scholarly journals HybQA: Hybrid Deep Relation Extraction for Question Answering on Freebase

2017 ◽  
Author(s):  
Reham Mohamed ◽  
Nagwa M. El-Makky ◽  
Khaled Nagi
Keyword(s):  
Question Answering ◽  
Relation Extraction

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 Building Graph for Events and Time in Natural Language Text

2020 ◽  
Vol 9 (3) ◽  
pp. 581-586
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Information Extraction ◽  
Language Processing ◽  
Question Answering ◽  
Relation Extraction ◽  
Event Extraction ◽  
Event Time ◽  
Time Graph ◽  
Question Answering Systems

Events and time are two major key terms in natural language processing due to the various event-oriented tasks these are become an essential terms in information extraction. In natural language processing and information extraction or retrieval event and time leads to several applications like text summaries, documents summaries, and question answering systems. In this paper, we present events-time graph as a new way of construction for event-time based information from text. In this event-time graph nodes are events, whereas edges represent the temporal and co-reference relations between events. In many of the previous researches of natural language processing mainly individually focused on extraction tasks and in domain-specific way but in this work we present extraction and representation of the relationship between events- time by representing with event time graph construction. Our overall system construction is in three-step process that performs event extraction, time extraction, and representing relation extraction. Each step is at a performance level comparable with the state of the art. We present Event extraction on MUC data corpus annotated with events mentions on which we train and evaluate our model. Next, we present time extraction the model of times tested for several news articles from Wikipedia corpus. Next is to represent event time relation by representation by next constructing event time graphs. Finally, we evaluate the overall quality of event graphs with the evaluation metrics and conclude the observations of the entire work

scholarly journals A semi-automatic approach to construct Vietnamese ontology from online text

2012 ◽  
Vol 13 (5) ◽  
pp. 148 ◽  
Author(s):  
Bao-An Nguyen ◽  
Don-Lin Yang
Keyword(s):  
Language Processing ◽  
Question Answering ◽  
Expert Knowledge ◽  
Relation Extraction ◽  
Knowledge Bases ◽  
Instructional Materials ◽  
Formal Representation ◽  
Text Documents ◽  
Ontology Construction ◽  
Question Answering Systems

An ontology is an effective formal representation of knowledge used commonly in artificial intelligence, semantic web, software engineering, and information retrieval. In open and distance learning, ontologies are used as knowledge bases for e-learning supplements, educational recommenders, and question answering systems that support students with much needed resources. In such systems, ontology construction is one of the most important phases. Since there are abundant documents on the Internet, useful learning materials can be acquired openly with the use of an ontology.  However, due to the lack of system support for ontology construction, it is difficult to construct self-instructional materials for Vietnamese people. In general, the cost of manual acquisition of ontologies from domain documents and expert knowledge is too high. Therefore, we present a support system for Vietnamese ontology construction using pattern-based mechanisms to discover Vietnamese concepts and conceptual relations from Vietnamese text documents. In this system, we use the combination of statistics-based, data mining, and Vietnamese natural language processing methods to develop concept and conceptual relation extraction algorithms to discover knowledge from Vietnamese text documents. From the experiments, we show that our approach provides a feasible solution to build Vietnamese ontologies used for supporting systems in education.<br /><br />

scholarly journals A sui generis QA approach using RoBERTa for adverse drug event identification

2021 ◽  
Vol 22 (S11) ◽  
Author(s):  
Harshit Jain ◽  
Nishant Raj ◽  
Suyash Mishra
Keyword(s):  
Question Answering ◽  
Adverse Drug Events ◽  
Short Term Memory ◽  
Domain Adaptation ◽  
Relation Extraction ◽  
Biomedical Literature ◽  
Drug Event ◽  
Textual Data ◽  
Entity Relation Extraction ◽  
End To End

Abstract Background Extraction of adverse drug events from biomedical literature and other textual data is an important component to monitor drug-safety and this has attracted attention of many researchers in healthcare. Existing works are more pivoted around entity-relation extraction using bidirectional long short term memory networks (Bi-LSTM) which does not attain the best feature representations. Results In this paper, we introduce a question answering framework that exploits the robustness, masking and dynamic attention capabilities of RoBERTa by a technique of domain adaptation and attempt to overcome the aforementioned limitations. With formulation of an end-to-end pipeline, our model outperforms the prior work by 9.53% F1-Score. Conclusion An end-to-end pipeline that leverages state of the art transformer architecture in conjunction with QA approach can bolster the performances of entity-relation extraction tasks in the biomedical domain. In particular, we believe our research would be helpful in identification of potential adverse drug reactions in mono as well as combination therapy related textual data.

scholarly journals Asking Effective and Diverse Questions: A Machine Reading Comprehension based Framework for Joint Entity-Relation Extraction

2020 ◽  
Author(s):  
Tianyang Zhao ◽  
Zhao Yan ◽  
Yunbo Cao ◽  
Zhoujun Li
Keyword(s):  
Reading Comprehension ◽  
Question Answering ◽  
Relation Extraction ◽  
Effective Solution ◽  
Joint Learning ◽  
Selection Strategies ◽  
Entity Relation Extraction ◽  
Extraction Model ◽  
Machine Reading ◽  
Single Question

Recent advances cast the entity-relation extraction to a multi-turn question answering (QA) task and provide an effective solution based on the machine reading comprehension (MRC) models. However, they use a single question to characterize the meaning of entities and relations, which is intuitively not enough because of the variety of context semantics. Meanwhile, existing models enumerate all relation types to generate questions, which is inefficient and easily leads to confusing questions. In this paper, we improve the existing MRC-based entity-relation extraction model through diverse question answering. First, a diversity question answering mechanism is introduced to detect entity spans and two answering selection strategies are designed to integrate different answers. Then, we propose to predict a subset of potential relations and filter out irrelevant ones to generate questions effectively. Finally, entity and relation extractions are integrated in an end-to-end way and optimized through joint learning. Experiment results show that the proposed method significantly outperforms baseline models, which improves the relation F1 to 62.1% (+1.9%) on ACE05 and 71.9% (+3.0%) on CoNLL04. Our implementation is available at https://github.com/TanyaZhao/MRC4ERE.

scholarly journals Relation Extraction With Clause-Based Open Information Extraction

2021 ◽  
Author(s):  
Duc Thuan Vo
Keyword(s):  
Natural Language ◽  
Information Extraction ◽  
Language Processing ◽  
Question Answering ◽  
Relation Extraction ◽  
Linguistic Knowledge ◽  
Dependency Parsing ◽  
Grammatical Structure ◽  
Open Information Extraction ◽  
Wide Range

Information Extraction (IE) is one of the challenging tasks in natural language processing. The goal of relation extraction is to discover the relevant segments of information in large numbers of textual documents such that they can be used for structuring data. IE aims at discovering various semantic relations in natural language text and has a wide range of applications such as question answering, information retrieval, knowledge presentation, among others. This thesis proposes approaches for relation extraction with clause-based Open Information Extraction that use linguistic knowledge to capture a variety of information including semantic concepts, words, POS tags, shallow and full syntax, dependency parsing in rich syntactic and semantic structures.<div>Within the plethora of Open Information Extraction that focus on the use of syntactic and dependency parsing for the purposes of detecting relations, incoherent and uninformative relation extractions can still be found. The extracted relations can be erroneous at times and fail to have a meaningful interpretation. As such, we first propose refinements to the grammatical structure of syntactic and dependency parsing with clause structures and clause types in an effort to generate propositions that can be deemed as meaningful extractable relations. Second, considering that choosing the most efficient seeds are pivotal to the success of the bootstrapping process when extracting relations, we propose an extended clause-based pattern extraction method with selftraining for unsupervised relation extraction. The proposed self-training algorithm relies on the clause-based approach to extract a small set of seed instances in order to identify and derive new patterns. Third, we employ matrix factorization and collaborative filtering for relation extraction. To avoid the need for manually predefined schemas, we employ the notion of universal schemas that is formed as a collection of patterns derived from Open Information Extraction tools as well as from relation schemas of pre-existing datasets. While previous systems have trained relations only for entities, we exploit advanced features from relation characteristics such as clause types and semantic topics for predicting new relation instances. Finally, we present an event network representation for temporal and causal event relation extraction that benefits from existing Open IE systems to generate a set of triple relations that are then used to build an event network. The event network is bootstrapped by labeling the temporal and causal disposition of events that are directly linked to each other. The event network can be systematically traversed to identify temporal and causal relations between indirectly connected events. <br></div>

scholarly journals Entity-Relation Extraction as Multi-Turn Question Answering

2019 ◽  
Author(s):  
Xiaoya Li ◽  
Fan Yin ◽  
Zijun Sun ◽  
Xiayu Li ◽  
Arianna Yuan ◽  
...  
Keyword(s):  
Question Answering ◽  
Relation Extraction ◽  
Entity Relation Extraction
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