracting linguistic features like informativeness, subjectivity and readability, an attempt is made to find difference between shill and normal reviews. On the basis of these three characteristics, hypotheses are formed and generalized. These hypotheses help to compare shill and normal reviews in analytical terms. Proposed work is for based on polarity of the text (positive or negative), as shill reviewer tend to use a definite polarity based on their intention, positive or negative

THE IDENTIFICATION OF NOTEWORTHY HOTEL REVIEWS FOR HOTEL MANAGEMENT

The rapid emergence of user-generated content (UGC) inspires knowledge sharing among Internet users. A good example is the well-known travel site TripAdvisor.com, which enables users to share their experiences and express their opinions on attractions, accommodations, restaurants, etc. The UGC about travel provide precious information to the users as well as staff in travel industry. In particular, how to identify reviews that are noteworthy for hotel management is critical to the success of hotels in the competitive travel industry. We have employed two hotel managers to conduct an examination on Taiwan’s hotel reviews in Tripadvisor.com and found that noteworthy reviews can be characterized by their content features, sentiments, and review qualities. Through the experiments using tripadvisor.com data, we find that all three types of features are important in identifying noteworthy hotel reviews. Specifically, content features are shown to have the most impact, followed by sentiments and review qualities. With respect to the various methods for representing content features, LDA method achieves comparable performance to TF-IDF method with higher recall and much fewer features

IRIT at INEX 2014 : Tweet Contextualization Track

National audienceThe paper presents IRIT's approach used at INEX Tweet Contextualization Track 2014. Systems had to provide a context to a tweet from the perspective of the entity. This year we further modified our approach presented at INEX 2011, 2012 and 2013 underlain by the product of different measures based on smoothing from local context, named entity recognition, part-ofspeech weighting and sentence quality analysis. We introduced two ways to link an entity and a tweet, namely (1) concatenation of the entity and the tweet and (2) usage of the results obtained for the entity as a restriction to filter results retrieved for the tweet. Besides, we examined the influence of topic-comment relationship on contextualization

The evolution of 10-K textual disclosure: Evidence from Latent Dirichlet Allocation

Abstract We document marked trends in 10-K disclosure over the period 1996–2013, with increases in length, boilerplate, stickiness, and redundancy and decreases in specificity, readability, and the relative amount of hard information. We use Latent Dirichlet Allocation (LDA) to examine specific topics and find that new FASB and SEC requirements explain most of the increase in length and that 3 of the 150 topics—fair value, internal controls, and risk factor disclosures—account for virtually all of the increase. These three disclosures also play a major role in explaining the trends in the remaining textual characteristics