Universal dependencies (UD) is a framework for morphosyntactic annotation of human language, which to date has been used to create treebanks for more than 100 languages. In this article, we outline the linguistic theory of the UD framework, which draws on a long tradition of typologically oriented grammatical theories. Grammatical relations between words are centrally used to explain how predicate–argument structures are encoded morphosyntactically in different languages while morphological features and part-of-speech classes give the properties of words. We argue that this theory is a good basis for cross-linguistically consistent annotation of typologically diverse languages in a way that supports computational natural language understanding as well as broader linguistic studies.
The hand, one of the most versatile but mechanically redundant parts of the human body, must overcome imperfect motor commands and inherent noise in both the sensory and motor systems in order to produce desired motor actions. For example, it is nearly impossible to produce a perfectly consistent note during a single violin stroke or to produce the exact same note over multiple strokes, which we denote online and offline control, respectively. To overcome these challenges, the central nervous system synergistically integrates multiple sensory modalities and coordinates multiple motor effectors. Among these sensory modalities, tactile sensation plays an important role in manual motor tasks by providing hand-object contact information. The purpose of this study was to investigate the role of tactile feedback in individual finger actions and multi-finger interactions during constant force production tasks. We developed analytical techniques for the linear decomposition of the overall variance in the motor system in both online and offline control. We removed tactile feedback from the fingers and demonstrated that tactile sensors played a critical role in the online control of synergistic interactions between fingers. In contrast, the same sensors did not contribute to offline control. We also demonstrated that when tactile feedback was removed from the fingers, the combined motor output of individual fingers did not change while individual finger behaviors did. This finding supports the idea of hierarchical control where individual fingers at the lower level work together to stabilize the performance of combined motor output at the higher level.
Objectives To evaluate implant survival rate, any complications, and changes in residual alveolar bone height (RABH) using saline or platelet‐rich fibrin (PRF) filling after hydraulic transcrestal sinus lifting. Methods Dental implants were placed after hydraulic transcrestal sinus lifting and the filling of saline (20 patients) or PRF (20 patients). Outcome measurements were implant survival, any complications, and RABH changes. Cone‐beam computed tomography (CBCT) scans were taken and compared preoperatively (T0), immediately postoperatively (T1), at 3 months (T2), 6 months (T3), and 12 months postoperatively (T4), respectively. Results In a total of 40 patients, 45 implants with a mean length of 10.4 ± 0.8 mm were placed in posterior maxilla of a mean RABH of 6.8 ± 1.1 mm. The increase in RABH peaked at T1, and continuous drooping of the sinus membrane was observed but stabilized at T3. Meanwhile, the gradual increase in the radiopacities was found below the lifted sinus membrane. The PRF filling induced the radiographic intrasinus bone gain of 2.6 ± 1.1 mm, which was significantly more than 1.7 ± 1.0 mm of saline filling at T4 (p < .05). All the implants were in function with no significant complications over the one‐year follow‐up period. Conclusions In this randomized case–control study, the feasibility of hydraulic transcrestal sinus lifting without bone graft was confirmed and PRF might be a better filler to support the elevated sinus membrane. However, adjunctive bone grafting should still be indicated for cases requiring more than 2–3 mm of intrasinus bone gain.
Research on distributed word representations is focused on widely-used languages such as English. Although the same methods can be used for other languages, language-specific knowledge can enhance the accuracy and richness of word vector representations. In this paper, we look at improving distributed word representations for Korean using knowledge about the unique linguistic structure of Korean. Specifically, we decompose Korean words into the jamo level, beyond the characterlevel, allowing a systematic use of subword information. To evaluate the vectors, we develop Korean test sets for word similarity and analogy and make them publicly available. The results show that our simple method outperforms word2vec and character-level Skip-Grams on semantic and syntactic similarity and analogy tasks and contributes positively toward downstream NLP tasks such as sentiment analysis.
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