Real-time Query Expansion in Relevance Models

Automatic query expansion is well known as a technique that improves query effectiveness on average. Unfortunately, it is usually very slow, increasing the time to process a query by 20 times or more. In this study, we use relevance models to show how the process can be made almost as fast as running a non-expanded query. 1

Sentiment Retrieval using Generative Models

Ranking documents or sentences according to both topic and sentiment relevance should serve a critical function in helping users when topics and sentiment polarities of the targeted text are not explicitly given, as is often the case on the web. In this paper, we propose several sentiment information retrieval models in the framework of probabilistic language models, assuming that a user both inputs query terms expressing a certain topic and also specifies a sentiment polarity of interest in some manner. We combine sentiment relevance models and topic relevance models with model parameters estimated from training data, considering the topic dependence of the sentiment. Our experiments prove that our models are effective.

Streaming first story detection with application to Twitter

With the recent rise in popularity and size of social media, there is a growing need for systems that can extract useful information from this amount of data. We address the problem of detecting new events from a stream of Twitter posts. To make event detection feasible on web-scale corpora, we present an algorithm based on locality-sensitive hashing which is able overcome the limitations of traditional approaches, while maintaining competitive results. In particular, a comparison with a stateof-the-art system on the first story detection task shows that we achieve over an order of magnitude speedup in processing time, while retaining comparable performance. Event detection experiments on a collection of 160 million Twitter posts show that celebrity deaths are the fastest spreading news on Twitter.

Query-by-Example Image Retrieval using Visual Dependency Representations

Image retrieval models typically represent images as bags-of-terms, a representation that is well-suited to matching images based on the presence or absence of terms. For some information needs, such as searching for images of people performing actions, it may be useful to retain data about how parts of an image relate to each other. If the underlying representation of an image can distinguish between images where objects only co-occur from images where people are in-teracting with objects, then it should be possible to improve retrieval performance. In this paper we model the spatial relationships between image regions using Visual Dependency Represen-tations, a structured image representation that makes it possible to distinguish between object co-occurrence and interaction. In a query-by-example image retrieval experiment on data set of people performing actions, we find an 8.8 % relative increase in MAP and an 8.6 % relative increase in Precision@10 when images are represented using the Visual Dependency Represen-tation compared to a bag-of-terms baseline.

The BladeMistress Corpus: From Talk to Action in Virtual Worlds

htmlabstractVirtual Worlds (VW) are online environments where people come together to interact and perform various tasks. The chat transcripts of interactions in VWs pose unique opportunities and challenges for language analysis: Firstly, the language of the transcripts is very brief, informal, and task-oriented. Secondly, in addition to chat, a VW system records users’ in-world activities. Such a record could allow us to analyze how the language of interactions is linked to the users actions. For example, we can make the language analysis of the users dialogues more effective by taking into account the context of the corresponding action or we can predict or detect users actions by analyzing the content of conversations. Thirdly, a joined analysis of both the language and the actions would empower us to build effective modes of the users and their behavior. In this paper we present a corpus constructed from logs from an online multiplayer game BladeMistress. We describe the original logs, annotations that we created on the data, and summarize some of the experiments

A Factored Relevance Model for Contextual Point-of-Interest Recommendation

The challenge of providing personalized and contextually appropriate recommendations to a user is faced in a range of use-cases, e.g., recommendations for movies, places to visit, articles to read etc. In this paper, we focus on one such application, namely that of suggesting 'points of interest' (POIs) to a user given her current location, by leveraging relevant information from her past preferences. An automated contextual recommendation algorithm is likely to work well if it can extract information from the preference history of a user (exploitation) and effectively combine it with information from the user's current context (exploration) to predict an item's 'usefulness' in the new context. To balance this trade-off between exploration and exploitation, we propose a generic unsupervised framework involving a factored relevance model (FRLM), comprising two distinct components, one corresponding to the historical information from past contexts, and the other pertaining to the information from the local context. Our experiments are conducted on the TREC contextual suggestion (TREC-CS) 2016 dataset. The results of our experiments demonstrate the effectiveness of our proposed approach in comparison to a number of standard IR and recommender-based baselines

Etiology and efficacy of anti-microbial treatment for community-acquired pneumonia in adults requiring hospital admission in Ukraine

Background and aim: Empiric therapy of community-acquired pneumonia (CAP) remains the standard care and guidelines are mostly based on published data from the United States or Europe. In this study, we determined the bacterial etiology of CAP and evaluated the clinical outcomes under antimicrobial treatment of CAP in Ukraine. Methods: A total of 98 adult subjects with CAP and PORT risk II-IV were recruited for the study. The sputum diagnostic samples were obtained from all patients for causative pathogen identification. Subjects were randomly assigned in a 1:1 ratio to receive delafloxacin 300 mg (n=51) or moxifloxacin 400 mg (n=47) with a blinding placebo. The switch to oral treatment was after a minimum of 6 IV doses according to clinical criteria. The total duration of antibacterial treatment was 5-10 days. In vitro susceptibility of pathogens to delafloxacin and other comparator antibiotics was determined. Results: The most frequently isolated pathogens in adults with CAP were S. pneumoniae – 19.5%, M. pneumoniae – 15.3%, H. influenzae – 13.2%, S. aureus – 10.5%, K. pneumoniae – 10.1%, and H. parainfluenzae – 6.4%. All isolates of S. pneumoniae, S. aureus, M. pneumoniae had sufficient susceptibility to appropriate antibiotics. 9.0% of H. influenzae strains were susceptible to azithromycin. 94.8 % of patients had a successful clinical response to delafloxacin at the end of treatment and 93.9 % – at test-of-cure. Conclusions: In Ukraine, the major bacterial agents that induced CAP in adults were S. pneumoniae, M. pneumoniae, H. influenzae, S. aureus, K. pneumoniae, H. parainfluenzae, E. cloacae, L. pneumophila. Delafloxacin is a promising effective antibiotic for monotherapy of CAP in adults and could be used in cases of antimicrobial-resistant strains. (www.actabiomedica.it