Hard news articles, just like op-ed articles, can reflect a media organization's bias. This study assesses bias in the hard news articles published by Fox News and MSNBC. Indicative linguistic features identified by the Gramulator reveal biases in corpora from the two networks.

With an explosion in the proliferation of user-generated content in communities, information overload is increasing and quality of readily available online content is deteriorating. There is an increasing need for intelligent systems that make use of implicit user generated knowledge in communities for community engagement. We describe our approach based on modeling user utterances in communities to proactively target the community for exchange of questions and answers. We envision a system that automatically encourages user engagement and participation by routing relevant conversations to users based on individual and community activity levels. In this paper, we analyze health forum conversations from WebMD, a popular health portal consumer site, and classify them in different acts of speech using Verbal Response Modes (VRM) theory. We describe our approach for modeling an intelligent community recommender to engage participants based on observations from our analysis.

Analyzing chat traffic has important applications for both the military and the civilian world. This poster will report on an effort to automatically separate chat messages into topic threads.

Harnessing rich, but unstructured information on social networks in real-time and showing it to relevant audience based on its geographic location is a major challenge. The system developed, TwitterTagger, geotags tweets and shows them to users based on their current physical location. Experimental validation shows a performance improvement of three orders by TwitterTagger compared to that of the baseline model.

Expressions (MWEs) (Okita et al. 2010), this may improve the overall translation. For example in EN-JP, the empirical evidences 2007; Koehn 2010) intends to handle morphologically rich suggest that we separate affix(es) and word stem(s) since it languages in the target side by integrating additional linguistic obtains better BLEU score than the case when we do not separate markup at the word level, where each type of additional them although the adequacy decreases.

In this study, we analyze two corpora of art critiques: one on the subject of photography and the other on the subject of modern art. We use two computational tools, the Gramulator and GPAT to analyze both sets of texts. The Gramulator was used to show the indicative linguistic features that make photography criticism a distinct genre from modern art criticism. Results suggest that lexical features, structural formats, and genre consistency differed significantly between the two corpora. The findings provide information for teachers, students, publishers, and curriculum developers for creating more effective writing and teaching materials. This includes material for English for Specific Purposes (ESP) in the form of textbooks, workbooks and other external learning material.

Our corpus comprises 752 texts, culled from newspapers of U.S. border states (approximately 75 texts per state). Immigration is a national issue in the United States; Because four states border Mexico, we selected four however, regional implications differ because of matching states (of the 11) that border Canada. To do so, immigrants' varying effects on local economies. These we considered the following criteria for all 15 terrestrial implications are made manifest in the reportage of local border states: total population, immigrant population, newspapers, which, while ostensibly portraying length of international border, and political leaning. These "objective" language, may reveal the narrative of local data were input into a custom PERL script designed to perspectives on national issues.

This paper describes preliminary research in the use of ontological knowledge for the task of automatically classifying classical Chinese poetry (CCCP) according to authorship. Based on a collection of poems written by Liu Yong (987–1053 AD) and Su Shi (1037– 1101 AD), which have been analyzed according to a taxonomy of ontological entities at the lexical level, the research looks into the issue of whether characteristic features can be automatically extracted as important stylistic differences between the two poets. This paper examines the efficiency of different ontological concepts as features in CCCP using Support Vector Machine (SVMs). The experiment shows that an integration of ontological knowledge and bags-of-words (BoW) produces a higher precision for CCCP than BoW only with an overall increase of 2.1% and 2.2% in terms of precision and F-score.

This paper presents an automatic extraction and classification approach of opinions in texts. Therefore, we propose a similarity measurement calculating semantically similarities between a word and predefined subgroups of seed words. We have evaluated our approach on the semantic evaluation company “SemEval 2007” corpus, and we obtained promising results: the best value of Precision, 62%; and F1, 61%; as an improvement of 20 % compared to the participant systems.

Question generation tools can be used to extract a question-answer database from text articles. We investigate how suitable this technique is for giving domain-specific knowledge to conversational characters. We tested these characters by collecting questions and answers from naive participants, running the questions through the character, and comparing the system responses to the participant answers. Characters gave a full or partial answer to 53% of the user questions which had an answer available in the source text, and 43% of all questions asked. Performance was better for questions asked after the user had read the source text, and also varied by question type: the best results were answers to who questions, while answers to yes/no questions were among the poorer performers. The results show that question generation is a promising method for creating a question answering conversational character from an existing text.