A corpus for studying full answer justification

International audienceQuestion answering (QA) systems aim at retrieving precise information from a large collection of documents. To be considered as reliable by users, a QA system must provide elements to evaluate the answer. This notion of answer justification can also be useful when developing a QA system in order to give criteria for selecting correct answers. An answer justification can be found in a sentence, a passage made of several consecutive sentences or several passages of a document or several documents. Thus, we are interested in pinpointing the set of information that allows verifying the correctness of the answer in a candidate passage and the question elements that are missing in this passage. Moreover, the relevant information is often given in texts in a different form from the question form : anaphora, paraphrases, synonyms. In order to have a better idea of the importance of all the phenomena we underlined, and to provide enough examples at the QA developer’s disposal to study them, we decided to build an annotated corpus

Sentiment analysis of health care tweets: review of the methods used.

BACKGROUND: Twitter is a microblogging service where users can send and read short 140-character messages called "tweets." There are several unstructured, free-text tweets relating to health care being shared on Twitter, which is becoming a popular area for health care research. Sentiment is a metric commonly used to investigate the positive or negative opinion within these messages. Exploring the methods used for sentiment analysis in Twitter health care research may allow us to better understand the options available for future research in this growing field. OBJECTIVE: The first objective of this study was to understand which tools would be available for sentiment analysis of Twitter health care research, by reviewing existing studies in this area and the methods they used. The second objective was to determine which method would work best in the health care settings, by analyzing how the methods were used to answer specific health care questions, their production, and how their accuracy was analyzed. METHODS: A review of the literature was conducted pertaining to Twitter and health care research, which used a quantitative method of sentiment analysis for the free-text messages (tweets). The study compared the types of tools used in each case and examined methods for tool production, tool training, and analysis of accuracy. RESULTS: A total of 12 papers studying the quantitative measurement of sentiment in the health care setting were found. More than half of these studies produced tools specifically for their research, 4 used open source tools available freely, and 2 used commercially available software. Moreover, 4 out of the 12 tools were trained using a smaller sample of the study's final data. The sentiment method was trained against, on an average, 0.45% (2816/627,024) of the total sample data. One of the 12 papers commented on the analysis of accuracy of the tool used. CONCLUSIONS: Multiple methods are used for sentiment analysis of tweets in the health care setting. These range from self-produced basic categorizations to more complex and expensive commercial software. The open source and commercial methods are developed on product reviews and generic social media messages. None of these methods have been extensively tested against a corpus of health care messages to check their accuracy. This study suggests that there is a need for an accurate and tested tool for sentiment analysis of tweets trained using a health care setting-specific corpus of manually annotated tweets first

Modelling Users, Intentions, and Structure in Spoken Dialog

We outline how utterances in dialogs can be interpreted using a partial first order logic. We exploit the capability of this logic to talk about the truth status of formulae to define a notion of coherence between utterances and explain how this coherence relation can serve for the construction of AND/OR trees that represent the segmentation of the dialog. In a BDI model we formalize basic assumptions about dialog and cooperative behaviour of participants. These assumptions provide a basis for inferring speech acts from coherence relations between utterances and attitudes of dialog participants. Speech acts prove to be useful for determining dialog segments defined on the notion of completing expectations of dialog participants. Finally, we sketch how explicit segmentation signalled by cue phrases and performatives is covered by our dialog model.Comment: 17 page

Review: Gender shifts in the history of English. Anne Curzan.Cambridge: Cambridge University Press, 2003.pp. 223 + xii.

Herbert Schendl (2001:9) defines ‘the study of ongoing changes in a language’ as one of the fundamental goals of historical linguistics. Curzan’s book, which examines the historical development of the English gender system, is a work noteworthy not only for historical linguists, but also for experts of gender and language precisely because it attains the aforementioned objective. The book not only gives a well-argued description of the development of English linguistic gender – a fact that makes it a pivotal addition to earlier theories of the field (e.g. Corbett 1991) – but it also utilises its findings to contribute to the research on contemporary gendered language

Exploration and Exploitation of Victorian Science in Darwin's Reading Notebooks

Search in an environment with an uncertain distribution of resources involves a trade-off between exploitation of past discoveries and further exploration. This extends to information foraging, where a knowledge-seeker shifts between reading in depth and studying new domains. To study this decision-making process, we examine the reading choices made by one of the most celebrated scientists of the modern era: Charles Darwin. From the full-text of books listed in his chronologically-organized reading journals, we generate topic models to quantify his local (text-to-text) and global (text-to-past) reading decisions using Kullback-Liebler Divergence, a cognitively-validated, information-theoretic measure of relative surprise. Rather than a pattern of surprise-minimization, corresponding to a pure exploitation strategy, Darwin's behavior shifts from early exploitation to later exploration, seeking unusually high levels of cognitive surprise relative to previous eras. These shifts, detected by an unsupervised Bayesian model, correlate with major intellectual epochs of his career as identified both by qualitative scholarship and Darwin's own self-commentary. Our methods allow us to compare his consumption of texts with their publication order. We find Darwin's consumption more exploratory than the culture's production, suggesting that underneath gradual societal changes are the explorations of individual synthesis and discovery. Our quantitative methods advance the study of cognitive search through a framework for testing interactions between individual and collective behavior and between short- and long-term consumption choices. This novel application of topic modeling to characterize individual reading complements widespread studies of collective scientific behavior.Comment: Cognition pre-print, published February 2017; 22 pages, plus 17 pages supporting information, 7 pages reference

The new path of law : from theory of chaos to theory of law

From chaos to chaos theory, from the primordial perception of the world as disorderly to the scientific research of disorder a long distance has been covered. This path implies openness of mind and scientific boldness which connect mythological perceptions of the world with philosophical and scientific interpretations of phenomena throughout the world in a quite distinctive way resting on the creation of a model and application of computing. Owing to this, for the first time instead of asking What awaits us in the future? we can ask What can be done in the future? and get a reliable scientific answer to the question