## 1 Introduction
The reasons for causing \(M_{1}\
eq_{\text{s}}M_{2}\) (\"\(\
eq_{\text{s}}\)\" refers to not semantically equal) include:  
1. Heterogeneous grammatical rules: The language grammars of the components in \(E_{i}\) and \(C_{i}\) have their own rules to generate a sentence and it is impossible to achieve a one-to-one mapping. 2. Synonyms and homonyms: Each term in \(E_{i}\) may have several synonyms or homonyms. A wrong term in meaning may cause semantic ambiguity. 3. Peculiar language phenomena: Some phenomena in \(E_{i}\) never appear in \(C_{i}\), resulting in asymmetric mapping. For example, the particles of \"\(\xleftarrow{\}\), \(\xleftarrow{\}\), \(\xleftarrow{\}\), \(\xleftarrow{\}\)\" in Japanese do not have counterparts in Chinese. Therefore, the same sentence will produce completely different scenarios in a heterogeneous text, and the original meaning in mind may be shifted to another meaning. The above problems are called semantic shift problems that change a sentence's original meaning in multilingual semantic representation. Moreover, in natural language texts, users  
Figure 1: Complex document interaction between computers and users. cannot express their information needs in a computer-understandable way or interpret the representation correctly due to problems in representing complex semantics. Therefore, the development of a novel model has been motivated by the following aspects:  
1. Computer-human-understandable representation: providing information understandable by both computers and humans, realizing the accurate interpretation of sentences in the human-computer messaging cycle of humans and computers without ambiguity. 2. Accurate semantic representation among computing applications: applying computer-human-understandable information in computing applications and enabling information to be semantically interoperable. 3. Automated multilingual information processing by software agents: allowing multilingual information to be automatically processed across domains and contexts. Thus, this research proposes a new multilingual semantic representation parser for sentence-based text or documents that enhances textual representation and reduces multilingual ambiguity. Based on our previous conceptual work [16], we propose a novel Machine Natural Language Parser (Mparser) to realize universal representations between computers and users unambiguously. The explainable MParser parses a simple English sentence, resolving complex concepts towards a bag of universal concepts sentence-readable and -understandable for any heterogeneous information, and mediates contextual human natural languages collaboratively, as shown in Figure 2. The universal concepts sentence shares a common concept at both the syntactic and semantic levels between users and computers. To achieve consistency and universal representation, _MParser_ designs from human input and sentence generation:  
1. In the human input, each unique concept is collaboratively edited with SIM [15] based on a common dictionary (CoDic) [17] for eliminating atomic concept ambiguity and morphological features. Thus, a simple English sentence can be converted to a sequence of unique concepts across conversational contexts. 2. To maintain complex semantic concept consistency between computers and users, an MParser for English sentences parses the semantic roles between English words and represents them for deriving a unique concept that can be accurately represented and understood by computers through case grammar [16]. The cases are used to label words, which are aligned from local language perspectives. The proposed parser utilizes powerful linguistic tools such as Stanford Parser and universal dependency relations. 3. Evaluate the proposed MParser through annotator agreement between the expert's case labeling and MParser's outputs. Additionally, 154 non-expert participants investigated judgments of semantic expressiveness. The rest of this paper is organized as follows. Section 2 compares the proposed approach with related work.