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In recent years, formal semantic research on the meaning of tense and aspect has benefited from a number of studies investigating languages with graded tense systems. This paper contributes a first sketch of the temporal marking system of Awing (Grassfields Bantu), focusing on two varieties of remote past and remote future. We argue that the data support a "symmetric" analysis of past and future tense in Awing. In our specific proposal, Awing temporal remoteness markers are uniformly analyzed as quantificational tense operators, and both the past and the future paradigm include a form that prevents contextual restriction of this temporal quantifier.
Linguistic query systems are special purpose IR applications. We present a novel state-of-the-art approach for the efficient exploitation of very large linguistic corpora, combining the advantages of relational database management systems (RDBMS) with the functional MapReduce programming model. Our implementation uses the German DEREKO reference corpus with multi-layer linguistic annotations and several types of text-specific metadata, but the proposed strategy is language-independent and adaptable to large-scale multilingual corpora.
The Manatee corpus management system on which the Sketch Engine is built is efficient, but unable to harness the power of today’s multiprocessor machines. We describe a new, compatible implementation of Manatee which we develop in the Go language and report on the performance gains that we obtained.
In the first volume of Corpus Linguistics and Linguistic Theory, Gries (2005. Null-hypothesis significance testing of word frequencies: A follow-up on Kilgarriff. Corpus Linguistics and Linguistic Theory 1(2). doi:10.1515/ cllt.2005.1.2.277. http://www.degruyter.com/view/j/cllt.2005.1.issue-2/cllt.2005. 1.2.277/cllt.2005.1.2.277.xml: 285) asked whether corpus linguists should abandon null-hypothesis significance testing. In this paper, I want to revive this discussion by defending the argument that the assumptions that allow inferences about a given population – in this case about the studied languages – based on results observed in a sample – in this case a collection of naturally occurring language data – are not fulfilled. As a consequence, corpus linguists should indeed abandon null-hypothesis significance testing.
American English and German AI, AU observed in cognates such as Wein, wine, Haus, house are usually treated on a par, represented with the same initial vowel (cf. [ai], [au] for Am. Engl, and German [1]). Yet, acoustic measurements indicate differences as the relevant trajectories characteristically cross in Am. Engl, but not in German. These data may indicate consistency with the same initial target for these diphthongs in German, supporting the choice of the same Symbol /a/ in phonemic representation, as opposed to distinct targets (and distinct initial phonemes) in American English.
The English language has taken advantage of the Digital Revolution to establish itself as the global language; however, only 28.6 %of Internet users speak English as their native language. Machine Trans-lation (MT) is a powerful technology that can bridge this gap. In devel-opment since the mid-20th century, MT has become available to every Internet user in the last decade, due to free online MT services. This paper aims to discuss the implications that these tools may have for the privacy of their users and how they are addressed by EU data protec-tion law. It examines the data-flows in respect of the initial processing (both from the perspective of the user and the MT service provider) and potential further processing that may be undertaken by the MT service provider.