Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userBuilding the Turkish FrameNet
بناء framenet التركية
274
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
Framenet (Lowe، 1997؛ Baker et al.، 1998؛ Fillmore and Atkins، 1998؛ جونسون وآخرون، 2001) هو مشروع معجم حسابي يهدف إلى تقديم نظرة ثاقبة على العلاقات الدلالية بين المسندات والحجج.الاستخدامات في العديد من تطبيقات NLP، أثبتت Framenet نفسها كمورد قيم.يضع الهدف الرئيسي لهذه الدراسة مؤسسة بناء فرامين تركية شاملة ومتماسكة متوافقة مع موارد أخرى مثل Propbank (Kara et al.، 2020) أو Wordnet (Bakay et al.، 2019؛ Ehsani، 2018؛ Ehsani etal.، 2018؛ برلمان وآخرون، 2019؛ باكاي وآخرون، 2020) في اللغة التركية.
FrameNet (Lowe, 1997; Baker et al., 1998; Fillmore and Atkins, 1998; Johnson et al., 2001) is a computational lexicography project that aims to offer insight into the semantic relationships between predicate and arguments. Having uses in many NLP applications, FrameNet has proven itself as a valuable resource. The main goal of this study is laying the foundation for building a comprehensive and cohesive Turkish FrameNet that is compatible with other resources like PropBank (Kara et al., 2020) or WordNet (Bakay et al., 2019; Ehsani, 2018; Ehsani et al., 2018; Parlar et al., 2019; Bakay et al., 2020) in the Turkish language.
References used
https://aclanthology.org/
rate research
Read More
This paper deliberates on the process of building the first constituency-to-dependency conversion tool of Turkish. The starting point of this work is a previous study in which 10,000 phrase structure trees were manually transformed into Turkish from
the original PennTreebank corpus. Within the scope of this project, these Turkish phrase structure trees were automatically converted into UD-style dependency structures, using both a rule-based algorithm and a machine learning algorithm specific to the requirements of the Turkish language. The results of both algorithms were compared and the machine learning approach proved to be more accurate than the rule-based algorithm. The output was revised by a team of linguists. The refined versions were taken as gold standard annotations for the evaluation of the algorithms. In addition to its contribution to the UD Project with a large dataset of 10,000 Turkish dependency trees, this project also fulfills the important gap of a Turkish conversion tool, enabling the quick compilation of dependency corpora which can be used for the training of better dependency parsers.
FrameNet and the Multilingual FrameNet project have produced multilingual semantic annotations of parallel texts that yield extremely fine-grained typological insights. Moreover, frame semantic annotation of a wide cross-section of languages would pr
ovide information on the limits of Frame Semantics (Fillmore 1982, Fillmore1985). Multilingual semantic annotation offers critical input for research on linguistic diversity and recurrent patterns in computational typology. Drawing on results from FrameNet annotation of parallel texts, this paper proposes frame semantic annotation as a new component to complement the state of the art in computational semantic typology.
Currently, there are two available wordnets for Turkish: TR-wordnet of BalkaNet and KeNet. As the more comprehensive wordnet for Turkish, KeNet includes 76,757 synsets. KeNet has both intralingual semantic relations and is linked to PWN through inter
lingual relations. In this paper, we present the procedure adopted in creating KeNet, give details about our approach in annotating semantic relations such as hypernymy and discuss the language-specific problems encountered in these processes.
Although olfactory references play a crucial role in our cultural memory, only few works in NLP have tried to capture them from a computational perspective. Currently, the main challenge is not much the development of technological components for olf
actory information extraction, given recent advances in semantic processing and natural language understanding, but rather the lack of a theoretical framework to capture this information from a linguistic point of view, as a preliminary step towards the development of automated systems. Therefore, in this work we present the annotation guidelines, developed with the help of history scholars and domain experts, aimed at capturing all the relevant elements involved in olfactory situations or events described in texts. These guidelines have been inspired by FrameNet annotation, but underwent some adaptations, which are detailed in this paper. Furthermore, we present a case study concerning the annotation of olfactory situations in English historical travel writings describing trips to Italy. An analysis of the most frequent role fillers show that olfactory descriptions pertain to some typical domains such as religion, food, nature, ancient past, poor sanitation, all supporting the creation of a stereotypical imagery related to Italy. On the other hand, positive feelings triggered by smells are prevalent, and contribute to framing travels to Italy as an exciting experience involving all senses.
The study addressed the most important phases of Turkish foreign policy since the
establishment of the modern Turkish republic in 1923. And the principles govering it. Then
it focused on the new trends of foreign policy after the Jastice and develo
pment Islamic
rooted party came to power in Turkey 2002. Especially the strategic depth, reset problems
or zero problems with neighbors and new ottomanism.
The study concluded that the basic tenets which guided turkey's foreign policy since
the founding of the republic included caution and pragmatism.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
