The deployment of Web 2.0 technologies has led to rapid growth of various opinions and reviews on the web, such as reviews on products and opinions about people. Such content can be very useful to help people find interesting entities like products, businesses and people based on their individual preferences or tradeoffs. Most existing work on leveraging opinionated content has focused on integrating and summarizing opinions on entities to help users better digest all the opinions. In this paper, we propose a different way of leveraging opinionated content, by directly ranking entities based on a user's preferences. Our idea is to represent each entity with the text of all the reviews of that entity. Given a user's keyword query that expresses the desired features of an entity, we can then rank all the candidate entities based on how well opinions on these entities match the user's preferences. We study several methods for solving this problem, including both standard text retrieval models and some extensions of these models. Experiment results on ranking entities based on opinions in two different domains (hotels and cars) show that the proposed extensions are effective and lead to improvement of ranking accuracy over the standard text retrieval models for this task.
Segmentation of clinical texts is critical for all sorts of tasks such as medical coding for billing, auto drafting of discharge summaries, patient problem list generation and many such applications. While there have been previous studies on using supervised approaches to segmentation of clinical texts, these existing approaches were trained and tested on a fairly limited data set showing low adaptibility to new unseen documents. We propose a highly generalized supervised model for segmenting clinical texts, based on a set of line-wise predictions by a classifier with constraints imposing their coherence. Evaluation results on 5 independent test sets show that our approach can work on all sorts of note types and performs consistently across enterprises.
While storytelling has long been recognized as an important part of effective knowledge management in organizations, knowledge management technologies have generally not distinguished between stories and other types of discourse. In this paper we describe a new type of technological support for storytelling that involves automatically capturing the stories that people tell to each other in conversations. We describe our first attempt at constructing an automated story extraction system using statistical text classification and a simple voting scheme. We evaluate the performance of this system and demonstrate that useful levels of precision and recall can be obtained when analyzing transcripts of interviews, but that performance on speech recognition data is not above what can be expected by chance. This paper establishes the level of performance that can be obtained using a straightforward approach to story extraction, and outlines ways in which future systems can improve on these results and enable a wide range of knowledge socialization applications. Categories and Subject Descriptors I.2.7 Natural Language Processing General TermsAlgorithms. KeywordsStorytelling, Knowledge Management. KNOWLEDGE SOCIALIZATIONMuch of the knowledge that is shared among members of communities and organizations is exhibited only in the telling of stories in spoken conversations. While support for storytelling in organizations has been long recognized as important to effective knowledge management [2][12], few attempts have been made to specifically support storytelling through technology. Instead of creating specific technologies for automatically capturing, analyzing, and routing stories that are naturally told in conversations, knowledge management technology development has targeted the more general problem of supporting computer-mediated communication, without much regard to the genre of the content [9]. In not distinguishing between storytelling and other types of human-human communication, today's knowledge management technologies fail to exploit the value of stories in packaging and transmitting tacit knowledge [13], understanding organizational change [10], and driving the development of professional training applications [5]. Furthermore, knowledge management technologies have had difficulty breaking out of the mold of traditional networked groupware applications, which limits their applicability to the fraction of people who spend their days working at computer terminals. A different vision for knowledge management technology is one that is specifically targeted to the capture and use of the stories told in communities and organizations in the context of normal, spoken conversations. The role of technology would be to support the capture of stories from spoken conversations, perform a task-directed analysis of its content, and present stories or analysis of stories to people in service of their organizational tasks. As a hypothetical example, consider the utility of story management technology for militaries with...
In this paper we describe our mining system which automatically mines tags from feedback text in an eCommerce scenario. It renders these tags in a visually appealing manner. Further, emoticons are attached to mined tags to add sentiment to the visual aspect.
The ability to find highly related clinical concepts is essential for many applications such as for hypothesis generation, query expansion for medical literature search, search results filtering, ICD-10 code filtering and many other applications. While manually constructed medical terminologies such as SNOMED CT can surface certain related concepts, these terminologies are inadequate as they depend on expertise of several subject matter experts making the terminology curation process open to geographic and language bias. In addition, these terminologies also provide no quantifiable evidence on how related the concepts are. In this work, we explore an unsupervised graphical approach to mine related concepts by leveraging the volume within large amounts of clinical notes. Our evaluation shows that we are able to use a data driven approach to discovering highly related concepts for various search terms including medications, symptoms and diseases.
This paper presents a new unsupervised approach to generating ultra-concise summaries of opinions. We formulate the problem of generating such a micropinion summary as an optimization problem, where we seek a set of concise and non-redundant phrases that are readable and represent key opinions in text. We measure representativeness based on a modified mutual information function and model readability with an n-gram language model. We propose some heuristic algorithms to efficiently solve this optimization problem. Evaluation results show that our unsupervised approach outperforms other state of the art summarization methods and the generated summaries are informative and readable.
Traditional sentiment analysis has been focused on predicting the polarity of texts as positive or negative at different granularity. This broad categorization does not account for informativeness of the underlying text. For many real-world applications such as social listening, brand monitoring and e-commerce platforms, the opinions that really matter are the informative opinions describing why something is good or bad. In this paper, we try to understand the properties of complaints and praises which is an informative subset of the negative and positive categories. Our analysis in the context of user reviews shows that complaints and praises have distinct properties that differentiate it from positive only or negative only sentences.
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