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Register a new userCreating Domain Dependent Turkish WordNet and SentiNet
إنشاء المجال المعتمدة التركية و STENTINET
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كلمة Wordnet هي عبارة سرية لديها قائمة منظم من الكلمات المنظمة حسب معانيها.تمثل WordNETS حواس الكلمات، وكل المعاني قد يكون ليممة واحدة، والعلاقات بين هذه الحواس، وتعاريفها.دراسة أخرى داخل مجال معالجة اللغة الطبيعية هي تحليل المعنويات.مع تحليل المعنويات، يمكن تسجيل مجموعات البيانات وفقا للعاطفة التي تحتوي عليها.في تحليل المعنويات، فعلنا بالبيانات التي تلقيناها على Wordnet السياحة، أجرينا دراسة تحليل المعنويات الخاصة بالمجال عن طريق التخلص من البيانات.في هذه الورقة، نقترح طريقة لتسهيل مهام معالجة اللغة الطبيعية مثل تحليل المعنويات التي يتم إجراؤها في مجالات محددة عبر إنشاء مجموعة فرعية مجال محددة من قاموس تركي أصلي.كدراسة أولية، أنشأنا Wordnet للمجال السياحي مع 14000 كلمة وتقوم بتحقق من صحةها في مهام بسيطة.
A WordNet is a thesaurus that has a structured list of words organized depending on their meanings. WordNet represents word senses, all meanings a single lemma may have, the relations between these senses, and their definitions. Another study within the domain of Natural Language Processing is sentiment analysis. With sentiment analysis, data sets can be scored according to the emotion they contain. In the sentiment analysis we did with the data we received on the Tourism WordNet, we performed a domain-specific sentiment analysis study by annotating the data. In this paper, we propose a method to facilitate Natural Language Processing tasks such as sentiment analysis performed in specific domains via creating a specific-domain subset of an original Turkish dictionary. As the preliminary study, we have created a WordNet for the tourism domain with 14,000 words and validated it on simple tasks.
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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.
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 app
lications, 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.
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.
Building tools to remove sensitive information such as personal names, addresses, and telephone numbers - so called Protected Health Information (PHI) - from clinical free text is an important task to make clinical texts available for research. These
de-identification tools must be assessed regarding their quality in the form of the measurements precision and re- call. To assess such tools, gold standards - annotated clinical text - must be available. Such gold standards exist for larger languages. For Norwegian, how- ever, there are no such resources. Therefore, an already existing Norwegian synthetic clinical corpus, NorSynthClinical, has been extended with PHIs and annotated by two annotators, obtaining an inter-annotator agreement of 0.94 F1-measure. In total, the corpus has 409 annotated PHI instances and is called NorSynthClinical PHI. A de-identification hybrid tool (machine learning and rule-based meth- ods) for Norwegian was developed and trained with open available resources, and obtained an overall F1-measure of 0.73 and a recall of 0.62, when tested using NorSynthClinical PHI. NorSynthClinical PHI is made open and available at Github to be used by the research community.
This study aimed to reveal changes in (Syrian- Turkish) relation ,though this relation is too sensitive, the study separated into two phases:
-First: covers the rapprochement period between the two countries, till the year 2011.
- Second: covers th
e Syrian crisis period when the two countries diverged.
As a result, we see that Turkish –Syrian relations kept on developing in all fields in the first phase till the strategic cooperation in 2009 between them . but when the Syrian crisis happened, turkey found that the change which might happen in Syria could bring allies of The AKP (Muslim Brotherhood ),that means retake the old Turkish strategic domain , that cause stress and regress in the relation between Turkey and Syria, Turkey has started to plan to control and intervene in internal Syrian matters, it has been started by putting economic sanctions on Syria which had abad effect on the standard of living for Syrian inhabitants, then it started to go deeper in its intervention by planning to destroy Syrian industry and stealing industrial companies and factories ,especially in Aleppo.
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