Graph-based Arabic text semantic representation

Highlights

• Semantic representation of Arabic text can facilitate several natural language processing applications such as text summarization and textual entailment.

• A graph-based Arabic text semantic representation model were used to represent the meaning of Arabic sentences as a rooted acyclic graph.

• The proposed representation model was evaluated according to its ability to enhance Arabic Textual Entailment recognition.

• Arabic Textual Entailment Dataset (ArbTED) was used in the experiments, and the results showed the proposed model enhanced the performance of Arabic textual entailment recognition.

Abstract

Semantic representation reflects the meaning of the text as it may be understood by humans. Thus, it contributes to facilitating various automated language processing applications. Although semantic representation is very useful for several applications, a few models were proposed for the Arabic language. In that context, this paper proposes a graph-based semantic representation model for Arabic text. The proposed model aims to extract the semantic relations between Arabic words. Several tools and concepts have been employed such as dependency relations, part-of-speech tags, name entities, patterns, and Arabic language predefined linguistic rules. The core idea of the proposed model is to represent the meaning of Arabic sentences as a rooted acyclic graph. Textual entailment recognition challenge is considered in order to evaluate the ability of the proposed model to enhance other Arabic NLP applications. The experiments have been conducted using a benchmark Arabic textual entailment dataset, namely, ArbTED. The results proved that the proposed graph-based model is able to enhance the performance of the textual entailment recognition task in comparison to other baseline models. On average, the proposed model achieved 8.6%, 30.2%, 5.3% and 16.2% improvement in terms of accuracy, recall, precision, and F-score results, respectively.

Introduction

Semantics refers to the systematic representation of the knowledge in a sufficiently precise notation that can be used by computer programs (Hayes, 1974). The semantic relations between the text components help in better understanding of human language and in building more accurate automated cognitive systems. According to linguistics, semantics refers to the study of the relations between text components (words, statements, etc.) and their implicit signification (Abend & Rappoport, 2017), while semantic representation reflects the meaning of the text as it is understood by humans. Several applications utilize semantic representation to obtain better results in the computational linguistic area (e.g., machine translation and question answering). The core idea of the semantic representation is to develop specific and precise notations of the text that reflect its meaning.

The common techniques that are used to represent knowledge and semantic can be classified into four main groups: Predicate logic representation, Network representation, Frame representation, and Rule-based representation. In predicate logic representation, the sentences are split into words, and the semantic relations between words are defined using predicate logic notations. For instance, the statement Time is running is represented in the form of text as: running(time). Predicate logic is utilized to represent the semantic level of analysis for many languages such as English (Ali & Khan, 2009) and Urdu (Ali & Khan, 2010). The representation and retrieval complexities for complex sentences and the exclusion of supporting words (e.g., is) are the main drawbacks of this notation. In addition, predicate logic-based methods face difficulties when trying to represent ambiguous words that have different meanings (Ali & Khan, 2009). Network representation (i.e., a semantic network or semantic graph) was proposed by Quillian (1968) in 1968. It describes the text as a directed labeled graph in terms of vertices and edges. There are positive relationships between the amount of original text and the size of the semantic network and between its complexity and the time needed for the manipulation process. However, semantic networks are powerful and flexible knowledge representation techniques that could be used to model the semantic relation between text components. The frame representation is a data structure proposed by Minsky in 1974 that represents sentences as slots of objects that carry information Mylopoulos (1980). Splitting the original text into small slots and extracting their values are time-consuming processes that make the frame representation an inefficient knowledge representation. Furthermore, building the original sentence from its frame representation is very difficult (Ali & Khan, 2009). Finally, in the rule-based representation, a sentence is represented as a set of if-then rules. In rule-based systems, when a set of rules is satisfied, the system provides a solution without applying the remaining rules. Thus, the solution may differ from the solution provided when applying other rules. This allows rule-based representation to provide multiple representations of the same sentence, which makes the process of retrieving the original text from its rule-based representation a difficult task (Tayal, Raghuwanshi, & Malik, 2015).

The task of mapping the natural language text into its semantic representation is called semantic parsing. The mapping process parses the text into its semantic representation without syntactic classification of the texts components (Wilks & Fass, 1992). Semantic parsers have attracted a huge amount of attention in the field of Natural Language Processing (NLP) over the last few decades (Liang, 2016). Semantic parsers have been used to perform several NLP tasks such as Question Answering and Machine Translation. Semantic parsers have been categorized into two main types: shallow semantic parsers and deep semantic parsers. The shallow semantic parser labels each word in the original sentence according to its semantic role (Jurafsky & Martin, 2009).The deep semantic parser represents each composite component in the text depending on its meaning in the sentence (Liang & Potts, 2015).

This paper is organized as follows: In Section 2, the main objectives and goals of this research are described and listed. In Section 3,we briefly review the related work on knowledge representation and semantic representation for Arabic text. Section 4 describes the main features of the Arabic language that affect the semantic representation model. Section 5 describes the proposed model. Section 6 represents the process of building the proposed semantic graph. The experimental results are discussed in Section 7. Finally, the conclusion is presented in Section 8.