Information retrieval in the workplace: A comparison of professional search practices

Legal researchers, recruitment professionals, healthcare information professionals, and patent analysts all undertake work tasks where search forms a core part of their duties. In these instances, the search task is often complex and time-consuming and requires specialist expertise to identify relevant documents and insights within large domain-specific repositories and collections. Several studies have been made investigating the search practices of professionals such as these, but few have attempted to directly compare their professional practices and so it remains unclear to what extent insights and approaches from one domain can be applied to another. In this paper we describe the results of a survey of a purposive sample of 108 legal researchers, 64 recruitment professionals and 107 healthcare information professionals. Their responses are compared with results from a previous survey of 81 patent analysts. The survey investigated their search practices and preferences, the types of functionality they value, and their requirements for future information retrieval systems. The results reveal that these professions share many fundamental needs and face similar challenges. In particular a continuing preference to formulate queries as Boolean expressions, the need to manage, organise and re-use search strategies and results and an ambivalence toward the use of relevance ranking. The results stress the importance of recall and coverage for the healthcare and patent professionals, while precision and recency were more important to the legal and recruitment professionals. The results also highlight the need to ensure that search systems give confidence to the professional searcher and so trust, explainability and accountability remains a significant challenge when developing such systems. The findings suggest that translational research between the different areas could benefit professionals across domains.     

Reliable automatic word spacing using a space insertion and correction model based on neural networks in Korean

Automatic word spacing in Korean remains a significant task in natural language processing owing to the extremely complex word spacing rules involved. Most previous models remove all spaces in input sentences and insert new spaces in the modified input sentences. If input sentences include only a small number of spacing errors, the previous models often return sentences with even more spacing errors than the input sentences because they remove the correct spaces that were typed intentionally by the users. To reduce this problem, we propose an automatic word spacing model based on a neural network that effectively uses word spacing information from input sentences. The proposed model comprises a space insertion layer and a spacing-error correction layer. Using an approach similar to previous models, the space insertion layer inserts word spaces into input sentences from which all spaces have been removed. The spacing error correction layer post-corrects the spacing errors of the space insertion model using word spacing typed by users. Because the two layers are tightly connected in the proposed model, the backpropagation flows are not blocked. As a result, the space insertion and error correction are performed simultaneously. In experiments, the proposed model outperformed all compared models on all measures on the same test data. In addition, it exhibited reliable performance (word-unit F1-measures of 94.17%～97.87%) regardless of how many word spacing errors were present in the input sentences.     

TEXUS: A unified framework for extracting and understanding tables in PDF documents

Tables in documents are a widely-available and rich source of information, but not yet well-utilised computationally because of the difficulty in automatically extracting their structure and data content. There has been a plethora of systems proposed to solve the problem, but current methods present low usability and accuracy and lack precision in detecting data from diverse layouts. We propose a component-based design and implementation of table processing concepts which can offer flexibility and re-usability as well as high performance on a wide range of table types. In this paper, we describe a system named TEXUS which is a fully automated table processing system that takes a PDF document and detects tables in a layout independent manner. We introduce TEXUS’s own table processing specific document model and the two-phased processing pipeline design. Through an extensive evaluation on a dataset comprised of complex financial tables, we show the performance of the system on different table types.     

How narratives move your mind: A corpus of shared-character stories for connecting emotional flow and interestingness

Creativity is considered a human characteristic; creative endeavors, including automatic story generation, have been a major challenge for artificial intelligences. To understand how humans create and evaluate stories, we (1) construct a story dataset and (2) analyze the relationship between emotions and story interestingness. Given that understanding how to move readers emotionally is a crucial creative technique, we focus on the role of emotions in evaluating reader satisfaction. Although conventional research has highlighted emotions read from a text, we hypothesize that readers’ emotions do not necessarily coincide with those of the characters. The story dataset created for this study describes situations surrounding two characters. Crowdsourced volunteers label stories with the emotions of the two characters and those of readers; we then empirically analyze the relationship between emotions and interestingness. The results show that a story’s score has a stronger relationship to the readers’ emotions than the characters’ emotions.     

Real-time processing of social media with SENTINEL: A syndromic surveillance system incorporating deep learning for health classification

Interest in real-time syndromic surveillance based on social media data has greatly increased in recent years. The ability to detect disease outbreaks earlier than traditional methods would be highly useful for public health officials. This paper describes a software system which is built upon recent developments in machine learning and data processing to achieve this goal. The system is built from reusable modules integrated into data processing pipelines that are easily deployable and configurable. It applies deep learning to the problem of classifying health-related tweets and is able to do so with high accuracy. It has the capability to detect illness outbreaks from Twitter data and then to build up and display information about these outbreaks, including relevant news articles, to provide situational awareness. It also provides nowcasting functionality of current disease levels from previous clinical data combined with Twitter data.The preliminary results are promising, with the system being able to detect outbreaks of influenza-like illness symptoms which could then be confirmed by existing official sources. The Nowcasting module shows that using social media data can improve prediction for multiple diseases over simply using traditional data sources.