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Register a new userWhere Do Aspectual Variants of Light Verb Constructions Belong?
أين تنتمي المتغيرات الجوفية من أعمال الفعل الخفيفة؟
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التعبيرات ذات البديل الجيري لفعل ضوء، على سبيل المثالتأخذ على الديون "مقابل الديون"، متكررة في النصوص، ولكن غالبا ما يصعب تصنيفها بين التعابير اللفظية أو الإنشاءات الفعلية أو العبارات التركيبية.نحقق في خصائص هذه التعبيرات مع عضوية متنازع عليها واقتراح مجموعة مختارة من الميزات التي تحدد حدود أكثر مرضية بين الفئات الثلاث في هذه المنطقة، وتعيين التعبيرات إلى أحدهم.
Expressions with an aspectual variant of a light verb, e.g. take on debt' vs. have debt', are frequent in texts but often difficult to classify between verbal idioms, light verb constructions or compositional phrases. We investigate the properties of such expressions with a disputed membership and propose a selection of features that determine more satisfactory boundaries between the three categories in this zone, assigning the expressions to one of them.
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The paper reports on a corpus study of German light verb constructions (LVCs). LVCs come in families which exemplify systematic interpretation patterns. The paper's aim is to account for the properties determining these patterns on the basis of a corpus study on German LVCs of the type stehen unter' NP' (stand under NP').
Discourse parsers recognize the intentional and inferential relationships that organize extended texts. They have had a great influence on a variety of NLP tasks as well as theoretical studies in linguistics and cognitive science. However it is often
difficult to achieve good results from current discourse models, largely due to the difficulty of the task, particularly recognizing implicit discourse relations. Recent developments in transformer-based models have shown great promise on these analyses, but challenges still remain. We present a position paper which provides a systematic analysis of the state of the art discourse parsers. We aim to examine the performance of current discourse parsing models via gradual domain shift: within the corpus, on in-domain texts, and on out-of-domain texts, and discuss the differences between the transformer-based models and the previous models in predicting different types of implicit relations both inter- and intra-sentential. We conclude by describing several shortcomings of the existing models and a discussion of how future work should approach this problem.
We present here the results of a morphosemantic analysis of the verb-noun pairs in the Princeton WordNet as reflected in the standoff file containing pairs annotated with a set of 14 semantic relations. We have automatically distinguished between zer
o-derivation and affixal derivation in the data and identified the affixes and manually checked the results. The data show that for each semantic relation an affix prevails in creating new words, although we cannot talk about their specificity with respect to such a relation. Moreover, certain pairs of verb-noun semantic primes are better represented for each semantic relation, and some semantic clusters (in the form of WordNet subtrees) take shape as a result. We thus employ a large-scale data-driven linguistically motivated analysis afforded by the rich derivational and morphosemantic description in WordNet to the end of capturing finer regularities in the process of derivation as represented in the semantic properties of the words involved and as reflected in the structure of the lexicon.
This research is done to identify the structures of the verb in Ugaritic Language in a
comparative study in Arabic.
This study shows that the verb in Ugaritic Language is studied in terms of its
derivative “root”, its original characters “abstract
ion”, its extra characters , suffixes,
prefixes and, infix , its form and metres “inflection”, its syntactic forms , and its tenses
that it indicates.
Also this study shows that the verb in Ugaritic Language is studied by looking into
its meaning whether it is an intransitive or transitive verb. In addition to study the relations
“syntactic functions” in the Ugaritic sentence.
Many recent works have demonstrated that unsupervised sentence representations of neural networks encode syntactic information by observing that neural language models are able to predict the agreement between a verb and its subject. We take a critic
al look at this line of research by showing that it is possible to achieve high accuracy on this agreement task with simple surface heuristics, indicating a possible flaw in our assessment of neural networks' syntactic ability. Our fine-grained analyses of results on the long-range French object-verb agreement show that contrary to LSTMs, Transformers are able to capture a non-trivial amount of grammatical structure.
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