71 research outputs found

    Genre Annotation for the Web: text-external and text-internal perspectives

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    This paper describes a digital curation study aimed at comparing the composition of large Web corpora, such as enTenTen, ukWac or ruWac, by means of automatic text classification. First, the paper presents a Deep Learning model suitable for classifying texts from large Web corpora using a small number of communicative functions, such as Argumentation or Reporting. Second, it describes the results of applying the automatic classification model to these corpora and compares their composition. Finally, the paper introduces a framework for interpreting the results of automatic genre classification using linguistic features. The framework can help in comparing general reference corpora obtained from the Web and in comparing corpora across languages

    Functional Text Dimensions for the annotation of web corpora

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    This paper presents an approach to classifying large web corpora into genres by means of Functional Text Dimensions (FTDs). This offers a topological approach to text typology in which the texts are described in terms of their similarity to prototype genres. The suggested set of categories is designed to be applicable to any text on the web and to be reliable in annotation practice. Interannotator agreement results show that the suggested categories produce Krippendorff's α at above 0.76. In addition to the functional space of eighteen dimensions, similarity between annotated documents can be described visually within a space of reduced dimensions obtained through t-distributed Statistical Neighbour Embedding. Reliably annotated texts also provide the basis for automatic genre classification, which can be done in each FTD, as well as as within the space of reduced dimensions. An example comparing texts from the Brown Corpus, the BNC and ukWac, a large web corpus, is provided

    English → Russian MT evaluation campaign

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    This paper presents the settings and the result of the ROMIP 2013 MT shared task for the English→Russian language direction. The quality of generated translations was assessed using automatic metrics and human evaluation. We also discuss ways to reduce human evaluation efforts using pairwise sentence comparisons by human judges to simulate sort operations

    Crowdsourcing for web genre annotation

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    Recently, genre collection and automatic genre identification for the web has attracted much attention. However, currently there is no genre-annotated corpus of web pages where inter-annotator reliability has been established, i.e. the corpora are either not tested for inter-annotator reliability or exhibit low inter-coder agreement. Annotation has also mostly been carried out by a small number of experts, leading to concerns with regard to scalability of these annotation efforts and transferability of the schemes to annotators outside these small expert groups. In this paper, we tackle these problems by using crowd-sourcing for genre annotation, leading to the Leeds Web Genre Corpus—the first web corpus which is, demonstrably reliably annotated for genre and which can be easily and cost-effectively expanded using naive annotators. We also show that the corpus is source and topic diverse

    Slavic corpus and computational linguistics

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    In this paper, we focus on corpus-linguistic studies that address theoretical questions and on computational linguistic work on corpus annotation, that makes corpora useful for linguistic work. First, we discuss why the corpus linguistic approach was discredited by generative linguists in the second half of the 20th century, how it made a comeback through advances in computing and was adopted by usage-based linguistics at the beginning of the 21st century. Then, we move on to an overview of necessary and common annotation layers and the issues that are encountered when performing automatic annotation, with special emphasis on Slavic languages. Finally, we survey the types of research requiring corpora that Slavic linguists are involved in world-wide, and the resources they have at their disposal

    Arabic and Arab English in the Arab world

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    We begin with two questions about the relative status of Arabic and English in the Arab World: Is there an Arab English? And should Arab science be reported in English or Arabic? To investigate the first question, we collected a WWW corpus of English from Arab countries, and used this as a basis for comparison with UK and US English WWW-corpora. We present the differences found, and possible explanations for the differences. This leads us to some conclusions and ideas for further investigation

    ROMIP MT Evaluatio n Track 2013: Organizer s’ Report

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    The paper presents the settings and the results of the ROMIP 2013 machine translation evaluation campaign for the English-to-Russian language pair. The quality of generated translations was assessed using automatic metrics and human evaluation. We also demonstrate the usefulness of a dynamic mechanism for human evaluation based on pairwise segment comparison

    Dynamics of conflicts in Wikipedia

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    In this work we study the dynamical features of editorial wars in Wikipedia (WP). Based on our previously established algorithm, we build up samples of controversial and peaceful articles and analyze the temporal characteristics of the activity in these samples. On short time scales, we show that there is a clear correspondence between conflict and burstiness of activity patterns, and that memory effects play an important role in controversies. On long time scales, we identify three distinct developmental patterns for the overall behavior of the articles. We are able to distinguish cases eventually leading to consensus from those cases where a compromise is far from achievable. Finally, we analyze discussion networks and conclude that edit wars are mainly fought by few editors only.Comment: Supporting information adde
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