비유언어와 문맥 표지를 이용한 반어법 자동 분류 연구

학위논문 (석사)-- 서울대학교 대학원 : 언어학과, 2014. 8. 신효필.본 논문은 고빈도 비유언어(figurative language)를 이용한 반어법 자동 인식 방법을 제안한다. 반어법과 비유언어들(직유법, 은유법, 의인법, 과장법)을 인식하는 문제는 컴퓨터 언어학에서 매우 중요한 분야이다. 이런 비유 언어들은 표면적인 의미와 다른 의미를 내포하기 때문에 그 문장의 의미를 파악하는데 필요한 연구이다. 과장법이나 과소 법 같은 비유언어와 달리 특별히 반어법은 그 표현적 의미와 정 반대 또는 부합하지 않는 의미를 내포하기 때문에 더욱 문제가 된다. 구어에서 반어법이 사용될 때는 운율이라는 요소가 인식에 중요한 역할을 하는 반면, 문어에서 반어법은 운율 정보가 없기 때문에 더 인식이 어렵다. 또한, 반어법은 대부분의 경우 표면적으로 나타나는 명확한 단서를 포함하지 않고, 단지 준언어적, 문맥적 화용적인 단서만을 갖기 때문에 인식에 더 어려움이 크다. 반어법의 단서가 되는 예로는 청자에게 문자 그대로 이해되기를 바라지 않음을 암시하는 과장법, 과소법, 수사적 질문법, 부가 의문문 같은 것들이 존재한다. 본고는 동시에 나타나는 비유언어들을 각각 인식하여 그 결과를 반어법 검출기에 제공하는 방식의 분할-정복법을 소개한다. 짧은 길이의 트위터와 상대적으로 긴 아마존 상품평에 대해 실행한 실험은 이러한 비유언어들을 개별적으로 인식하여 반어법의 자동 인식에 사용하는 것이 비유언어들을 한번에 인식하는 방법 보다 반어법 인식에 효과적이라는 사실을 밝혔다. 또한, 지금까지 개별적으로 제한된 문맥만을 고려한 과장법, 과소법 연구와 달리 본 연구는 반어법 인식에 사용되는 기존의 연구 방법을 과장법과 과소법 인식에도 적용할 수 있는 가능성을 제시하였다는 의의가 있다.This thesis proposes a linguistic-based irony detection method which uses these frequently co-occurring figurative languages to identify areas where irony is likely to occur. The detection and proper interpretation of irony and other figurative languages represents an important area of research for Computational Linguistics. Since figurative languages typically convey meanings which differ from their literal interpretations, interpreting such utterances at face value is likely to give incorrect results. Irony in particular represents a special challenge as, unlike some figurative languages like hyperbole or understatement which express sentiments which are more-or-less in line with their literal interpretation, differing only in intensity, ironic utterances convey intended meanings incongruent with – or even the exact opposite of – their literal interpretation. Compounding the need for effective irony detection is ironys near ubiquitous use in online writings and computer-mediated communications, both of which are commonly used in Computational Linguistics experiments. While irony in spoken contexts tends to be denoted using prosody, irony in written contexts is much harder to detect. One of the major difficulties is that irony typically does not present with any explicit clues such as punctuation marks or verbal inflections. Instead, irony tends to be denoted using paralinguistic, contextual, or pragmatic cues. Among these are the co-occurrence of figurative languages such as hyperbole, understatement, rhetorical questions, tag questions, or other ironic utterances which alert the listener that the speaker does not expect to be interpreted literally. This thesis introduces a divide-and-conquer approach to irony detection where co-occurring figurative languages are identified independently and then fed into an overall irony detector. Experiments on both short-form Twitter tweets and longer-form Amazon product reviews show not only that co-textual figurative languages are useful in the automatic classification of irony but that identifying these co-occurring figurative languages separately yields better overall irony detection by resolving conflicts between conflicting features, such as those for hyperbole and understatement. This thesis also introduces detection methods for hyperbole and understatement in general contexts by adapting existing approaches to irony detection. Before this point hyperbole detection was focused only on specialized contexts while understatement detection had been largely ignored. Experiments show that these proposed automated hyperbole and understatement detection methods outperformed methods which rely on fixed vocabularies.1 Introduction 1 1.1 What is Irony? 2 1.2 Irony and Co-textual Markers 4 1.2.1 Hyperbole 6 1.2.2 Understatement 7 1.2.3 Rhetorical Questions 8 1.2.4 Tag Questions 9 2 Previous Works 10 2.1 Irony Detection 10 2.2 Detection of Co-textual Markers 12 3 Data Collection 15 3.1 Twitter Data 15 3.1.1 Twitter Irony Corpus 18 3.1.2 Twitter Hyperbole Corpus 18 3.1.3 Twitter Understatement Corpus 18 3.2 Amazon Data 19 4 Experimental Set-up 21 4.1 Hyperbole Detection 22 4.2 Understatement Detection 23 4.3 Rhetorical Question Detection 25 4.4 Tag Question Detection 27 4.5 Irony Detection 28 4.5.1 Twitter Data 30 4.5.2 Amazon Product Review Data 30 5 Results and Discussion 33 5.1 Hyperbole 33 5.2 Understatement 39 5.3 Irony 44 5.3.1 Twitter 44 5.3.2 Amazon Product Reviews 50 6 Conclusions and Future Work 57 7 References 60 Appendix 1 Hyperbole Word List 66 Appendix 2 Hedge Word List 69Maste

Multimorbidity and survival for patients with acute myocardial infarction in England and Wales: Latent class analysis of a nationwide population-based cohort

Background: There is limited knowledge of the scale and impact of multimorbidity for patients who have had an acute myocardial infarction (AMI). Therefore, this study aimed to determine the extent to which multimorbidity is associated with long-term survival following AMI. Methods and findings: This national observational study included 693,388 patients (median age 70.7 years, 452,896 [65.5%] male) from the Myocardial Ischaemia National Audit Project (England and Wales) who were admitted with AMI between 1 January 2003 and 30 June 2013. There were 412,809 (59.5%) patients with multimorbidity at the time of admission with AMI, i.e., having at least 1 of the following long-term health conditions: diabetes, chronic obstructive pulmonary disease or asthma, heart failure, renal failure, cerebrovascular disease, peripheral vascular disease, or hypertension. Those with heart failure, renal failure, or cerebrovascular disease had the worst outcomes (39.5 [95% CI 39.0–40.0], 38.2 [27.7–26.8], and 26.6 [25.2–26.4] deaths per 100 person-years, respectively). Latent class analysis revealed 3 multimorbidity phenotype clusters: (1) a high multimorbidity class, with concomitant heart failure, peripheral vascular disease, and hypertension, (2) a medium multimorbidity class, with peripheral vascular disease and hypertension, and (3) a low multimorbidity class. Patients in class 1 were less likely to receive pharmacological therapies compared with class 2 and 3 patients (including aspirin, 83.8% versus 87.3% and 87.2%, respectively; β-blockers, 74.0% versus 80.9% and 81.4%; and statins, 80.6% versus 85.9% and 85.2%). Flexible parametric survival modelling indicated that patients in class 1 and class 2 had a 2.4-fold (95% CI 2.3–2.5) and 1.5-fold (95% CI 1.4–1.5) increased risk of death and a loss in life expectancy of 2.89 and 1.52 years, respectively, compared with those in class 3 over the 8.4-year follow-up period. The study was limited to all-cause mortality due to the lack of available cause-specific mortality data. However, we isolated the disease-specific association with mortality by providing the loss in life expectancy following AMI according to multimorbidity phenotype cluster compared with the general age-, sex-, and year-matched population. Conclusions: Multimorbidity among patients with AMI was common, and conferred an accumulative increased risk of death. Three multimorbidity phenotype clusters that were significantly associated with loss in life expectancy were identified and should be a concomitant treatment target to improve cardiovascular outcomes

Role of eolian dust deposits in landscape development and soil degradation in southeastern Australia

Eolian dust plays a significant role in landscape development and landscape processes in Australia. Thin dust mantles, rarely exceeding 3 m in thickness, have been identified across many parts of the Australian landscape, particularly in southeastern Australia. The nature and properties of these dust materials can have a major influence on environmental degradation processes such as salinisation and soil erosion. Despite the existing body of research regarding this topic, there are still conflicting views about the likely sources, transport modes and properties of eolian dust in the Australian landscape. The aim of this review is to synthesise much of the available information and put forward a working hypothesis for the distribution and fundamental properties of dust deposits in southeastern Australia. A conceptual model describing the various dust sources and sinks, and the modes of transport of dust materials both into and out of these source areas, is introduced. The model identifies key source areas, such as the alluvial and lacustrine environments of the Lake Eyre and Murray-Darling Basins, and sinks, such as the Eastern Highlands. Transport rates and paths for eolian-dust materials across the Australian continent are also outlined. The model places particular emphasis on the recycling of dust, whereby sediments sourced from the Eastern Highlands are transported westward via the major alluvial networks, and deposited on floodplains or in terminal drainage systems. These sediments are then available to be reworked into local eolian landforms prior to re-entrainment of the finer materials in the easterly dust pathway. The characteristics of deposited eolian sediments are then outlined in detail, focusing particularly on their particle-size distribution, mineralogical composition, and geophysical and geochemical properties. The review also presents data and images of dust materials sampled from source areas, sink areas, as well as materials that have been deposited by modern dust events. Finally, the role of dust materials in land-degradation processes, particularly soil erosion, is discussed. The degree of reworking of the eolian sediments, the extent of leaching and the level of sodicity are all important in determining soil structural stability and hence erosion potential of these materials

The role of climate and local regolith-landscape processes in determining the pedological characteristics of æolian dust deposits across south-eastern Australia

Fine-grained æolian sediments are an important component of many loessic soil-landscape systems across south-eastern Australia. These loessic soils are commonly related to the deposition of 'parna', a red, clayey, calcareous material proposed to have be

Prospect Eleven: Princeton University's entry in the 2005 DARPA Grand Challenge

This paper describes Princeton University's approach to the 2005 DARPA Grand Challenge, an off-road race for fully autonomous ground vehicles. The system, Prospect Eleven, takes a simple approach to address the problems posed by the Grand Challenge, including obstacle detection, path planning, and extended operation in harsh environments. Obstacles are detected using stereo vision, and tracked in the time domain to improve accuracy in localization and reduce false positives. The navigation system processes a geometric representation of the world to identify passable regions in the terrain ahead, and the vehicle is controlled to drive through these regions. Performance of the system is evaluated both during the Grand Challenge and in subsequent desert testing. The vehicle completed 9.3 miles of the course on race day, and extensive portions of the 2004 and 2005 Grand Challenge courses in later tests