Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userPredicting Informativeness of Semantic Triples
التنبؤ بصلاحية ثلاثة أضعاف
211
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
العديد من أدوات استخراج العلاقات الدلالية التلقائي استخراج ثلاث مرات من النص غير منظم.ومع ذلك، فإن كمية كبيرة من هذه الثلاثي هي فقط تمثل المعرفة الأساسية.نستكشف استخدام النصوص الكاملة للمنشورات الطبية الحيوية لإنشاء كائن تدريبي ثلاثي ثلاث مرات دهالية مفيدة ومهمة تستند إلى فكرة أن الاشتراكات الرئيسية للمقال ملخصة في مجردة لها.يتم استخدام هذه الشورقة لتدريب مصنف التعلم العميق لتحديد ثلاثة أضعاف ثلاثة أضعاف، ونقترح أن يتم توليد ترتيب الأهمية الثلاثية الدلالية.
Many automatic semantic relation extraction tools extract subject-predicate-object triples from unstructured text. However, a large quantity of these triples merely represent background knowledge. We explore using full texts of biomedical publications to create a training corpus of informative and important semantic triples based on the notion that the main contributions of an article are summarized in its abstract. This corpus is used to train a deep learning classifier to identify important triples, and we suggest that an importance ranking for semantic triples could also be generated.
References used
https://aclanthology.org/
rate research
Read More
This research aims to predict the level of air pollution with a set of data used to make predictions through them and to obtain the best prediction using several models and compare them and find the appropriate solution.
Automatically extracting interpersonal relationships of conversation interlocutors can enrich personal knowledge bases to enhance personalized search, recommenders and chatbots. To infer speakers' relationships from dialogues we propose PRIDE, a neur
al multi-label classifier, based on BERT and Transformer for creating a conversation representation. PRIDE utilizes dialogue structure and augments it with external knowledge about speaker features and conversation style.Unlike prior works, we address multi-label prediction of fine-grained relationships. We release large-scale datasets, based on screenplays of movies and TV shows, with directed relationships of conversation participants. Extensive experiments on both datasets show superior performance of PRIDE compared to the state-of-the-art baselines.
We present a manually annotated lexical semantic change dataset for Russian: RuShiftEval. Its novelty is ensured by a single set of target words annotated for their diachronic semantic shifts across three time periods, while the previous work either
used only two time periods, or different sets of target words. The paper describes the composition and annotation procedure for the dataset. In addition, it is shown how the ternary nature of RuShiftEval allows to trace specific diachronic trajectories: changed at a particular time period and stable afterwards' or was changing throughout all time periods'. Based on the analysis of the submissions to the recent shared task on semantic change detection for Russian, we argue that correctly identifying such trajectories can be an interesting sub-task itself.
Transfer learning methods, and in particular domain adaptation, help exploit labeled data in one domain to improve the performance of a certain task in another domain. However, it is still not clear what factors affect the success of domain adaptatio
n. This paper models adaptation success and selection of the most suitable source domains among several candidates in text similarity. We use descriptive domain information and cross-domain similarity metrics as predictive features. While mostly positive, the results also point to some domains where adaptation success was difficult to predict.
This study recruited 51 elders aged 53-74 to discuss their daily activities in focus groups. The transcribed discourse was analyzed using the Chinese version of LIWC (Lin et al., 2020; Pennebaker et al., 2015) for cognitive complexity and dynamic lan
guage as well as content words related to elders' daily activities. The interruption behavior during the conversation was also coded and analyzed. After controlling for education, gender and age, the results showed that cognitive flexibility performance was accompanied by the increasing adoption of dynamic language, insight words and family words. These findings serve as the basis for predicting elders' cognitive flexibility through their daily language use.
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
