2,572 research outputs found
Doctors’ Pragmatic Identity Construction Based on The Doctors
In recent years, conflicts between doctors and patients in China have occurred from time to time. In the past, some scholars conducted research on the doctor-patient relationship, but there are few studies on doctors’ pragmatic identity construction. Therefore, guided by Chen Xinren’s pragmatic identity theory, using python as an analytical aid, this paper uses a combination of qualitative and quantitative analysis to conduct a study of doctor’s pragmatic identity construction based on a medical documentary The Doctors. The main focus of this study is not only the types of pragmatic identity constructed by doctors in the documentary, but also the emotional characteristics of these pragmatic identities. According to this research, the doctors in the documentary The Doctors mainly construct expert identity, peer identity, and stress bearer identity. The overall emotional characteristics of the constructed pragmatic identities are neutral, and positive emotions are greater than negative ones. This paper has certain research significance. For one thing, this study provides a new research perspective for doctors’ pragmatic identity construction, that is, to study the overall emotional characteristics of the constructed identities. For another, this study can help the public understand the pragmatic identity of doctors to a certain extent, and promote the harmonious relationship between doctors and patients
Phraseology in Corpus-Based Translation Studies: A Stylistic Study of Two Contemporary Chinese Translations of Cervantes's Don Quijote
The present work sets out to investigate the stylistic profiles of two modern Chinese versions of
Cervantes’s Don Quijote (I): by Yang Jiang (1978), the first direct translation from Castilian to Chinese,
and by Liu Jingsheng (1995), which is one of the most commercially successful versions of the
Castilian literary classic. This thesis focuses on a detailed linguistic analysis carried out with the help
of the latest textual analytical tools, natural language processing applications and statistical packages.
The type of linguistic phenomenon singled out for study is four-character expressions (FCEXs), which
are a very typical category of Chinese phraseology. The work opens with the creation of a descriptive
framework for the annotation of linguistic data extracted from the parallel corpus of Don Quijote.
Subsequently, the classified and extracted data are put through several statistical tests. The results of
these tests prove to be very revealing regarding the different use of FCEXs in the two Chinese
translations. The computational modelling of the linguistic data would seem to indicate that among
other findings, while Liu’s use of archaic idioms has followed the general patterns of the original and
also of Yang’s work in the first half of Don Quijote I, noticeable variations begin to emerge in the
second half of Liu’s more recent version. Such an idiosyncratic use of archaisms by Liu, which may be
defined as style shifting or style variation, is then analyzed in quantitative terms through the application
of the proposed context-motivated theory (CMT). The results of applying the CMT-derived statistical
models show that the detected stylistic variation may well point to the internal consistency of the
translator in rendering the second half of Part I of the novel, which reflects his freer, more creative and
experimental style of translation. Through the introduction and testing of quantitative research methods
adapted from corpus linguistics and textual statistics, this thesis has made a major contribution to
methodological innovation in the study of style within the context of corpus-based translation studies
Identifying communicative functions in discourse with content types
Texts are not monolithic entities but rather coherent collections of micro illocutionary acts which help to convey a unitary message of content and purpose. Identifying such text segments is challenging because they require a fine-grained level of analysis even within a single sentence. At the same time, accessing them facilitates the analysis of the communicative functions of a text as well as the identification of relevant information. We propose an empirical framework for modelling micro illocutionary acts at clause level, that we call content types, grounded on linguistic theories of text types, in particular on the framework proposed by Werlich in 1976. We make available a newly annotated corpus of 279 documents (for a total of more than 180,000 tokens) belonging to different genres and temporal periods, based on a dedicated annotation scheme. We obtain an average Cohen’s kappa of 0.89 at token level. We achieve an average F1 score of 74.99% on the automatic classification of content types using a bi-LSTM model. Similar results are obtained on contemporary and historical documents, while performances on genres are more varied. This work promotes a discourse-oriented approach to information extraction and cross-fertilisation across disciplines through a computationally-aided linguistic analysis
Linguistically-Informed Neural Architectures for Lexical, Syntactic and Semantic Tasks in Sanskrit
The primary focus of this thesis is to make Sanskrit manuscripts more
accessible to the end-users through natural language technologies. The
morphological richness, compounding, free word orderliness, and low-resource
nature of Sanskrit pose significant challenges for developing deep learning
solutions. We identify four fundamental tasks, which are crucial for developing
a robust NLP technology for Sanskrit: word segmentation, dependency parsing,
compound type identification, and poetry analysis. The first task, Sanskrit
Word Segmentation (SWS), is a fundamental text processing task for any other
downstream applications. However, it is challenging due to the sandhi
phenomenon that modifies characters at word boundaries. Similarly, the existing
dependency parsing approaches struggle with morphologically rich and
low-resource languages like Sanskrit. Compound type identification is also
challenging for Sanskrit due to the context-sensitive semantic relation between
components. All these challenges result in sub-optimal performance in NLP
applications like question answering and machine translation. Finally, Sanskrit
poetry has not been extensively studied in computational linguistics.
While addressing these challenges, this thesis makes various contributions:
(1) The thesis proposes linguistically-informed neural architectures for these
tasks. (2) We showcase the interpretability and multilingual extension of the
proposed systems. (3) Our proposed systems report state-of-the-art performance.
(4) Finally, we present a neural toolkit named SanskritShala, a web-based
application that provides real-time analysis of input for various NLP tasks.
Overall, this thesis contributes to making Sanskrit manuscripts more accessible
by developing robust NLP technology and releasing various resources, datasets,
and web-based toolkit.Comment: Ph.D. dissertatio
- …