## 2 Related Work
In short, these approaches rely on the homogeneity of concept in multilingual text or domain semantics, and sentence-based documents or complex concepts may cause semantic loss among different contexts through the above state-of-art approaches. **Semantic Representation**: It defines the annotation to construct syntactic structure such as FrameNet [29] and Semlink [30], but focuses on argument out of other relations. In this context, there are several available semantic representation approaches. For instance, universal networking language (UNL) proposes independent language representation so that sentences inputted in any language can be translated into any other natural language. Abstract meaning representation (AMR) [9] proposes a relatively more straightforward sentence-level semantic parser to cover semantic role broad predictions. AMR manually an notates sentences and utilizes PropBank frames [31] to represent the semantic relationship between words. However, AMR faces difficulties across translation because the syntactical similarity is not suitable cross-linguistically [32]. Therefore, new multilayered solutions such as universal concept cognitive annotation (UCCA) [10] and universal decompositional semantics (UDS) [33] are applied in cross text for semantic annotation and word senses by BabelNet [34] and Open Multilingual Wordnet ([[http://compling.hss.ntu.edu.sg/omw/](http://compling.hss.ntu.edu.sg/omw/)]([http://compling.hss.ntu.edu.sg/omw/](http://compling.hss.ntu.edu.sg/omw/))). They constructed substantial multilingual semantic nets to achieve universality by connecting resources such as WordNet and Wikipedia. The method adapts linguistic theory to build a manual and multilingual scheme. However, UCCA annotates short sentences (e.g., multiword expressions) where the same multiword or entity is annotated in many different sentences. Groningen Meaning Bank (GMB) is a new solution to integrate language phenomena into a single formalism instead of covering single phenomena in an isolated way. Additionally, universal dependencies (UD) [12] build cross text dependency-based annotations for multilingual sentences. Most of the semantic representation methods use simple concepts such as UCCA, but some other methods adapt concepts such as WordNet synsets for UNL and PropBank frames for AMR. Furthermore, UNL has its relationships set while AMR uses PropBank relationships. UNL, UCCA, and AMR are fully manual annotated, but GMB produces meaning representations automatically and can be corrected by experts. However, such approaches (e.g., AMR, UCCA, GMB, and UNL) focus on lexical-semantic or multilingual words rather than on sentence semantics and cannot guarantee sentence-based semantic representation to be universal and unambiguous across languages. Most of the proposed semantic representation methods do not consider the morphological and syntactic characteristics of the language in the construction of sentence-level semantic labeling. Contributions made in the semantic representation of any language text will utilize the translated English resources, which may negatively affect the performance of other semantic representation methods. In our research work, MParser propose a universal semantic representation to extract semantic relationships from local language text using local language tools and resources, such as Stanford Parser. In addition, the proposed parser takes into account the syntactic and morphological features of a given sentence. It is worth noting that the proposed MParser model uses various tools, resources, and text features to reduce the negative impact of resource quality on semantic representation. Moreover, MParser achieves a universal representation and semantic consistency across languages.