761 research outputs found
Identifying literary texts with bigrams
We study perceptions of literariness in a set of contemporary Dutch novels. Experiments with machine learning models show that it is possible to automatically distinguish novels that are seen as highly literary from those that are seen as less literary, using surprisingly simple textual features. The most discriminating features of our classification model indicate that genre might be a confounding factor, but a regression model shows that we can also explain variation between highly literary novels from less literary ones within genre
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Identifying idiolect in forensic authorship attribution: an n-gram textbite approach
Forensic authorship attribution is concerned with identifying authors of disputed or anonymous documents, which are potentially evidential in legal cases, through the analysis of linguistic clues left behind by writers. The forensic linguist “approaches this problem of questioned authorship from the theoretical position that every native speaker has their own distinct and individual version of the language [. . . ], their own idiolect” (Coulthard, 2004: 31). However, given the diXculty in empirically substantiating a theory of idiolect, there is growing concern in the Veld that it remains too abstract to be of practical use (Kredens, 2002; Grant, 2010; Turell, 2010). Stylistic, corpus, and computational approaches to text, however, are able to identify repeated collocational patterns, or n-grams, two to six word chunks of language, similar to the popular notion of soundbites: small segments of no more than a few seconds of speech that journalists are able to recognise as having news value and which characterise the important moments of talk. The soundbite oUers an intriguing parallel for authorship attribution studies, with the following question arising: looking at any set of texts by any author, is it possible to identify ‘n-gram textbites’, small textual segments that characterise that author’s writing, providing DNA-like chunks of identifying material
A Data-Oriented Model of Literary Language
We consider the task of predicting how literary a text is, with a gold
standard from human ratings. Aside from a standard bigram baseline, we apply
rich syntactic tree fragments, mined from the training set, and a series of
hand-picked features. Our model is the first to distinguish degrees of highly
and less literary novels using a variety of lexical and syntactic features, and
explains 76.0 % of the variation in literary ratings.Comment: To be published in EACL 2017, 11 page
Statistical Augmentation of a Chinese Machine-Readable Dictionary
We describe a method of using statistically-collected Chinese character
groups from a corpus to augment a Chinese dictionary. The method is
particularly useful for extracting domain-specific and regional words not
readily available in machine-readable dictionaries. Output was evaluated both
using human evaluators and against a previously available dictionary. We also
evaluated performance improvement in automatic Chinese tokenization. Results
show that our method outputs legitimate words, acronymic constructions, idioms,
names and titles, as well as technical compounds, many of which were lacking
from the original dictionary.Comment: 17 pages, uuencoded compressed PostScrip
Predicting the Law Area and Decisions of French Supreme Court Cases
In this paper, we investigate the application of text classification methods
to predict the law area and the decision of cases judged by the French Supreme
Court. We also investigate the influence of the time period in which a ruling
was made over the textual form of the case description and the extent to which
it is necessary to mask the judge's motivation for a ruling to emulate a
real-world test scenario. We report results of 96% f1 score in predicting a
case ruling, 90% f1 score in predicting the law area of a case, and 75.9% f1
score in estimating the time span when a ruling has been issued using a linear
Support Vector Machine (SVM) classifier trained on lexical features.Comment: RANLP 201
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