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.
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').
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.
Semantic representation that supports the choice of an appropriate connective between pairs of clauses inherently addresses discourse coherence, which is important for tasks such as narrative understanding, argumentation, and discourse parsing. We pr
opose a novel clause embedding method that applies graph learning to a data structure we refer to as a dependency-anchor graph. The dependency anchor graph incorporates two kinds of syntactic information, constituency structure, and dependency relations, to highlight the subject and verb phrase relation. This enhances coherence-related aspects of representation. We design a neural model to learn a semantic representation for clauses from graph convolution over latent representations of the subject and verb phrase. We evaluate our method on two new datasets: a subset of a large corpus where the source texts are published novels, and a new dataset collected from students' essays. The results demonstrate a significant improvement over tree-based models, confirming the importance of emphasizing the subject and verb phrase. The performance gap between the two datasets illustrates the challenges of analyzing student's written text, plus a potential evaluation task for coherence modeling and an application for suggesting revisions to students.
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.
This research deals with the role of the context in the rotation between the actual
forms of past, present and future, time is often divided into past, present and future
formulas, the research examines the contexts that leads the original time fro
m its position
to another time that does not indicate it in the first place that shows that there is a
difference between the temporal time and the grammatical, the research shows that the
formula of the present verb is different from that of the past because of the existence of a
presumption function and repent formula of the past about the present and future, which
does not indicate by the origin of the past but the present time and future, so the context
determines the grammar time.
With the increasing growth in popularity of Web services and SOA, discovering
relevant Web services becomes a significant challenge. The introduction of intentional
services is necessary to bridge the gap between low level, technical software-servi
ce
descriptions and high level, strategic expressions of business needs for services.
Current Web Services technology based on UDDI and WSDL does not make use of
this “intention” and therefore fails to address the problem of matching between capabilities
of services and business user needs.
This work addresses the problem of intentional semantic web service search in
Arabic, where a novel approach is proposed for partitioning user goals based on Arabic
verb ontology, in addition to showing a practical example about the effect of applying verb
ontology in intentional web service search.
This research aims to study the play of " The adventure of Al –Mamlouk Gabers head " according to the theory of verbal verb . This theory which cares to study the meaning that considers it the use . There are clauses if you speak them ,they don’t giv
e just word ,but they give actions at the same time ,and this what we are going to concentrate on in analyzing Saad ALah Wanous play .
In this play I tried to study it as possible as I could according to this theory and dividing the verbs in it according to their indicative fields .
