scholarly journals Multilingual Open Information Extraction: Challenges and Opportunities

2019 ◽  
Author(s):  
Daniela Barreiro Claro ◽  
Marlo Souza ◽  
Clarissa Castellã Xavier ◽  
Leandro Oliveira
Keyword(s):  
Information Extraction ◽  
Language Processing ◽  
Extraction Method ◽  
State Of The Art ◽  
Transfer Of Knowledge ◽  
Open Information Extraction ◽  
General Rules ◽  
Challenges And Opportunities ◽  
Multilingual Approach

The number of documents published on the Web other languages than English grows every year. As a consequence, it increases the necessity of extracting useful information from different languages, pointing out the importance of researching Open Information Extraction (OIE) techniques. Different OIE methods have been dealing with features from a unique language. On the other hand, few approaches tackle multilingual aspects. In such approaches, multilingual is only treated as an extraction method, which results in low precision due to the use of general rules. Multilingual methods have been applied to a vast amount of problems in Natural Language Processing achieving satisfactory results and demonstrating that knowledge acquisition for a language can be transferred to other languages to improve the quality of the facts extracted. We state that a multilingual approach can enhance OIE methods, being ideal to evaluate and compare OIE systems, and as a consequence, to applying it to the collected facts. In this work, we discuss how the transfer knowledge between languages can increase the acquisition from multilingual approaches. We provide a roadmap of the Multilingual Open IE area concerning the state of the art studies. Additionally, we evaluate the transfer of knowledge to improve the quality of the facts extracted in each language. Moreover, we discuss the importance of a parallel corpus to evaluate and compare multilingual systems.

scholarly journals Multilingual Open Information Extraction: Challenges and Opportunities

Information ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 228 ◽  
Author(s):  
Daniela Barreiro Claro ◽  
Marlo Souza ◽  
Clarissa Castellã Xavier ◽  
Leandro Oliveira
Keyword(s):  
Information Extraction ◽  
Language Processing ◽  
State Of The Art ◽  
Transfer Of Knowledge ◽  
Linguistic Resources ◽  
Open Information Extraction ◽  
General Rules ◽  
Challenges And Opportunities ◽  
Multilingual Approach

The number of documents published on the Web in languages other than English grows every year. As a consequence, the need to extract useful information from different languages increases, highlighting the importance of research into Open Information Extraction (OIE) techniques. Different OIE methods have dealt with features from a unique language; however, few approaches tackle multilingual aspects. In those approaches, multilingualism is restricted to processing text in different languages, rather than exploring cross-linguistic resources, which results in low precision due to the use of general rules. Multilingual methods have been applied to numerous problems in Natural Language Processing, achieving satisfactory results and demonstrating that knowledge acquisition for a language can be transferred to other languages to improve the quality of the facts extracted. We argue that a multilingual approach can enhance OIE methods as it is ideal to evaluate and compare OIE systems, and therefore can be applied to the collected facts. In this work, we discuss how the transfer knowledge between languages can increase acquisition from multilingual approaches. We provide a roadmap of the Multilingual Open IE area concerning state of the art studies. Additionally, we evaluate the transfer of knowledge to improve the quality of the facts extracted in each language. Moreover, we discuss the importance of a parallel corpus to evaluate and compare multilingual systems.

scholarly journals A Methodology for Open Information Extraction and Representation from Large Scientific Corpora: The CORD-19 Data Exploration Use Case

2020 ◽  
Vol 10 (16) ◽  
pp. 5630
Author(s):  
Dimitris Papadopoulos ◽  
Nikolaos Papadakis ◽  
Antonis Litke
Keyword(s):  
Information Extraction ◽  
Language Processing ◽  
State Of The Art ◽  
Data Exploration ◽  
Graph Representation ◽  
Linguistic Features ◽  
Open Information Extraction ◽  
Structured Knowledge ◽  
Machine Readable ◽  
Medical Dataset

The usefulness of automated information extraction tools in generating structured knowledge from unstructured and semi-structured machine-readable documents is limited by challenges related to the variety and intricacy of the targeted entities, the complex linguistic features of heterogeneous corpora, and the computational availability for readily scaling to large amounts of text. In this paper, we argue that the redundancy and ambiguity of subject–predicate–object (SPO) triples in open information extraction systems has to be treated as an equally important step in order to ensure the quality and preciseness of generated triples. To this end, we propose a pipeline approach for information extraction from large corpora, encompassing a series of natural language processing tasks. Our methodology consists of four steps: i. in-place coreference resolution, ii. extractive text summarization, iii. parallel triple extraction, and iv. entity enrichment and graph representation. We manifest our methodology on a large medical dataset (CORD-19), relying on state-of-the-art tools to fulfil the aforementioned steps and extract triples that are subsequently mapped to a comprehensive ontology of biomedical concepts. We evaluate the effectiveness of our information extraction method by comparing it in terms of precision, recall, and F1-score with state-of-the-art OIE engines and demonstrate its capabilities on a set of data exploration tasks.

scholarly journals Span Model for Open Information Extraction on Accurate Corpus

2020 ◽  
Vol 34 (05) ◽  
pp. 9523-9530
Author(s):  
Junlang Zhan ◽  
Hai Zhao
Keyword(s):  
Information Extraction ◽  
State Of The Art ◽  
Training Dataset ◽  
Model Design ◽  
Test Set ◽  
Benchmark Test ◽  
Open Information Extraction ◽  
Test Sets ◽  
Improved Model ◽  
Benchmark Evaluation

Open Information Extraction (Open IE) is a challenging task especially due to its brittle data basis. Most of Open IE systems have to be trained on automatically built corpus and evaluated on inaccurate test set. In this work, we first alleviate this difficulty from both sides of training and test sets. For the former, we propose an improved model design to more sufficiently exploit training dataset. For the latter, we present our accurately re-annotated benchmark test set (Re-OIE2016) according to a series of linguistic observation and analysis. Then, we introduce a span model instead of previous adopted sequence labeling formulization for n-ary Open IE. Our newly introduced model achieves new state-of-the-art performance on both benchmark evaluation datasets.

scholarly journals Deep learning for brain disorders: from data processing to disease treatment

2020 ◽  
Author(s):  
Ninon Burgos ◽  
Simona Bottani ◽  
Johann Faouzi ◽  
Elina Thibeau-Sutre ◽  
Olivier Colliot
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Language Processing ◽  
State Of The Art ◽  
Imaging Genetics ◽  
Environmental Data ◽  
Brain Disorders ◽  
Disease Treatment ◽  
Clinical Routine

Abstract In order to reach precision medicine and improve patients’ quality of life, machine learning is increasingly used in medicine. Brain disorders are often complex and heterogeneous, and several modalities such as demographic, clinical, imaging, genetics and environmental data have been studied to improve their understanding. Deep learning, a subpart of machine learning, provides complex algorithms that can learn from such various data. It has become state of the art in numerous fields, including computer vision and natural language processing, and is also growingly applied in medicine. In this article, we review the use of deep learning for brain disorders. More specifically, we identify the main applications, the concerned disorders and the types of architectures and data used. Finally, we provide guidelines to bridge the gap between research studies and clinical routine.

scholarly journals Can We Survive without Labelled Data in NLP? Transfer Learning for Open Information Extraction

2020 ◽  
Vol 10 (17) ◽  
pp. 5758
Author(s):  
Injy Sarhan ◽  
Marco Spruit
Keyword(s):  
Information Extraction ◽  
Transfer Learning ◽  
Language Processing ◽  
Relation Extraction ◽  
Training Data ◽  
Open Information Extraction ◽  
Comparable Performance ◽  
Medical Domain ◽  
Inductive Transfer Learning ◽  
F Measure

Various tasks in natural language processing (NLP) suffer from lack of labelled training data, which deep neural networks are hungry for. In this paper, we relied upon features learned to generate relation triples from the open information extraction (OIE) task. First, we studied how transferable these features are from one OIE domain to another, such as from a news domain to a bio-medical domain. Second, we analyzed their transferability to a semantically related NLP task, namely, relation extraction (RE). We thereby contribute to answering the question: can OIE help us achieve adequate NLP performance without labelled data? Our results showed comparable performance when using inductive transfer learning in both experiments by relying on a very small amount of the target data, wherein promising results were achieved. When transferring to the OIE bio-medical domain, we achieved an F-measure of 78.0%, only 1% lower when compared to traditional learning. Additionally, transferring to RE using an inductive approach scored an F-measure of 67.2%, which was 3.8% lower than training and testing on the same task. Hereby, our analysis shows that OIE can act as a reliable source task.

Dependency parsing of Polish

2019 ◽  
Vol 55 (2) ◽  
pp. 305-337 ◽  
Author(s):  
Alina Wróblewska ◽  
Piotr Rybak
Keyword(s):  
Language Processing ◽  
Argument Structure ◽  
Question Answering ◽  
State Of The Art ◽  
Dependency Parsing ◽  
Dependency Theory ◽  
Crucial Issue ◽  
Experimental Part ◽  
Predicate Argument Structure

Abstract The predicate-argument structure transparently encoded in dependency-based syntactic representations supports machine translation, question answering, information extraction, etc. The quality of dependency parsing is therefore a crucial issue in natural language processing. In the current paper we discuss the fundamental ideas of the dependency theory and provide an overview of selected dependency-based resources for Polish. Furthermore, we present some state-of-the-art dependency parsing systems whose models can be estimated on correctly annotated data. In the experimental part, we provide an in-depth evaluation of these systems on Polish data. Our results show that graph-based parsers, even those without any neural component, are better suited for Polish than transition-based parsing systems.

scholarly journals Indonesian Information Extraction : Challenges and Opportunities

2021 ◽  
Vol 8 (1) ◽  
pp. 421-429
Author(s):  
Yan Puspitarani
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Information Extraction ◽  
Language Processing ◽  
Research Trends ◽  
Structured Data ◽  
Daily Lives ◽  
Unstructured Text ◽  
Challenges And Opportunities ◽  
Data Source

Information extraction is part of natural language processing, aiming to find, retrieve, or process information. The data source for information extraction is text. Text cannot be separated from people's daily lives. Through text, a lot of confidential information can be obtained. To produce information, the unstructured text will be converted into structured data. There are many approaches that researchers take to this process. Most of the studies are in English. Therefore, this paper will present current research trends, challenges, and information extraction opportunities using Indonesian.

scholarly journals A Survey of Citation Recommendation Tasks and Methods

2021 ◽  
Vol 28 (3) ◽  
pp. 183-205
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
State Of The Art ◽  
Scientific Production ◽  
Machine Learning Methods ◽  
Citation Function ◽  
Key Aspects ◽  
Global And Local ◽  
Machine Learning Models

Scientific articles store vast amounts of knowledge amassed through many decades of research. They serve to communicate research results among scientists but also for learning and tracking progress in the field. However, scientific production has risen to levels that make it difficult even for experts to keep up with work in their field. As a remedy, specialized search engines are being deployed, incorporating novel natural language processing and machine learning methods. The task of citation recommendation, in particular, has attracted much interest as it holds promise for improving the quality of scientific production. In this paper, we present the state-of-the-art in citation recommendation: we survey the methods for global and local approaches to the task, the evaluation setups and datasets, and the most successful machine learning models. In addition, we overview two tasks complementary to citation recommendation: extraction of key aspects and entities from articles and citation function classification. With this survey, we hope to provide the ground for understanding current efforts and stimulate further research in this exciting and promising field.

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>

Automatic question generation based on sentence structure analysis using machine learning approach

2021 ◽  
pp. 1-31
Author(s):  
Miroslav Blšták ◽  
Viera Rozinajová
Keyword(s):  
Machine Learning ◽  
Natural Language ◽  
Language Processing ◽  
State Of The Art ◽  
Sentence Structure ◽  
Question Generation ◽  
Transformation Rules ◽  
Input Text ◽  
Automatic Question Generation

Abstract Automatic question generation is one of the most challenging tasks of Natural Language Processing. It requires “bidirectional” language processing: first, the system has to understand the input text (Natural Language Understanding), and it then has to generate questions also in the form of text (Natural Language Generation). In this article, we introduce our framework for generating the factual questions from unstructured text in the English language. It uses a combination of traditional linguistic approaches based on sentence patterns with several machine learning methods. We first obtain lexical, syntactic and semantic information from an input text, and we then construct a hierarchical set of patterns for each sentence. The set of features is extracted from the patterns, and it is then used for automated learning of new transformation rules. Our learning process is totally data-driven because the transformation rules are obtained from a set of initial sentence–question pairs. The advantages of this approach lie in a simple expansion of new transformation rules which allows us to generate various types of questions and also in the continuous improvement of the system by reinforcement learning. The framework also includes a question evaluation module which estimates the quality of generated questions. It serves as a filter for selecting the best questions and eliminating incorrect ones or duplicates. We have performed several experiments to evaluate the correctness of generated questions, and we have also compared our system with several state-of-the-art systems. Our results indicate that the quality of generated questions outperforms the state-of-the-art systems and our questions are also comparable to questions created by humans. We have also created and published an interface with all created data sets and evaluated questions, so it is possible to follow up on our work.

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