Synonym Expansion Research Articles

Deep Explainable Hate Speech Active Learning on Social-Media Data

Hate speech is demonstrably aimed at social tension and violence. Detection becomes increasingly difficult as overlapping emotional feelings occur. However, there are still several unresolved issues with informal and indirect targeting of negative communication, including sarcasm, misrepresentation, and praise for the target's or society's immoral behavior. In this study, we proposed a method for instance selection based on attention network visualization. The goal is to categorize, modify, and expand the number of training instances. To this end, we first used the lexicons of hate speech and online forums to train the embedding using transfer learning. Then, we used synonym expansion to the semantic vectors. The active learning approach was used to train the task using the result-label pairs. The entropy-based selection and visualization techniques help select unlabeled text for each active learning cycle. The approach is improved, and the number of training instances is increased to improve the model's accuracy. The active learning cycles are repeated until all unlabeled texts are converted to labeled text. The semantic embedding and lexicon expansion improve the model receiver operating characteristics (ROCs) from 0.89 to 0.91. The bidirectional LSTM with attention and active learning achieved 0.90 for precision-recall. The learned model can visualize the position-weighted terms to illustrate why hate speech is classified.

Systematic Literature Review on Ontology-based Indonesian Question Answering System

Question-Answering (QA) systems at the intersection of natural language processing, information retrieval, and knowledge representation aim to provide efficient responses to natural language queries. These systems have seen extensive development in English and languages like Indonesian present unique challenges and opportunities. This literature review paper delves into the state of ontology-based Indonesian QA systems, highlighting critical challenges. The first challenge lies in sentence understanding, variations, and complexity. Most systems rely on syntactic analysis and struggle to grasp sentence semantics. Complex sentences, especially in Indonesian, pose difficulties in parsing, semantic interpretation, and knowledge extraction. Addressing these linguistic intricacies is pivotal for accurate responses. Secondly, template-based SPARQL query construction, commonly used in Indonesian QA systems, suffers from semantic gaps and inflexibility. Advanced techniques like semantic matching algorithms and dynamic template generation can bridge these gaps and adapt to evolving ontologies. Thirdly, lexical gaps and ambiguity hinder QA systems. Bridging vocabulary mismatches between user queries and ontology labels remains a challenge. Strategies like synonym expansion, word embedding, and ontology enrichment must be explored further to overcome these challenges. Lastly, the review discusses the potential of developing multi-domain ontologies to broaden the knowledge coverage of QA systems. While this presents complex linguistic and ontological challenges, it offers the advantage of responding to various user queries across various domains. This literature review identifies crucial challenges in developing ontology-based Indonesian QA systems and suggests innovative approaches to address these challenges.

EANDC: An explainable attention network based deep adaptive clustering model for mental health treatment

Internet-delivered Psychological Treatment (IDPT) has been shown to be an effective method for improving psychological disorders. Natural language processing (NLP) requires an appropriate set of linguistic features for word representation and emotion segmentation. For psychological applications, models must be trained on extensive and diverse datasets to achieve expert-level performance. Labeling psychological texts authorized by patients is challenging because emotional biases can lead to incorrect segmentation of emotions and labeling emotional data is time consuming. In this paper, we propose an assistance tool for psychologists to explore the emotional aspects of mentally ill individuals. We first use an NLP-based method to create emotional lexicon embeddings, and then apply attention-based deep clustering. The learned representation is then used to visualize the emotional aspect of the text authorized by patients. We expand the patient authored text using synonymous semantic expansion. A latent semantic representation based on context is clustered using EANDC, which is a Explainable Attention Network-based Deep adaptive Clustering model. We use similarity metrics to select a subset of the text and then improve the explainability of learning using a curriculum-based optimization method. The experimental results show that synonym expansion based on the emotion lexicon increases accuracy without affecting the results. The attention method with bidirectional LSTM architecture achieved 0.81 ROC in a blind test. The self-learning based embedding then visualizes the weighted attention words and helps the psychiatrist to improve his explanatory power of the qualitative match for clinical notes and the remedy. The method helps in labeling text and improves the recognition rate of symptoms of mental disorders.

Construction of a 5G Wireless Semantic Web-Assisted English Digital Learning Resource Query System

Based on the 5G wireless semantic network, this paper realizes the construction of the English digital learning resources query system, providing the query and management functions for English digital learning resources, and realizes the integration of functions and resources on the digital learning system and mobile learning system. The platform is put into practical application, and data analysis is conducted to conclude how this system is used and whether it is conducive to improving students’ interest in learning English and their English proficiency. We propose a quality algorithm for 5G wireless networks, which not only captures the characteristics of 5G wireless networks such as local broadcast and node movement but also combines quality predicates as guards with broadcast reception actions, which are used to portray that a node can continue to execute subsequent processes only when the node reception actions satisfy certain conditions, which works together with the node default value mechanism to reduce the impact caused by unreliable links. The query content is interpreted and supplemented at the semantic level by using synonym expansion, and the knowledge base is used to filter the synonym expansion and further expand the synonym expansion query based on knowledge structure, to tap the implicit knowledge points related to the query, and to fuse the two extensions to realize the knowledge-based query expansion. We use 5G wireless semantic web-related technologies to build an English digital learning resource query system with complete functions and good experience across terminals. Through the questionnaire survey and data analysis of the users of this system, it is found that the system is well used and effectively enhances students’ interest in learning English and improves learning efficiency. By applying the experimental comparison method, it is known that this system has improved students’ English proficiency to a certain extent.

Improved characterisation of clinical text through ontology-based vocabulary expansion

BackgroundBiomedical ontologies contain a wealth of metadata that constitutes a fundamental infrastructural resource for text mining. For several reasons, redundancies exist in the ontology ecosystem, which lead to the same entities being described by several concepts in the same or similar contexts across several ontologies. While these concepts describe the same entities, they contain different sets of complementary metadata. Linking these definitions to make use of their combined metadata could lead to improved performance in ontology-based information retrieval, extraction, and analysis tasks.ResultsWe develop and present an algorithm that expands the set of labels associated with an ontology class using a combination of strict lexical matching and cross-ontology reasoner-enabled equivalency queries. Across all disease terms in the Disease Ontology, the approach found 51,362 additional labels, more than tripling the number defined by the ontology itself. Manual validation by a clinical expert on a random sampling of expanded synonyms over the Human Phenotype Ontology yielded a precision of 0.912. Furthermore, we found that annotating patient visits in MIMIC-III with an extended set of Disease Ontology labels led to semantic similarity score derived from those labels being a significantly better predictor of matching first diagnosis, with a mean average precision of 0.88 for the unexpanded set of annotations, and 0.913 for the expanded set.ConclusionsInter-ontology synonym expansion can lead to a vast increase in the scale of vocabulary available for text mining applications. While the accuracy of the extended vocabulary is not perfect, it nevertheless led to a significantly improved ontology-based characterisation of patients from text in one setting. Furthermore, where run-on error is not acceptable, the technique can be used to provide candidate synonyms which can be checked by a domain expert.

Attention-Based Deep Entropy Active Learning Using Lexical Algorithm for Mental Health Treatment.

With the increasing prevalence of Internet usage, Internet-Delivered Psychological Treatment (IDPT) has become a valuable tool to develop improved treatments of mental disorders. IDPT becomes complicated and labor intensive because of overlapping emotion in mental health. To create a usable learning application for IDPT requires diverse labeled datasets containing an adequate set of linguistic properties to extract word representations and segmentations of emotions. In medical applications, it is challenging to successfully refine such datasets since emotion-aware labeling is time consuming. Other known issues include vocabulary sizes per class, data source, method of creation, and baseline for the human performance level. This paper focuses on the application of personalized mental health interventions using Natural Language Processing (NLP) and attention-based in-depth entropy active learning. The objective of this research is to increase the trainable instances using a semantic clustering mechanism. For this purpose, we propose a method based on synonym expansion by semantic vectors. Semantic vectors based on semantic information derived from the context in which it appears are clustered. The resulting similarity metrics help to select the subset of unlabeled text by using semantic information. The proposed method separates unlabeled text and includes it in the next active learning mechanism cycle. Our method updates model training by using the new training points. The cycle continues until it reaches an optimal solution, and it converts all the unlabeled text into the training set. Our in-depth experimental results show that the synonym expansion semantic vectors help enhance training accuracy while not harming the results. The bidirectional Long Short-Term Memory (LSTM) architecture with an attention mechanism achieved 0.85 Receiver Operating Characteristic (ROC curve) on the blind test set. The learned embedding is then used to visualize the activated word's contribution to each symptom and find the psychiatrist's qualitative agreement. Our method improves the detection rate of depression symptoms from online forum text using the unlabeled forum texts.

Experimental Results on Customer Reviews Using Lexicon-Based Word Polarity Identification Method

Determining the polarities of words in a given context has been in existence since the inception of computational linguistics, text mining, and sentiment analysis. Due to its fundamental role in determining the overall semantic orientation of natural language expressions, it is considered one of the most challenging issues facing these areas of research. This paper introduces a new implementation of the lexicon-based word polarity identification method on several customer reviews datasets. Herein, we use a variation of a lexicon-based word polarity identification method that operates by computing the semantic relatedness between the context expansion set of the target word and a synonym expansion set comprising the synonyms of all words surrounding the target word within the original text fragment. The polarity of the target word is determined as that for which the semantic relatedness between these two meaningful sets is the highest. Unlike most existing lexicon-based multi-polarity word identification methods, the used method is not based on estimating pairwise relatedness at term-level, but instead, it is based on measuring semantic relatedness at the fragment-level. This enables the exploration and capture of a higher degree of semantic and sentimental information, and is more consistent with people’ understanding through the consideration of the larger context in which the word appears. Its performance can be further improved by incorporating an initial step in which the relative negation scope of words in the given text fragment is managed while determining their sentiment orientation. The implementation results demonstrate that the used variation of the lexicon-based word polarity identification method performs favourably against compared methods, as evaluated on numerous benchmark datasets through stand-alone and end-to-end evaluation models.

FactQA: question answering over domain knowledge graph based on two-level query expansion

PurposeWith the advent of the era of Big Data, the scale of knowledge graph (KG) in various domains is growing rapidly, which holds huge amount of knowledge surely benefiting the question answering (QA) research. However, the KG, which is always constituted of entities and relations, is structurally inconsistent with the natural language query. Thus, the QA system based on KG is still faced with difficulties. The purpose of this paper is to propose a method to answer the domain-specific questions based on KG, providing conveniences for the information query over domain KG.Design/methodology/approachThe authors propose a method FactQA to answer the factual questions about specific domain. A series of logical rules are designed to transform the factual questions into the triples, in order to solve the structural inconsistency between the user’s question and the domain knowledge. Then, the query expansion strategies and filtering strategies are proposed from two levels (i.e. words and triples in the question). For matching the question with domain knowledge, not only the similarity values between the words in the question and the resources in the domain knowledge but also the tag information of these words is considered. And the tag information is obtained by parsing the question using Stanford CoreNLP. In this paper, the KG in metallic materials domain is used to illustrate the FactQA method.FindingsThe designed logical rules have time stability for transforming the factual questions into the triples. Additionally, after filtering the synonym expansion results of the words in the question, the expansion quality of the triple representation of the question is improved. The tag information of the words in the question is considered in the process of data matching, which could help to filter out the wrong matches.Originality/valueAlthough the FactQA is proposed for domain-specific QA, it can also be applied to any other domain besides metallic materials domain. For a question that cannot be answered, FactQA would generate a new related question to answer, providing as much as possible the user with the information they probably need. The FactQA could facilitate the user’s information query based on the emerging KG.

A model of synesthetic metaphor interpretation based on cross-modality similarity

Synesthesia focuses on the cross-modality connections between different perceptual modalities (visual, auditory, olfactory, gustatory, and haptic). It presents the aesthetics and creativity of our language and cognitive systems. A model that automatically interprets synesthetic metaphor is proposed in this paper. The interpretation model can learn the semantic knowledge of a feature, and simulate the cross-modality semantic similarity. The semantic knowledge has three parts: feature, the perceptual modality of the feature, and its sentimental orientation. They are used for supervising feature generation and expansion. By multi-step synonyms expansion, our model explores the cross-modality associations between the source and target domains which belong to different modalities. The appropriate feature is selected by a heuristic way to describe the latent meaning of the target domain in the current context. The experimental result shows that our model can fully consider the semantic knowledge and embody the cross-modality relations. It achieves an accuracy of 84%.

LAILAPS-QSM: A RESTful API and JAVA library for semantic query suggestions.

In order to access and filter content of life-science databases, full text search is a widely applied query interface. But its high flexibility and intuitiveness is paid for with potentially imprecise and incomplete query results. To reduce this drawback, query assistance systems suggest those combinations of keywords with the highest potential to match most of the relevant data records. Widespread approaches are syntactic query corrections that avoid misspelling and support expansion of words by suffixes and prefixes. Synonym expansion approaches apply thesauri, ontologies, and query logs. All need laborious curation and maintenance. Furthermore, access to query logs is in general restricted. Approaches that infer related queries by their query profile like research field, geographic location, co-authorship, affiliation etc. require user’s registration and its public accessibility that contradict privacy concerns. To overcome these drawbacks, we implemented LAILAPS-QSM, a machine learning approach that reconstruct possible linguistic contexts of a given keyword query. The context is referred from the text records that are stored in the databases that are going to be queried or extracted for a general purpose query suggestion from PubMed abstracts and UniProt data. The supplied tool suite enables the pre-processing of these text records and the further computation of customized distributed word vectors. The latter are used to suggest alternative keyword queries. An evaluated of the query suggestion quality was done for plant science use cases. Locally present experts enable a cost-efficient quality assessment in the categories trait, biological entity, taxonomy, affiliation, and metabolic function which has been performed using ontology term similarities. LAILAPS-QSM mean information content similarity for 15 representative queries is 0.70, whereas 34% have a score above 0.80. In comparison, the information content similarity for human expert made query suggestions is 0.90. The software is either available as tool set to build and train dedicated query suggestion services or as already trained general purpose RESTful web service. The service uses open interfaces to be seamless embeddable into database frontends. The JAVA implementation uses highly optimized data structures and streamlined code to provide fast and scalable response for web service calls. The source code of LAILAPS-QSM is available under GNU General Public License version 2 in Bitbucket GIT repository: https://bitbucket.org/ipk_bit_team/bioescorte-suggestion

A Smart Email Client Prototype for Effective Reuse of Past Replies

Email communication is widely used in managing customer queries such as complaints and inquiries. With an increasing number of customer emails being received every day, better tools are needed to answer emails efficiently, reuse past efforts and prevent overwhelming and cluttered email backlog. Finding similar past cases is difficult since customer requests might use non-standard terminology. In this paper, we develop a smart email client prototype which helps in replying to an email by providing a list of replies gleaned from the past replied emails. The suggested replies are ranked according to the level of similarity. We have evaluated real-world email data in order to ascertain that reuse is possible, but requires careful retrieval mechanisms. We implement and evaluate a case-based reasoning approach as a methodology to solve the problem by reusing previously written solutions from the past replies stored in the case base. We build a retrieval algorithm that finds similar cases beyond the exact matching, by using text processing and semantics analysis techniques. To optimize retrieval, we apply and cross-evaluate several text analysis techniques such as lexical analysis and synonym expansion, and our evaluation shows that synonym expansion could improve the chance of retrieving a more relevant match even at lower ranks. We evaluate our prototype based on the quality of retrieval results, index size, and processing time elapsed.

An approach to semantic query expansion system based on Hepatitis ontology.

BackgroundOntology development, as an increasingly practical vehicle applied in various fields, plays a significant role in knowledge management. This paper, focusing on constructing and querying a hepatitis ontology, aims to provide a framework for ontology-based medical services. The paper is devoted to the algorithm of query expansion for the hepatitis ontology, including synonym expansion, hypernym/hyponym expansion and expansion of similar words. It applies semantic similarity calculation to judge the similarity of retrieval terms.ResultsThe paper proposes a new prototype system. The accuracy of query expansion is improved in both precision@40 and AP@40, which indicates that query expansion improves the accuracy of the query after using the method proposed in this paper.ConclusionsThe paper has adopted semantic similarity computing to improve retrieval performance. Experiments show that search precision of query expansion is higher based on domain concept relationship.

EXPRS: An extended pagerank method for product feature extraction from online consumer reviews

Online consumer product reviews are a main source for consumers to obtain product information and reduce product uncertainty before making a purchase decision. However, the great volume of product reviews makes it tedious and ineffective for consumers to peruse individual reviews one by one and search for comments on specific product features of their interest. This study proposes a novel method called EXPRS that integrates an extended PageRank algorithm, synonym expansion, and implicit feature inference to extract product features automatically. The empirical evaluation using consumer reviews on three different products shows that EXPRS is more effective than two baseline methods.

MedXN: an open source medication extraction and normalization tool for clinical text.

We developed the Medication Extraction and Normalization (MedXN) system to extract comprehensive medication information and normalize it to the most appropriate RxNorm concept unique identifier (RxCUI) as specifically as possible. Medication descriptions in clinical notes were decomposed into medication name and attributes, which were separately extracted using RxNorm dictionary lookup and regular expression. Then, each medication name and its attributes were combined together according to RxNorm convention to find the most appropriate RxNorm representation. To do this, we employed serialized hierarchical steps implemented in Apache's Unstructured Information Management Architecture. We also performed synonym expansion, removed false medications, and employed inference rules to improve the medication extraction and normalization performance. An evaluation on test data of 397 medication mentions showed F-measures of 0.975 for medication name and over 0.90 for most attributes. The RxCUI assignment produced F-measures of 0.932 for medication name and 0.864 for full medication information. Most false negative RxCUI assignments in full medication information are due to human assumption of missing attributes and medication names in the gold standard. The MedXN system (http://sourceforge.net/projects/ohnlp/files/MedXN/) was able to extract comprehensive medication information with high accuracy and demonstrated good normalization capability to RxCUI as long as explicit evidence existed. More sophisticated inference rules might result in further improvements to specific RxCUI assignments for incomplete medication descriptions.

Performance evaluation of unified medical language system®'s synonyms expansion to query PubMed

BackgroundPubMed is the main access to medical literature on the Internet. In order to enhance the performance of its information retrieval tools, primarily non-indexed citations, the authors propose a method: expanding users' queries using Unified Medical Language System' (UMLS) synonyms i.e. all the terms gathered under one unique Concept Unique Identifier.MethodsThis method was evaluated using queries constructed to emphasize the differences between this new method and the current PubMed automatic term mapping. Four experts assessed citation relevance.ResultsUsing UMLS, we were able to retrieve new citations in 45.5% of queries, which implies a small increase in recall. The new strategy led to a heterogeneous 23.7% mean increase in non-indexed citation retrieved. Of these, 82% have been published less than 4 months earlier. The overall mean precision was 48.4% but differed according to the evaluators, ranging from 36.7% to 88.1% (Inter rater agreement was poor: kappa = 0.34).ConclusionsThis study highlights the need for specific search tools for each type of user and use-cases. The proposed strategy may be useful to retrieve recent scientific advancement.

Eagle-i: An Ontology-Driven Framework For Biomedical Resource Curation And Discovery

The eagle-i Consortium ("http://www.eagle-i.org/home":www.eagle-i.org/home) comprises nine geographically and ethnically diverse universities across America working to build a federated network of research resources. Biomedical research generates many resources that are rarely shared or published, including: reagents, protocols, instruments, expertise, organisms, training opportunities, software, human studies, and biological specimens. The goal of eagle-i is to improve biomedical research by helping researchers more easily find scientific resources that are difficult to discover, reducing time-consuming and expensive duplication of resources. Now in early development, the system will ultimately expand to include research resources at other universities following the end of the two-year pilot phase. An application ontology is being developed to enable representation of core facility and research lab resources in the eagle-i repository, leading to more effective searches and better linkage between data types. The eagle-i ontology will guide users to valid queries via auto-suggestion, ontology browsing, concept-based search, and synonym expansion. The ontology development effort is being guided by active discussions within the ontology community and brings together relevant preexisting ontologies in a logical framework. Components of the data entry and search interfaces are generated directly from the ontology, which allows rapid change in response to user needs and ontology evolution. Each eagle-i institution will populate and manage a local repository using data collection and curation tools. To enhance the quantity and quality of data, the data tools will take advantage of the ontology to support semi-automated annotation of resources. NIH/NCRR ARRA award U24RR029825.

Eagle-i: An Ontology-Driven Framework For Biomedical Resource Curation And Discovery

AbstractThe eagle-i Consortium ("http://www.eagle-i.org/home":www.eagle-i.org/home) comprises nine geographically and ethnically diverse universities across America working to build a federated network of research resources. Biomedical research generates many resources that are rarely shared or published, including: reagents, protocols, instruments, expertise, organisms, training opportunities, software, human studies, and biological specimens. The goal of eagle-i is to improve biomedical research by helping researchers more easily find scientific resources that are difficult to discover, reducing time-consuming and expensive duplication of resources. Now in early development, the system will ultimately expand to include research resources at other universities following the end of the two-year pilot phase. An application ontology is being developed to enable representation of core facility and research lab resources in the eagle-i repository, leading to more effective searches and better linkage between data types. The eagle-i ontology will guide users to valid queries via auto-suggestion, ontology browsing, concept-based search, and synonym expansion. The ontology development effort is being guided by active discussions within the ontology community and brings together relevant preexisting ontologies in a logical framework. Components of the data entry and search interfaces are generated directly from the ontology, which allows rapid change in response to user needs and ontology evolution. Each eagle-i institution will populate and manage a local repository using data collection and curation tools. To enhance the quantity and quality of data, the data tools will take advantage of the ontology to support semi-automated annotation of resources. NIH/NCRR ARRA award U24RR029825.

An empirical study of gene synonym query expansion in biomedical information retrieval

Due to the heavy use of gene synonyms in biomedical text, people have tried many query expansion techniques using synonyms in order to improve performance in biomedical information retrieval. However, mixed results have been reported. The main challenge is that it is not trivial to assign appropriate weights to the added gene synonyms in the expanded query; under-weighting of synonyms would not bring much benefit, while overweighting some unreliable synonyms can hurt performance significantly. So far, there has been no systematic evaluation of various synonym query expansion strategies for biomedical text. In this work, we propose two different strategies to extend a standard language modeling approach for gene synonym query expansion and conduct a systematic evaluation of these methods on all the available TREC biomedical text collections for ad hoc document retrieval. Our experiment results show that synonym expansion can significantly improve the retrieval accuracy. However, different query types require different synonym expansion methods, and appropriate weighting of gene names and synonym terms is critical for improving performance.

GPSDB: a new database for synonyms expansion of gene and protein names

We present a new database, GPSDB (Gene and Protein Synonyms DataBase) which collects gene/protein names, in a species specific way, from 14 main biological resources. A web-based search interface gives access to the database: given a gene/protein name, it retrieves all synonyms for this entity and queries Medline with a set of user-selected terms. GPSDB is freely available from http://biomint.oefai.at/ johann@oefai.at.

The Co-Effects of Query Structure and Expansion on RetrievalPerformance in Probabilistic Text Retrieval

The effects of query structures and query expansion (QE) on retrieval performance were tested with a best match retrieval system (InQuery^1). Query structure means the use of operators to express the relations between search keys. Six different structures were tested, representing strong structures (e.g., queries with facets or concepts identified) and weak structures (no concepts identified, a query is ‘a bag of search keys’). QE was based on concepts, which were first selected from a searching thesaurus, and then expanded by semantic relationships given in the thesaurus. The expansion levels were (a) no expansion, (b) a synonym expansion, (c) a narrower concept expansion, (d) an associative concept expansion, and (e) a cumulative expansion of all other expansions. With weak structures and Boolean structured queries, QE was not very effective. The best performance was achieved with a combination of a facet structure, where search keys within a facet were treated as instances of one search key (the SYN operator), and the largest expansion.