An Unsupervised Language-Independent Entity Disambiguation Method and its Evaluation on the English and Persian Languages

Entity Linking is one of the essential tasks of information extraction and natural language understanding. Entity linking mainly consists of two tasks: recognition and disambiguation of named entities. Most studies address these two tasks separately or focus only on one of them. Moreover, most of the state-of-the -art entity linking algorithms are either supervised, which have poor performance in the absence of annotated corpora or language-dependent, which are not appropriate for multi-lingual applications. In this paper, we introduce an Unsupervised Language-Independent Entity Disambiguation (ULIED), which utilizes a novel approach to disambiguate and link named entities. Evaluation of ULIED on different English entity linking datasets as well as the only available Persian dataset illustrates that ULIED in most of the cases outperforms the state-of-the-art unsupervised multi-lingual approaches.

PERLEX: A Bilingual Persian-English Gold Dataset for Relation Extraction

Relation extraction is the task of extracting semantic relations between entities in a sentence. It is an essential part of some natural language processing tasks such as information extraction, knowledge extraction, and knowledge base population. The main motivations of this research stem from a lack of a dataset for relation extraction in the Persian language as well as the necessity of extracting knowledge from the growing big-data in the Persian language for different applications. In this paper, we present "PERLEX" as the first Persian dataset for relation extraction, which is an expert-translated version of the "Semeval-2010-Task-8" dataset. Moreover, this paper addresses Persian relation extraction utilizing state-of-the-art language-agnostic algorithms. We employ six different models for relation extraction on the proposed bilingual dataset, including a non-neural model (as the baseline), three neural models, and two deep learning models fed by multilingual-BERT contextual word representations. The experiments result in the maximum f-score 77.66% (provided by BERTEM-MTB method) as the state-of-the-art of relation extraction in the Persian language.

FarsBase-KBP: A Knowledge Base Population System for the Persian Knowledge Graph

While most of the knowledge bases already support the English language, there is only one knowledge base for the Persian language, known as FarsBase, which is automatically created via semi-structured web information. Unlike English knowledge bases such as Wikidata, which have tremendous community support, the population of a knowledge base like FarsBase must rely on automatically extracted knowledge. Knowledge base population can let FarsBase keep growing in size, as the system continues working. In this paper, we present a knowledge base population system for the Persian language, which extracts knowledge from unlabeled raw text, crawled from the Web. The proposed system consists of a set of state-of-the-art modules such as an entity linking module as well as information and relation extraction modules designed for FarsBase. Moreover, a canonicalization system is introduced to link extracted relations to FarsBase properties. Then, the system uses knowledge fusion techniques with minimal intervention of human experts to integrate and filter the proper knowledge instances, extracted by each module. To evaluate the performance of the presented knowledge base population system, we present the first gold dataset for benchmarking knowledge base population in the Persian language, which consisting of 22015 FarsBase triples and verified by human experts. The evaluation results demonstrate the efficiency of the proposed system.

ParsEL 1.0: Unsupervised Entity Linking in Persian Social Media Texts

In recent years, social media data has exponentially increased, which can be enumerated as one of the largest data repositories in the world. A large portion of this social media data is natural language text. However, the natural language is highly ambiguous due to exposure to the frequent occurrences of entities, which have polysemous words or phrases. Entity linking is the task of linking the entity mentions in the text to their corresponding entities in a knowledge base. Recently, FarsBase, a Persian knowledge graph, has been introduced containing almost half a million entities. In this paper, we propose an unsupervised Persian Entity Linking system, the first entity linking system specially focused on the Persian language, which utilizes context-dependent and context-independent features. For this purpose, we also publish the first entity linking corpus of the Persian language containing 67,595 words that have been crawled from social media texts of some popular channels in the Telegram messenger. The output of the proposed method is 86.94% f-score for the Persian language, which is comparable with the similar state-of-the-art methods in the English language.