3,046 research outputs found
An automatic part-of-speech tagger for Middle Low German
Syntactically annotated corpora are highly important for enabling large-scale diachronic and diatopic language research. Such corpora have recently been developed for a variety of historical languages, or are still under development. One of those under development is the fully tagged and parsed Corpus of Historical Low German (CHLG), which is aimed at facilitating research into the highly under-researched diachronic syntax of Low German. The present paper reports on a crucial step in creating the corpus, viz. the creation of a part-of-speech tagger for Middle Low German (MLG). Having been transmitted in several non-standardised written varieties, MLG poses a challenge to standard POS taggers, which usually rely on normalized spelling. We outline the major issues faced in the creation of the tagger and present our solutions to them
Blog Style Classification: Refining Affective Blogs
In the constantly growing blogosphere with no restrictions on form or topic, a number of writing styles and genres have emerged. Recognition and classification of these styles has become significant for information processing with an aim to improve blog search or sentiment mining. One of the main issues in this field is detection of informative and affective articles. However, such differentiation does not suffice today. In this paper we extend the differentiation and suggest a fine-grained set of subcategories for affective articles. We propose and evaluate a classification method employing novel lexical, morphological, lightweight syntactic and structural features of written text. The results show that our method outperforms the existing approaches
A literature survey of methods for analysis of subjective language
Subjective language is used to express attitudes and opinions towards things, ideas and people. While content and topic centred natural language processing is now part of everyday life, analysis of subjective aspects of natural language have until recently been largely neglected by the research community. The explosive growth of personal blogs, consumer opinion sites and social network applications in the last years, have however created increased interest in subjective language analysis. This paper provides an overview of recent research conducted in the area
A matter of words: NLP for quality evaluation of Wikipedia medical articles
Automatic quality evaluation of Web information is a task with many fields of
applications and of great relevance, especially in critical domains like the
medical one. We move from the intuition that the quality of content of medical
Web documents is affected by features related with the specific domain. First,
the usage of a specific vocabulary (Domain Informativeness); then, the adoption
of specific codes (like those used in the infoboxes of Wikipedia articles) and
the type of document (e.g., historical and technical ones). In this paper, we
propose to leverage specific domain features to improve the results of the
evaluation of Wikipedia medical articles. In particular, we evaluate the
articles adopting an "actionable" model, whose features are related to the
content of the articles, so that the model can also directly suggest strategies
for improving a given article quality. We rely on Natural Language Processing
(NLP) and dictionaries-based techniques in order to extract the bio-medical
concepts in a text. We prove the effectiveness of our approach by classifying
the medical articles of the Wikipedia Medicine Portal, which have been
previously manually labeled by the Wiki Project team. The results of our
experiments confirm that, by considering domain-oriented features, it is
possible to obtain sensible improvements with respect to existing solutions,
mainly for those articles that other approaches have less correctly classified.
Other than being interesting by their own, the results call for further
research in the area of domain specific features suitable for Web data quality
assessment
A Survey of AI Music Generation Tools and Models
In this work, we provide a comprehensive survey of AI music generation tools,
including both research projects and commercialized applications. To conduct
our analysis, we classified music generation approaches into three categories:
parameter-based, text-based, and visual-based classes. Our survey highlights
the diverse possibilities and functional features of these tools, which cater
to a wide range of users, from regular listeners to professional musicians. We
observed that each tool has its own set of advantages and limitations. As a
result, we have compiled a comprehensive list of these factors that should be
considered during the tool selection process. Moreover, our survey offers
critical insights into the underlying mechanisms and challenges of AI music
generation
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