Name Mentions Research Articles

Novel Character Identification Utilizing Semantic Relation with Animate Nouns in Korean

For identifying speakers of quoted speech or extracting social networks from literature, it is indispensable to extract character names and nominals. However, detecting proper nouns in the novels translated into or written in Korean is harder than in English because Korean does not have a capitalization feature. In addition, it is almost impossible for any proper noun dictionary to include all kinds of character names that have been created or will be created by authors. Fortunately, a previous study shows that utilizing postpositions for animate nouns is a simple and effective tool for character identification in Korean novels without a proper noun dictionary and a training corpus. In this article, we propose a character identification method utilizing the semantic relation with known animate nouns. For 80 novels in Korean, the proposed method increases the micro- and macro-average recall by 13.68% and 11.86%, respectively, while decreasing the micro-average precision by 0.28% and increasing the macro-average precision by 0.07% compared to the previous study. If we focus on characters that are responsible for more than 1% of the character name mentions in each novel, the micro- and macro-average F-measure of the proposed method are 96.98% and 97.32%, respectively.

Lost in Translation—and Transmission: Contrasting Chinese and U.S. Gymnastics Television Coverage in the 2016 Rio Olympic Games

Gymnastics is one of the most popular sports at the Summer Olympics in both the United States and China. This study content analyzes more than 23 hours of Olympic gymnastics at the 2016 Rio Games on NBC and CCTV, examining potential framing differences pertaining to nationality and biological sex. Findings reveal that, in terms of name mentions, NBC favored female and domestic gymnasts, yet CCTV devoted more mentions to male and nondomestic gymnasts. Regarding word-by-word descriptors, multiple significant differences strongly suggest that the two networks depict gymnasts in substantially different manners. Discussions and research directions are also offered.

Relaying Rio Through an Australian Gaze: Australian Nationalistic Broadcast Focus in the 2016 Summer Olympic Games

This study explored how potential national biases unfolded within the Australian broadcast of the 2016 Rio Summer Olympics. Applying social identity theory and self-categorization theory, this study content analyzed a total of 45 prime time broadcast hours of Australia’s Seven Network’s coverage of the Rio Games. Although the majority of top 20 most-mentioned athletes were Australian, non-Australian athletes were mentioned more frequently regarding total name mentions. Moreover, Australian athletes and non-Australian athletes were described in significantly different manners when ascribing reasons for athletic success and failure. This study contributed to the literature by uncovering how in-group members were portrayed in the Australian sports context while also providing insight into how consumers’ media consumption could potentially affect how the network broadcast the Olympics from a nationally partisan perspective.

DBpedia-Based Entity Linking via Greedy Search and Adjusted Monte Carlo Random Walk

Facing a large amount of entities appearing on the web, entity linking has recently become useful. It assigns an entity from a resource to one name mention to help users grasp the meaning of this name mention. Unfortunately, many possible entities can be assigned to one name mention. Apparently, the usually co-occurring name mentions are related and can be considered together to determine their best assignments. This approach is called collective entity linking and is often conducted based on entity graph. However, traditional collective entity linking methods either consume much time due to the large scale of entity graph or obtain low accuracy due to simplifying graph. To improve both accuracy and efficiency, this article proposes a novel collective entity linking algorithm. It first constructs an entity graph by connecting any two related entities, and then a probability-based objective function is proposed on this graph to ensure the high accuracy of the linking result. Via this function, we convert entity linking to the process of finding the nodes with the highest PageRank Values. Greedy search and an adjusted Monte Carlo random walk are proposed to fulfill this work. Experimental results demonstrate that our algorithm performs much better than traditional linking methods.

Gender differences through the lens of Rio: Australian Olympic primetime coverage of the 2016 Rio Summer Olympic Games

This study explored gender differences within the Australian primetime broadcast of the 2016 Rio Summer Olympic Games. Forty-five broadcast hours from the Seven Network were examined regarding clock-time, name mentions, and descriptions divided by biological sex, finding that the Seven Network devoted nearly equal clock-time to male and female athletes, yet 14 of the top 20 most mentioned athletes (70%) were men. In terms of word-by-word descriptors, gender differences were also uncovered on many levels relating to attributions of athletic success, failure, personality, and physicality. The findings of this study suggest that—at least within an Australian sports context—gender portrayals ranged from relative equality to significant differences depending on the metric employed. Theoretical and practical implications are provided.

When Women Fail to “Hold Up More Than Half the Sky”

Communist beliefs on gender equality and Chinese female athletes’ consistent ability to win national glory have lead to female athletes in Chinese sports media being primarily portrayed as legitimate athletes akin to levels of male athletes. This study examines such assumptions within Chinese Central Television’s (CCTV) 2016 Olympic telecast of gymnastics, a sport in which Chinese female athletes failed to attain national expectations, exploring potential variations of gendered frames in Chinese sports culture. A full examination of 100% (over 14 hr) of CCTV’s broadcast coverage revealed that female athletes received less media attention in terms of clock time and name mentions. Regarding word-by-word commentary ascribed to the gymnasts, CCTV employees significantly devoted more descriptors related to personality and physicality to female athletes, indicating a noticeable deviation from the Chinese traditional gendered frame of depicting female athletes as tenacious fighters with their nonsport-related aspects rarely covered. Implications for the findings are offered.

Ecolinguistics, GIS, and Corpus Linguistics for the Analysis of the Rosemont Copper Mine Debate

ABSTRACTThis article details an integration of geographical information system (GIS) and corpus linguistics (CL) techniques for an ecolinguistic analysis of a contentious, environmental debate in Arizona, USA. The application of GIS and CL procedures enabled the mapping of place name mentions present within two interest group corpora as well as the mapping of particular meanings and representations of specific places prominent in the debate, that is, maps representing both places and the discursive representations of those places were produced. The corpus-aided analysis and the GIS visualizations exhibit how different interest groups refer to and represent geographical places within their texts and how these references to and representations of places index ideological positions towards the mine and the environment. This integrated CL–GIS approach to the study of environmental discourse provides insights into the discursive representation of place and the importance that references to places serve within environmental discourse. The article briefly discusses the ecolinguistics framework underpinning the work, explains the GIS–CL approach, displays and interprets corpus findings and GIS visualizations, and details implications for additional GIS–CL research for environmental discourse.

Chinese Person Name Disambiguation Based on Two-Stage Clustering

Person name clustering disambiguation is the process that partitions name mentions according to corresponding target person entities in reality. The existed methods can not realize effective identification of important features to disambiguate person names. This paper presents a method of Chinese person name disambiguation based on two-stage clustering. This method adopts a stage-by-stage processing model to identify and utilize different types of important features. Firstly, we extract three kinds of core evidences namely direct social relation, indirect social relation and common description prefix, recognize document-pairs referring to the same person entity, and realize initial clustering of person names with high precision. Then, we take the result of initial clustering as new initial input, utilize the statistical properties of multi-documents to recognize and evaluate important features, and build a double-vector representation of clusters (cluster feature vector and important feature vector). Based on the processes above, the final clustering of person names is generated, and the recall of clustering is improved effectively. The experiments have been conducted on the dataset of CLP2010 Chinese person names disambiguation, and experimental results show that this method has good performance in person name clustering disambiguation.

Mobile phone name extraction from internet forums: a semi-supervised approach

Collecting users' feedback on products from Internet forums is challenging because users often mention a product with informal abbreviations or nicknames. In this paper, we propose a method named Gren to recognize and normalize mobile phone names from domain-specific Internet forums. Instead of directly recognizing phone names from sentences as in most named entity recognition tasks, we propose an approach to generating candidate names as the first step. The candidate names capture short forms, spelling variations, and nicknames of products, but are not noise free. To predict whether a candidate name mention in a sentence indeed refers to a specific phone model, a Conditional Random Field (CRF)-based name recognizer is developed. The CRF model is trained by using a large set of sentences obtained in a semi-automatic manner with minimal manual labeling effort. Lastly, a rule-based name normalization component maps a recognized name to its formal form. Evaluated on more than 4000 manually labeled sentences with about 1000 phone name mentions, Gren outperforms all baseline methods. Specifically, it achieves precision and recall of 0.918 and 0.875 respectively, with the best feature setting. We also provide detailed analysis of the intermediate results obtained by each of the three components in Gren.

Ambiguity and variability of database and software names in bioinformatics.

BackgroundThere are numerous options available to achieve various tasks in bioinformatics, but until recently, there were no tools that could systematically identify mentions of databases and tools within the literature. In this paper we explore the variability and ambiguity of database and software name mentions and compare dictionary and machine learning approaches to their identification.ResultsThrough the development and analysis of a corpus of 60 full-text documents manually annotated at the mention level, we report high variability and ambiguity in database and software mentions. On a test set of 25 full-text documents, a baseline dictionary look-up achieved an F-score of 46 %, highlighting not only variability and ambiguity but also the extensive number of new resources introduced. A machine learning approach achieved an F-score of 63 % (with precision of 74 %) and 70 % (with precision of 83 %) for strict and lenient matching respectively. We characterise the issues with various mention types and propose potential ways of capturing additional database and software mentions in the literature.ConclusionsOur analyses show that identification of mentions of databases and tools is a challenging task that cannot be achieved by relying on current manually-curated resource repositories. Although machine learning shows improvement and promise (primarily in precision), more contextual information needs to be taken into account to achieve a good degree of accuracy.

개체중의성해소에서 의미관련도 활용 효과 분석: 한국어 위키피디아를 사용하여

Abstract Entity linking is to link entity's name mentions occurring in t ext to corresponding entities within knowledge bases. Since the same entity mention may refer to different entities according to their context, entity linking needs to deal with entity disambiguation. Most recent works on entity disambiguation focus on semantic related-ness between entities and attempt to integrate semantic related ness with entity prior probabilities and term co-occurrence. To the best of my knowledge, however, it is hard to find studies that analyze and present the pure effects of semantic relatedness on entity disambiguation. From the experimentation on Korean Wikipedia da-ta set, this article empirically evaluates entity disambiguation approaches using semantic relatedness in terms of the following aspects: (1) the difference among semantic relatedness measures such as NGD, PMI, Jaccard, Dice,Simpson, (2) the influence of ambiguities in co-occurring entity mentions' set, and (3) the difference between in-dividual and collective disambiguation approaches.Key Words : Entity Linking, Entity Disambiguation, Semantic Relatedness, WikipediaReceived: Nov. 5, 2014Revised : Feb. 16, 2015Accepted: Mar. 10, 2015

Adapting the Edinburgh Geoparser for Historical Georeferencing

Place name mentions in text may have more than one potential referent (e.g. Peru, the country vs. Peru, the city in Indiana). The Edinburgh Language Technology Group (LTG) has developed the Edinburgh Geoparser, a system that can automatically recognise place name mentions in text and disambiguate them with respect to a gazetteer. The recognition step is required to identify location mentions in a given piece of text. The subsequent disambiguation step, generally referred to as georesolution, grounds location mentions to their corresponding gazetteer entries with latitude and longitude values, for example, to visualise them on a map. Geoparsing is not only useful for mapping purposes but also for making document collections more accessible as it can provide additional metadata about the geographical content of documents. Combined with other information mined from text such as person names and date expressions, complex relations between such pieces of information can be identified. The Edinburgh Geoparser can be used with several gazetteers including Unlock and GeoNames to process a variety of input texts. The original version of the Geoparser was a demonstrator configured for modern text. Since then, it has been adapted to georeference historic and ancient text collections as well as modern-day newspaper text. 1 , 2 , 3 , 4 Currently, the LTG is involved in three research projects applying the Geoparser to historical text collections of very different types and for a variety of end-user applications. This paper discusses the ways in which we have customised the Geoparser for specific datasets and applications relevant to each project.

ProNormz – An integrated approach for human proteins and protein kinases normalization

The task of recognizing and normalizing protein name mentions in biomedical literature is a challenging task and important for text mining applications such as protein–protein interactions, pathway reconstruction and many more. In this paper, we present ProNormz, an integrated approach for human proteins (HPs) tagging and normalization. In Homo sapiens, a greater number of biological processes are regulated by a large human gene family called protein kinases by post translational phosphorylation. Recognition and normalization of human protein kinases (HPKs) is considered to be important for the extraction of the underlying information on its regulatory mechanism from biomedical literature. ProNormz distinguishes HPKs from other HPs besides tagging and normalization. To our knowledge, ProNormz is the first normalization system available to distinguish HPKs from other HPs in addition to gene normalization task. ProNormz incorporates a specialized synonyms dictionary for human proteins and protein kinases, a set of 15 string matching rules and a disambiguation module to achieve the normalization. Experimental results on benchmark BioCreative II training and test datasets show that our integrated approach achieve a fairly good performance and outperforms more sophisticated semantic similarity and disambiguation systems presented in BioCreative II GN task. As a freely available web tool, ProNormz is useful to developers as extensible gene normalization implementation, to researchers as a standard for comparing their innovative techniques, and to biologists for normalization and categorization of HPs and HPKs mentions in biomedical literature. URL: http://www.biominingbu.org/pronormz.

More Tweets, More Votes: Social Media as a Quantitative Indicator of Political Behavior

Is social media a valid indicator of political behavior? We answer this question using a random sample of 537,231,508 tweets from August 1 to November 1, 2010 and data from 406 competitive U.S. congressional elections provided by the Federal Election Commission. Our results show that the percentage of Republican-candidate name mentions correlates with the Republican vote margin in the subsequent election. This finding persists even when controlling for incumbency, district partisanship, media coverage of the race, time, and demographic variables such as the district’s racial and gender composition. With over 500 million active users in 2012, Twitter now represents a new frontier for the study of human behavior. This research provides a framework for incorporating this emerging medium into the computational social science toolkit.

LINNAEUS: a species name identification system for biomedical literature.

BackgroundThe task of recognizing and identifying species names in biomedical literature has recently been regarded as critical for a number of applications in text and data mining, including gene name recognition, species-specific document retrieval, and semantic enrichment of biomedical articles.ResultsIn this paper we describe an open-source species name recognition and normalization software system, LINNAEUS, and evaluate its performance relative to several automatically generated biomedical corpora, as well as a novel corpus of full-text documents manually annotated for species mentions. LINNAEUS uses a dictionary-based approach (implemented as an efficient deterministic finite-state automaton) to identify species names and a set of heuristics to resolve ambiguous mentions. When compared against our manually annotated corpus, LINNAEUS performs with 94% recall and 97% precision at the mention level, and 98% recall and 90% precision at the document level. Our system successfully solves the problem of disambiguating uncertain species mentions, with 97% of all mentions in PubMed Central full-text documents resolved to unambiguous NCBI taxonomy identifiers.ConclusionsLINNAEUS is an open source, stand-alone software system capable of recognizing and normalizing species name mentions with speed and accuracy, and can therefore be integrated into a range of bioinformatics and text-mining applications. The software and manually annotated corpus can be downloaded freely at http://linnaeus.sourceforge.net/.

Overview of BioCreative II gene mention recognition

Nineteen teams presented results for the Gene Mention Task at the BioCreative II Workshop. In this task participants designed systems to identify substrings in sentences corresponding to gene name mentions. A variety of different methods were used and the results varied with a highest achieved F1 score of 0.8721. Here we present brief descriptions of all the methods used and a statistical analysis of the results. We also demonstrate that, by combining the results from all submissions, an F score of 0.9066 is feasible, and furthermore that the best result makes use of the lowest scoring submissions.

A cascaded approach to normalising gene mentions in biomedical literature.

Linking gene and protein names mentioned in the literature to unique identifiers in referent genomic databases is an essential step in accessing and integrating knowledge in the biomedical domain. However, it remains a challenging task due to lexical and terminological variation, and ambiguity of gene name mentions in documents. We present a generic and effective rule-based approach to link gene mentions in the literature to referent genomic databases, where pre-processing of both gene synonyms in the databases and gene mentions in text are first applied. The mapping method employs a cascaded approach, which combines exact, exact-like and token-based approximate matching by using flexible representations of a gene synonym dictionary and gene mentions generated during the pre-processing phase. We also consider multi-gene name mentions and permutation of components in gene names. A systematic evaluation of the suggested methods has identified steps that are beneficial for improving either precision or recall in gene name identification. The results of the experiments on the BioCreAtIvE2 data sets (identification of human gene names) demonstrated that our methods achieved highly encouraging results with F-measure of up to 81.20%.

Changes in the prevalence of alcohol use in rap song lyrics, 1979–97

This paper explores the role of changing images of drinking and alcoholic beverage use in rap music from its beginnings in the United States in the late 1970s to the late 1990s. A sample of 341 rap music song lyrics released from 1979 to 1997 were selected using Billboard and Gavin rating charts. Song lyrics were coded for music genres, alcohol beverage types and brand names, drinking behaviors, drinking contexts, intoxication, attitudes towards alcohol and consequences of drinking. From 1979 to 1997, songs with references to alcohol increased fivefold (from 8 to 44%); those exhibiting positive attitudes rose from 43% to 73%; and brand name mentions increased from 46% to 71%. There were also significant increases in songs mentioning champagne and liquor (mainly expensive brand names) when comparing songs released after 1994 with those from previous years. In addition, there were significant increases in references to alcohol to signify glamour and wealth, and using alcohol with drugs and for recreational purposes. The findings also showed that alcohol use in rap music was much more likely to result in positive than negative consequences. Many of these findings are consistent with the idea that rap music has been profoundly affected by commercial forces and the marketing of alcoholic beverages. In addition, it is possible that the increase in references to alcoholic beverages in rap music, particularly spirits, is a reflection of a broader advertising culture which increasingly associates African Americans with alcohol use.