2011-12-24 17:34:17 通知 wangfeng
PhD Studentship in Linguistics at the Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University
A full-time PhD position, funded with a 3-year studentship, is available in the Department of Chinese and Bilingual Studies at the Hong Kong Polytechnic University. This position is situated within the project titled “Crowdsourcing, Linguistic Analyses, and Language Resources”, funded by the Hong Kong Research Grant Council’s General Research Fund (GRF), and awarded to Prof Huang Chu Ren (Principal Investigator, Dept of Chinese & Bilingual Studies, The Hong Kong Polytechnic University), Dr. Angel Chan and Dr. Yao Yao (co-investigators, Dept of Chinese & Bilingual Studies, The Hong Kong Polytechnic University), Dr. Li, Shoushan (co-investigator, Dept of Computing, Soochow University), and Dr. Li Wenjie (co-investigator, Dept of Computing, The Hong Kong Polytechnic University). The abstract of the project can be found at the end of this posting.
The doctoral student will participate actively in project-related research meetings involving an interdisciplinary team of researchers that include faculty, postdocs and students in Linguistics, Psycholinguistics, Computational Linguistics, Computer Science and related disciplines. The PhD student is expected to take an active role in designing, conducting and interpreting a series of linguistic judgment experiments concerning word segmentation and transparency of compounds in a Chinese context, using both traditional laboratory experiments and crowdsourcing techniques (via the Mechanical Turk).
A native Chinese speaker with a Master degree in Linguistics, Psychology, Computer Science, Cognitive Science or a related field is required. Background in Linguistics is a prerequisite. Technical experience with or background in psycholinguistic experimental methodologies and/or crowdsourcing methodologies is preferred.
The position can start in February 2012 or as soon as possible thereafter. For more information about the postgraduate studentship, please see http://www.polyu.edu.hk/fh/PhD/Leaflet.pdf
Candidates should send a CV, samples of English written work, a copy of published papers (if any), a copy of master degree thesis (if any, or a draft if the thesis is still in writing), result(s) of public exams such as TOEFL, IELTS, or GRE indicating level of English proficiency, and two to three letters of recommendation electronically to a special gmail account:
Review of applications will begin on 15 January 2012 and will continue until the position is filled.
Abstract of the GRF project “Crowdsourcing, Linguistic Analyses, and Language Resources”
Empirical approaches to the scientific studies of language developed rapidly in the last few decades due to the introduction of psychological experiments and electronic corpora. As experiment and measurement tools become more and more sophisticated, and corpora grow bigger and more diversified, new research topics are frequently introduced and exciting discoveries are made. However, regardless of these two successful new directions, we still have not overcome one very basic bottleneck in linguistic research: a reasonably representative sampling size. Language is an ability shared by all human beings and a specific language is a convention of behaviours shared by thousands, even millions, of speakers. So far, the experimental approach can only access the language production data of no more than a few scores of speakers, while corpus sampling focuses on variations rather than repetitions. Ideally, linguistic studies should be based on the data produced by a substantial sample of all speakers from different background. The recent development of Mechanical Turk (MTurk) offers a new and unique opportunity to collect linguistic behavior data from a substantial number of speakers effectively and economically. In this research, we will design three linguistic studies using MTurk and compare the results with psycholinguistic experiments and manually corpus annotations in order to explore the linguistic interpretation, psychological co-relations, and computational implementation of this new ‘crowd-sourcing’ approach to linguistic studies. Results from the study will have implications for linguistic research as well as for applications such as public opinion mining in the Chinese context.