scholarly journals SynoExtractor: A Novel Pipeline for Arabic Synonym Extraction Using Word2Vec Word Embeddings

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Rawan N. Al-Matham ◽  
Hend S. Al-Khalifa
Keyword(s):  
Language Processing ◽  
Question Answering ◽  
Semantic Relation ◽  
Semantic Relations ◽  
Word Embeddings ◽  
Average Precision ◽  
King Saud University ◽  
Similar Word ◽  
Linguistic Rules ◽  
Classical Arabic

Automatic synonym extraction plays an important role in many natural language processing systems, such as those involving information retrieval and question answering. Recently, research has focused on extracting semantic relations from word embeddings since they capture relatedness and similarity between words. However, using word embeddings alone poses problems for synonym extraction because it cannot determine whether the relation between words is synonymy or some other semantic relation. In this paper, we present a novel solution for this problem by proposing the SynoExtractor pipeline, which can be used to filter similar word embeddings to retain synonyms based on specified linguistic rules. Our experiments were conducted using KSUCCA and Gigaword embeddings and trained with CBOW and SG models. We evaluated automatically extracted synonyms by comparing them with Alma’any Arabic synonym thesauri. We also arranged for a manual evaluation by two Arabic linguists. The results of experiments we conducted show that using the SynoExtractor pipeline enhances the precision of synonym extraction compared to using the cosine similarity measure alone. SynoExtractor obtained a 0.605 mean average precision (MAP) for the King Saud University Corpus of Classical Arabic with 21% improvement over the baseline and a 0.748 MAP for the Gigaword corpus with 25% improvement. SynoExtractor outperformed the Sketch Engine thesaurus for synonym extraction by 32% in terms of MAP. Our work shows promising results for synonym extraction suggesting that our method can also be used with other languages.

Document Summarization Using Sentence-Level Semantic Based on Word Embeddings

2019 ◽  
Vol 29 (02) ◽  
pp. 177-196 ◽  
Author(s):  
Kamal Al-Sabahi ◽  
Zhang Zuping
Keyword(s):  
Language Processing ◽  
Web Search ◽  
Question Answering ◽  
Information Overload ◽  
Good Representation ◽  
Intelligence Analysis ◽  
Word Embeddings ◽  
Question Answering Systems ◽  
Active Research ◽  
News Recommendation

In the era of information overload, text summarization has become a focus of attention in a number of diverse fields such as, question answering systems, intelligence analysis, news recommendation systems, search results in web search engines, and so on. A good document representation is the key point in any successful summarizer. Learning this representation becomes a very active research in natural language processing field (NLP). Traditional approaches mostly fail to deliver a good representation. Word embedding has proved an excellent performance in learning the representation. In this paper, a modified BM25 with Word Embeddings are used to build the sentence vectors from word vectors. The entire document is represented as a set of sentence vectors. Then, the similarity between every pair of sentence vectors is computed. After that, TextRank, a graph-based model, is used to rank the sentences. The summary is generated by picking the top-ranked sentences according to the compression rate. Two well-known datasets, DUC2002 and DUC2004, are used to evaluate the models. The experimental results show that the proposed models perform comprehensively better compared to the state-of-the-art methods.

Using multiple Web resources and inference rules to classify Chinese word semantic relation

2018 ◽  
Vol 46 (2) ◽  
pp. 120-126 ◽  
Author(s):  
Shutian Ma ◽  
Yingyi Zhang ◽  
Chengzhi Zhang
Keyword(s):  
Language Processing ◽  
Data Access ◽  
Semantic Relation ◽  
Semantic Relations ◽  
Primary System ◽  
Chinese Word ◽  
Lexical Resources ◽  
Data Set ◽  
Content Type ◽  
External Resources

Purpose The purpose of this paper is to classify Chinese word semantic relations, which are synonyms, antonyms, hyponyms and meronymys. Design/methodology/approach Basically, four simple methods are applied, ontology-based, dictionary-based, pattern-based and morpho-syntactic method. The authors make good use of search engine to build lexical and semantic resources for dictionary-based and pattern-based methods. To improve classification performance with more external resources, they also classify the given word pairs in Chinese and in English at the same time by using machine translation. Findings Experimental results show that the approach achieved an average F1 score of 50.87 per cent, an average accuracy of 70.36 per cent and an average recall of 40.05 per cent over all classification tasks. Synonym and antonym classification achieved high accuracy, i.e. above 90 per cent. Moreover, dictionary-based and pattern-based approaches work effectively on final data set. Originality/value For many natural language processing (NLP) tasks, the step of distinguishing word semantic relation can help to improve system performance, such as information extraction and knowledge graph generation. Currently, common methods for this task rely on large corpora for training or dictionaries and thesauri for inference, where limitation lies in freely data access and keeping built lexical resources up-date. This paper builds a primary system for classifying Chinese word semantic relations by seeking new ways to obtain the external resources efficiently.

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 Deep learning approach to text analysis for human emotion detection from big data

2022 ◽  
Vol 31 (1) ◽  
pp. 113-126
Author(s):  
Jia Guo
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Text Analysis ◽  
Question Answering ◽  
Word Embeddings ◽  
Emotion Detection ◽  
Human Emotion

Abstract Emotional recognition has arisen as an essential field of study that can expose a variety of valuable inputs. Emotion can be articulated in several means that can be seen, like speech and facial expressions, written text, and gestures. Emotion recognition in a text document is fundamentally a content-based classification issue, including notions from natural language processing (NLP) and deep learning fields. Hence, in this study, deep learning assisted semantic text analysis (DLSTA) has been proposed for human emotion detection using big data. Emotion detection from textual sources can be done utilizing notions of Natural Language Processing. Word embeddings are extensively utilized for several NLP tasks, like machine translation, sentiment analysis, and question answering. NLP techniques improve the performance of learning-based methods by incorporating the semantic and syntactic features of the text. The numerical outcomes demonstrate that the suggested method achieves an expressively superior quality of human emotion detection rate of 97.22% and the classification accuracy rate of 98.02% with different state-of-the-art methods and can be enhanced by other emotional word embeddings.

scholarly journals Query-Based Retrieval Using Universal Sentence Encoder

2021 ◽  
Vol 35 (4) ◽  
pp. 301-306
Author(s):  
Godavarthi Deepthi ◽  
A. Mary Sowjanya
Keyword(s):  
Natural Language Processing ◽  
Language Processing ◽  
Correct Answer ◽  
Question Answering ◽  
Semantic Information ◽  
Word Embeddings ◽  
Question Answering System ◽  
Universal Sentence ◽  
User Query ◽  
Averaging Network

In Natural language processing, various tasks can be implemented with the features provided by word embeddings. But for obtaining embeddings for larger chunks like sentences, the efforts applied through word embeddings will not be sufficient. To resolve such issues sentence embeddings can be used. In sentence embeddings, complete sentences along with their semantic information are represented as vectors so that the machine finds it easy to understand the context. In this paper, we propose a Question Answering System (QAS) based on sentence embeddings. Our goal is to obtain the text from the provided context for a user-query by extracting the sentence in which the correct answer is present. Traditionally, infersent models have been used on SQUAD for building QAS. In recent times, Universal Sentence Encoder with USECNN and USETrans have been developed. In this paper, we have used another variant of the Universal sentence encoder, i.e. Deep averaging network in order to obtain pre-trained sentence embeddings. The results on the SQUAD-2.0 dataset indicate our approach (USE with DAN) performs well compared to Facebook’s infersent embedding.

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 Multi-Question Learning for Visual Question Answering

2020 ◽  
Vol 34 (07) ◽  
pp. 11328-11335
Author(s):  
Chenyi Lei ◽  
Lei Wu ◽  
Dong Liu ◽  
Zhao Li ◽  
Guoxin Wang ◽  
...  
Keyword(s):  
Language Processing ◽  
Question Answering ◽  
Video Sequence ◽  
Network Models ◽  
Semantic Relations ◽  
Training Procedure ◽  
New Paradigm ◽  
Visual Question Answering ◽  
The Arts ◽  
Public Datasets

Visual Question Answering (VQA) raises a great challenge for computer vision and natural language processing communities. Most of the existing approaches consider video-question pairs individually during training. However, we observe that there are usually multiple (either sequentially generated or not) questions for the target video in a VQA task, and the questions themselves have abundant semantic relations. To explore these relations, we propose a new paradigm for VQA termed Multi-Question Learning (MQL). Inspired by the multi-task learning, MQL learns from multiple questions jointly together with their corresponding answers for a target video sequence. The learned representations of video-question pairs are then more general to be transferred for new questions. We further propose an effective VQA framework and design a training procedure for MQL, where the specifically designed attention network models the relation between input video and corresponding questions, enabling multiple video-question pairs to be co-trained. Experimental results on public datasets show the favorable performance of the proposed MQL-VQA framework compared to state-of-the-arts.

scholarly journals The Same Semantic Relations Link Structurally Different Realizations of Concepts

2009 ◽  
Vol 2 ◽  
Author(s):  
Vivi Nastase ◽  
Stan Szpakowicz
Keyword(s):  
Language Processing ◽  
Text Processing ◽  
Surface Expression ◽  
Semantic Relation ◽  
Point Of View ◽  
Semantic Relations ◽  
Linguistic Expression ◽  
Grammatical Structure ◽  
Systematic Account ◽  
Computational Point

To make sense of an utterance, people identify in its linear linguistic expression the concepts and the connections between them. A concept normally has a lexical realization; connections between concepts often do not, but they are perceived even without the benefit of lexical cues. Making these connections – called semantic relations in the field of natural language processing – relies on the form and structure of linguistic expressions, and the concepts these expressions evoke. This implies two levels: the level of the text, the linguistic expression with its form and (grammatical) structure, and the level of the concepts which the speaker wants to convey. An overview of the literature shows that semantic relations are, for pragmatic reasons, a means to an end – extract information, explain the links between the head of a phrase and its arguments, and so on – and that is why they are analyzed from the perspective of what they link. At the text level, the process of semantic relation analysis is informed by syntactic elements – noun phrases, verbs and their arguments, clauses and so on – thus differentiating semantic relations based on the complexity of the syntactic constructions in which their arguments appear. At the conceptual level, the same semantic relation is assigned to pairs of concepts, regardless of their surface expression. The process can be said to disregard the implications of having syntactic constructions of various complexity correspond to the concepts linked. In this article, we propose to put semantic relations first: analyze them, determine what constraints they place on the concepts they connect, and how those concepts can be lexicalized. Lexicalization takes place via expressions of increasing syntactic complexity: phrases, clauses and multi-clause sentences. Next, we show how the linguistic phenomena involved in producing different lexicalizations explain – in a systematic manner – how semantic relations can have instances in syntactic constructions of various complexity. We focus on binary semantic relations between concepts/textual elements within sentences. This kind of analysis leads to a better understanding of the relations themselves and to a systematic account of phenomena related to their occurrence in texts. It reveals some of the assumptions and linguistic gaps people fill when they recognize relations in text. From the computational point of view of text processing, such a solid basis of the analysis of semantic relations adds consistency. Evidence for a particular relation can come from all its instances in a text, regardless of the syntactic form of the concepts it connects. Knowledge of the expected concepts and their syntactic realization may signal the presence of covert or implied information, which we can then work to retrieve. Assigning a semantic relation should be a conscious choice, with the understanding of what implications such a tag has both for the implicitly and explicitly expressed elements of a concept.

Reports of the Workshops Held at the 2019 AAAI Conference on Artificial Intelligence

AI Magazine ◽  
2019 ◽  
Vol 40 (3) ◽  
pp. 67-78
Author(s):  
Guy Barash ◽  
Mauricio Castillo-Effen ◽  
Niyati Chhaya ◽  
Peter Clark ◽  
Huáscar Espinoza ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Cyber Security ◽  
Question Answering ◽  
Intent Recognition ◽  
Affective Content ◽  
Learning Plan ◽  
Dialog System ◽  
Affective Content Analysis ◽  
Games And Simulations

The workshop program of the Association for the Advancement of Artificial Intelligence’s 33rd Conference on Artificial Intelligence (AAAI-19) was held in Honolulu, Hawaii, on Sunday and Monday, January 27–28, 2019. There were fifteen workshops in the program: Affective Content Analysis: Modeling Affect-in-Action, Agile Robotics for Industrial Automation Competition, Artificial Intelligence for Cyber Security, Artificial Intelligence Safety, Dialog System Technology Challenge, Engineering Dependable and Secure Machine Learning Systems, Games and Simulations for Artificial Intelligence, Health Intelligence, Knowledge Extraction from Games, Network Interpretability for Deep Learning, Plan, Activity, and Intent Recognition, Reasoning and Learning for Human-Machine Dialogues, Reasoning for Complex Question Answering, Recommender Systems Meet Natural Language Processing, Reinforcement Learning in Games, and Reproducible AI. This report contains brief summaries of the all the workshops that were held.

Fast Neural Network Engine for Natural Science Language Processing: A Drug-Search Case.

2020 ◽  
Author(s):  
Vadim V. Korolev ◽  
Artem Mitrofanov ◽  
Kirill Karpov ◽  
Valery Tkachenko
Keyword(s):  
Neural Network ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Natural Science ◽  
Therapeutic Agent ◽  
Semantic Relations ◽  
Chemical Data ◽  
Processing Methods ◽  
Modern Natural

The main advantage of modern natural language processing methods is a possibility to turn an amorphous human-readable task into a strict mathematic form. That allows to extract chemical data and insights from articles and to find new semantic relations. We propose a universal engine for processing chemical and biological texts. We successfully tested it on various use-cases and applied to a case of searching a therapeutic agent for a COVID-19 disease by analyzing PubMed archive.

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