scholarly journals Towards relation extraction from Arabic text: a review

2019 ◽  
Vol 5 (5) ◽  
pp. 212-215
Author(s):  
Abeer AlArfaj
Keyword(s):  
Language Processing ◽  
Question Answering ◽  
State Of The Art ◽  
Relation Extraction ◽  
Arabic Language ◽  
Extraction Methods ◽  
The State ◽  
Semantic Relations ◽  
Arabic Text ◽  
Taxonomic Relation

Semantic relation extraction is an important component of ontologies that can support many applications e.g. text mining, question answering, and information extraction. However, extracting semantic relations between concepts is not trivial and one of the main challenges in Natural Language Processing (NLP) Field. The Arabic language has complex morphological, grammatical, and semantic aspects since it is a highly inflectional and derivational language, which makes task even more challenging. In this paper, we present a review of the state of the art for relation extraction from texts, addressing the progress and difficulties in this field. We discuss several aspects related to this task, considering the taxonomic and non-taxonomic relation extraction methods. Majority of relation extraction approaches implement a combination of statistical and linguistic techniques to extract semantic relations from text. We also give special attention to the state of the work on relation extraction from Arabic texts, which need further progress.

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

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Majid Asgari-Bidhendi ◽  
Mehrdad Nasser ◽  
Behrooz Janfada ◽  
Behrouz Minaei-Bidgoli
Keyword(s):  
Language Processing ◽  
Question Answering ◽  
State Of The Art ◽  
Relation Extraction ◽  
Neural Model ◽  
Semantic Relations ◽  
Base Population ◽  
Neural Models ◽  
Persian Language ◽  
Knowledge Base Population

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, question answering, 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 F1-score of 77.66% (provided by BERTEM-MTB method) as the state of the art of relation extraction in the Persian language.

scholarly journals Location Analysis for Arabic COVID-19 Twitter Data Using Enhanced Dialect Identification Models

2021 ◽  
Vol 11 (23) ◽  
pp. 11328
Author(s):  
Nader Essam ◽  
Abdullah M. Moussa ◽  
Khaled M. Elsayed ◽  
Sherif Abdou ◽  
Mohsen Rashwan ◽  
...  
Keyword(s):  
Social Media ◽  
Language Processing ◽  
State Of The Art ◽  
Weighted Average ◽  
Arabic Language ◽  
Arab Countries ◽  
The State ◽  
Identification Accuracy ◽  
Language Models ◽  
Twitter Data

The recent surge of social media networks has provided a channel to gather and publish vital medical and health information. The focal role of these networks has become more prominent in periods of crisis, such as the recent pandemic of COVID-19. These social networks have been the leading platform for broadcasting health news updates, precaution instructions, and governmental procedures. They also provide an effective means for gathering public opinion and tracking breaking events and stories. To achieve location-based analysis for social media input, the location information of the users must be captured. Most of the time, this information is either missing or hidden. For some languages, such as Arabic, the users’ location can be predicted from their dialects. The Arabic language has many local dialects for most Arab countries. Natural Language Processing (NLP) techniques have provided several approaches for dialect identification. The recent advanced language models using contextual-based word representations in the continuous domain, such as BERT models, have provided significant improvement for many NLP applications. In this work, we present our efforts to use BERT-based models to improve the dialect identification of Arabic text. We show the results of the developed models to recognize the source of the Arabic country, or the Arabic region, from Twitter data. Our results show 3.4% absolute enhancement in dialect identification accuracy on the regional level over the state-of-the-art result. When we excluded the Modern Standard Arabic (MSA) set, which is formal Arabic language, we achieved 3% absolute gain in accuracy between the three major Arabic dialects over the state-of-the-art level. Finally, we applied the developed models on a recently collected resource for COVID-19 Arabic tweets to recognize the source country from the users’ tweets. We achieved a weighted average accuracy of 97.36%, which proposes a tool to be used by policymakers to support country-level disaster-related activities.

Named Entity Recognition and Relation Extraction

2021 ◽  
Vol 54 (1) ◽  
pp. 1-39
Author(s):  
Zara Nasar ◽  
Syed Waqar Jaffry ◽  
Muhammad Kamran Malik
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
Relation Extraction ◽  
The State ◽  
Entity Recognition ◽  
Joint Models ◽  
Named Entity ◽  
Textual Data ◽  
Benchmark Datasets

With the advent of Web 2.0, there exist many online platforms that result in massive textual-data production. With ever-increasing textual data at hand, it is of immense importance to extract information nuggets from this data. One approach towards effective harnessing of this unstructured textual data could be its transformation into structured text. Hence, this study aims to present an overview of approaches that can be applied to extract key insights from textual data in a structured way. For this, Named Entity Recognition and Relation Extraction are being majorly addressed in this review study. The former deals with identification of named entities, and the latter deals with problem of extracting relation between set of entities. This study covers early approaches as well as the developments made up till now using machine learning models. Survey findings conclude that deep-learning-based hybrid and joint models are currently governing the state-of-the-art. It is also observed that annotated benchmark datasets for various textual-data generators such as Twitter and other social forums are not available. This scarcity of dataset has resulted into relatively less progress in these domains. Additionally, the majority of the state-of-the-art techniques are offline and computationally expensive. Last, with increasing focus on deep-learning frameworks, there is need to understand and explain the under-going processes in deep architectures.

Events Automatic Extraction from Arabic Texts

2020 ◽  
pp. 1686-1704
Author(s):  
Emna Hkiri ◽  
Souheyl Mallat ◽  
Mounir Zrigui
Keyword(s):  
Information Retrieval ◽  
Natural Language Processing ◽  
Text Mining ◽  
Machine Translation ◽  
Language Processing ◽  
Question Answering ◽  
Arabic Language ◽  
Event Extraction ◽  
Mining Machine ◽  
Open Domain

The event extraction task consists in determining and classifying events within an open-domain text. It is very new for the Arabic language, whereas it attained its maturity for some languages such as English and French. Events extraction was also proved to help Natural Language Processing tasks such as Information Retrieval and Question Answering, text mining, machine translation etc… to obtain a higher performance. In this article, we present an ongoing effort to build a system for event extraction from Arabic texts using Gate platform and other tools.

scholarly journals Unsupervised Keyphrase Extraction for Web Pages

2019 ◽  
Vol 3 (3) ◽  
pp. 58 ◽  
Author(s):  
Tim Haarman ◽  
Bastiaan Zijlema ◽  
Marco Wiering
Keyword(s):  
Language Processing ◽  
State Of The Art ◽  
Structural Information ◽  
Extraction Methods ◽  
Web Pages ◽  
Keyphrase Extraction ◽  
Text Documents ◽  
Normal Text ◽  
Textual Data ◽  
Novel Method

Keyphrase extraction is an important part of natural language processing (NLP) research, although little research is done in the domain of web pages. The World Wide Web contains billions of pages that are potentially interesting for various NLP tasks, yet it remains largely untouched in scientific research. Current research is often only applied to clean corpora such as abstracts and articles from academic journals or sets of scraped texts from a single domain. However, textual data from web pages differ from normal text documents, as it is structured using HTML elements and often consists of many small fragments. These elements are furthermore used in a highly inconsistent manner and are likely to contain noise. We evaluated the keyphrases extracted by several state-of-the-art extraction methods and found that they did not transfer well to web pages. We therefore propose WebEmbedRank, an adaptation of a recently proposed extraction method that can make use of structural information in web pages in a robust manner. We compared this novel method to other baselines and state-of-the-art methods using a manually annotated dataset and found that WebEmbedRank achieved significant improvements over existing extraction methods on web pages.

scholarly journals Learning to Transfer Relational Representations through Analogy

2019 ◽  
Vol 33 ◽  
pp. 10015-10016
Author(s):  
Gaetano Rossiello ◽  
Alfio Gliozzo ◽  
Michael Glass
Keyword(s):  
State Of The Art ◽  
Relation Extraction ◽  
Knowledge Bases ◽  
The State ◽  
Large Set ◽  
Relational Information ◽  
Siamese Network ◽  
Distant Supervision ◽  
Novel Approach ◽  
Art Methods

We propose a novel approach to learn representations of relations expressed by their textual mentions. In our assumption, if two pairs of entities belong to the same relation, then those two pairs are analogous. We collect a large set of analogous pairs by matching triples in knowledge bases with web-scale corpora through distant supervision. This dataset is adopted to train a hierarchical siamese network in order to learn entity-entity embeddings which encode relational information through the different linguistic paraphrasing expressing the same relation. The model can be used to generate pre-trained embeddings which provide a valuable signal when integrated into an existing neural-based model by outperforming the state-of-the-art methods on a relation extraction task.

scholarly journals Title-Guided Encoding for Keyphrase Generation

2019 ◽  
Vol 33 ◽  
pp. 6268-6275 ◽  
Author(s):  
Wang Chen ◽  
Yifan Gao ◽  
Jiani Zhang ◽  
Irwin King ◽  
Michael R. Lyu
Keyword(s):  
Language Processing ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Relevant Information ◽  
The State ◽  
Main Body ◽  
Generation Task ◽  
Leading Role ◽  
Very High

Keyphrase generation (KG) aims to generate a set of keyphrases given a document, which is a fundamental task in natural language processing (NLP). Most previous methods solve this problem in an extractive manner, while recently, several attempts are made under the generative setting using deep neural networks. However, the state-of-the-art generative methods simply treat the document title and the document main body equally, ignoring the leading role of the title to the overall document. To solve this problem, we introduce a new model called Title-Guided Network (TG-Net) for automatic keyphrase generation task based on the encoderdecoder architecture with two new features: (i) the title is additionally employed as a query-like input, and (ii) a titleguided encoder gathers the relevant information from the title to each word in the document. Experiments on a range of KG datasets demonstrate that our model outperforms the state-of-the-art models with a large margin, especially for documents with either very low or very high title length ratios.

scholarly journals Using Natural Language Processing and Artificial Intelligence to Explore the Nutrition and Sustainability of Recipes and Food

2021 ◽  
Vol 3 ◽  
Author(s):  
Marieke van Erp ◽  
Christian Reynolds ◽  
Diana Maynard ◽  
Alain Starke ◽  
Rebeca Ibáñez Martín ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Domain Knowledge ◽  
State Of The Art ◽  
Interdisciplinary Approach ◽  
Comprehensive Analysis ◽  
The State ◽  
Use Cases

In this paper, we discuss the use of natural language processing and artificial intelligence to analyze nutritional and sustainability aspects of recipes and food. We present the state-of-the-art and some use cases, followed by a discussion of challenges. Our perspective on addressing these is that while they typically have a technical nature, they nevertheless require an interdisciplinary approach combining natural language processing and artificial intelligence with expert domain knowledge to create practical tools and comprehensive analysis for the food domain.

scholarly journals A Constraint-Based Hypergraph Partitioning Approach to Coreference Resolution

2013 ◽  
Vol 39 (4) ◽  
pp. 847-884 ◽  
Author(s):  
Emili Sapena ◽  
Lluís Padró ◽  
Jordi Turmo
Keyword(s):  
Language Processing ◽  
State Of The Art ◽  
The State ◽  
Classification Model ◽  
Coreference Resolution ◽  
World Knowledge ◽  
Relaxation Labeling ◽  
Hypergraph Partitioning ◽  
Lack Of Information ◽  
New Information

This work is focused on research in machine learning for coreference resolution. Coreference resolution is a natural language processing task that consists of determining the expressions in a discourse that refer to the same entity. The main contributions of this article are (i) a new approach to coreference resolution based on constraint satisfaction, using a hypergraph to represent the problem and solving it by relaxation labeling; and (ii) research towards improving coreference resolution performance using world knowledge extracted from Wikipedia. The developed approach is able to use an entity-mention classification model with more expressiveness than the pair-based ones, and overcome the weaknesses of previous approaches in the state of the art such as linking contradictions, classifications without context, and lack of information evaluating pairs. Furthermore, the approach allows the incorporation of new information by adding constraints, and research has been done in order to use world knowledge to improve performances. RelaxCor, the implementation of the approach, achieved results at the state-of-the-art level, and participated in international competitions: SemEval-2010 and CoNLL-2011. RelaxCor achieved second place in CoNLL-2011.

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