A Semantic neighborhood approach to relatedness evaluation on well-founded domain ontologies

In the context of natural language processing and information retrieval, ontologies can improve the results of the word sense disambiguation (WSD) techniques. By making explicit the semantics of the term, ontology-based semantic measures play a crucial role in determining how different ontology classes have a similar or related meaning. In this context, it is common to use semantic similarity as a basis for WSD. However, the measures generally consider only taxonomic relationships, which negatively affect the discrimination of two ontology classes that are related by the other relationship types. On the other hand, semantic relatedness measures consider diverse types of relationships to determine how much two classes on the ontology are related. However, these measures, especially the path-based approaches, have as the main drawback a high computational complexity to calculate the relatedness value. Also, for both types of semantic measures, it is unpractical to store all similarity or relatedness values between all ontology classes in memory, especially for ontologies with a large number of classes. In this work, we propose a novel approach based on semantic neighbors that aim to improve the performance of the knowledge-based measures in relatedness analysis. We also explain how to use this proposal into the path and feature-based measures. We evaluate our proposal on WSD using an existent domain ontology for a well-core description. This ontology contains 929 classes related to rock facies. Also, we use a set of sentences from four different corpora on the Oil&Gas domain. In the experiments, we compare our proposal with state-of-the-art semantic relatedness measures, such as path-based, feature-based, information content, and hybrid methods regarding the F-score, evaluation time, and memory consumption. The experimental results show that the proposed method obtains F-score gains in WSD, as well as a low evaluation time and memory consumption concerning the traditional knowledge-based measures.No contexto do processamento de linguagem natural e recuperação de informações, as ontologias podem melhorar os resultados das técnicas de desambiguação. Ao tornar explícita a semântica do termo, as medidas semânticas baseadas em ontologia desempenham um papel crucial para determinar como diferentes classes de ontologia têm um significado semelhante ou relacionado. Nesse contexto, é comum usar similaridade semântica como base para a desembiguação. No entanto, as medidas geralmente consideram apenas relações taxonômicas, o que afeta negativamente a discriminação de duas classes de ontologia relacionadas por outros tipos de relações. Por outro lado, as medidas de relacionamento semântico consideram diversos tipos de relacionamentos ontológicos para determinar o quanto duas classes estão relacionadas. No entanto, essas medidas, especialmente as abordagens baseadas em caminhos, têm como principal desvantagem uma alta complexidade computacional para sua execução. Além disso, tende a ser impraticável armazenar na memória todos os valores de similaridade ou relacionamento entre todas as classes de uma ontologia, especialmente para ontologias com um grande número de classes. Neste trabalho, propomos uma nova abordagem baseada em vizinhos semânticos que visa melhorar o desempenho das medidas baseadas em conhecimento na análise de relacionamento. Também explicamos como usar esta proposta em medidas baseadas em caminhos e características. Avaliamos nossa proposta na desambiguação utilizando uma ontologia de domínio preexistente para descrição de testemunhos. Esta ontologia contém 929 classes relacionadas a fácies de rocha. Além disso, usamos um conjunto de sentenças de quatro corpora diferentes no domínio Petróleo e Gás. Em nossos experimentos, comparamos nossa proposta com medidas de relacionamento semântico do estado-daarte, como métodos baseados em caminhos, características, conteúdo de informação, e métodos híbridos em relação ao F-score, tempo de avaliação e consumo de memória. Os resultados experimentais mostram que o método proposto obtém ganhos de F-score na desambiguação, além de um baixo tempo de avaliação e consumo de memória em relação às medidas tradicionais baseadas em conhecimento

Using Social Media to Combat Opioid Epidemic

Opioid addiction has become one of the largest and deadliest epidemics in the United States. To combat such deadly epidemic, there is an urgent need for novel tools and methodologies to gain new insights into the behavioral processes of opioid abuse and addiction. The role of social media in biomedical knowledge mining has turned into increasingly significant in recent years. The data from social media may contribute information beyond the knowledge of domain professionals (e.g., psychiatrists and epidemics researchers) and could potentially assist in sharpening our understanding toward the behavioral process of opioid addiction and treatment. In this thesis, we propose a novel framework to automate the analysis of social media (i.e., Twitter) for the detection of the opioid users. To model the Twitter users and posted tweets as well as their rich relationships, we constructed a structured heterogeneous information network (HIN) for representation. We then introduce a meta-path-based approach to characterize the semantic relatedness over users. As different meta-paths depict the relatedness over users at different views, we used Laplacian scores to aggregate different similarities formulated by different meta-paths and then a transductive classification model was built to make predictions. We conduct a comprehensive experimental study based on the real sample collections from Twitter to validate the effectiveness of our proposed approach. To improve the performance of automatic opioid user detection, we presented a meta-structure-based method to depict relatedness and integrate content-based similarity to formulate a similarity measure over users. We then aggregate different similarities using multi-kernel learning for opioid user detection. Comprehensive experimental results on real sample collections from Twitter demonstrate the effectiveness of our proposed learning models

Role of semantic indexing for text classification.

The Vector Space Model (VSM) of text representation suffers a number of limitations for text classification. Firstly, the VSM is based on the Bag-Of-Words (BOW) assumption where terms from the indexing vocabulary are treated independently of one another. However, the expressiveness of natural language means that lexically different terms often have related or even identical meanings. Thus, failure to take into account the semantic relatedness between terms means that document similarity is not properly captured in the VSM. To address this problem, semantic indexing approaches have been proposed for modelling the semantic relatedness between terms in document representations. Accordingly, in this thesis, we empirically review the impact of semantic indexing on text classification. This empirical review allows us to answer one important question: how beneficial is semantic indexing to text classification performance. We also carry out a detailed analysis of the semantic indexing process which allows us to identify reasons why semantic indexing may lead to poor text classification performance. Based on our findings, we propose a semantic indexing framework called Relevance Weighted Semantic Indexing (RWSI) that addresses the limitations identified in our analysis. RWSI uses relevance weights of terms to improve the semantic indexing of documents. A second problem with the VSM is the lack of supervision in the process of creating document representations. This arises from the fact that the VSM was originally designed for unsupervised document retrieval. An important feature of effective document representations is the ability to discriminate between relevant and non-relevant documents. For text classification, relevance information is explicitly available in the form of document class labels. Thus, more effective document vectors can be derived in a supervised manner by taking advantage of available class knowledge. Accordingly, we investigate approaches for utilising class knowledge for supervised indexing of documents. Firstly, we demonstrate how the RWSI framework can be utilised for assigning supervised weights to terms for supervised document indexing. Secondly, we present an approach called Supervised Sub-Spacing (S3) for supervised semantic indexing of documents. A further limitation of the standard VSM is that an indexing vocabulary that consists only of terms from the document collection is used for document representation. This is based on the assumption that terms alone are sufficient to model the meaning of text documents. However for certain classification tasks, terms are insufficient to adequately model the semantics needed for accurate document classification. A solution is to index documents using semantically rich concepts. Accordingly, we present an event extraction framework called Rule-Based Event Extractor (RUBEE) for identifying and utilising event information for concept-based indexing of incident reports. We also demonstrate how certain attributes of these events e.g. negation, can be taken into consideration to distinguish between documents that describe the occurrence of an event, and those that mention the non-occurrence of that event

Integrative priming occurs rapidly and uncontrollably during lexical processing

Lexical priming, whereby a prime word facilitates recognition of a related target word (e.g., nurse ? doctor), is typically attributed to association strength, semantic similarity, or compound familiarity. Here, the authors demonstrate a novel type of lexical priming that occurs among unassociated, dissimilar, and unfamiliar concepts (e.g., horse ? doctor). Specifically, integrative priming occurs when a prime word can be easily integrated with a target word to create a unitary representation. Across several manipulations of timing (stimulus onset asynchrony) and list context (relatedness proportion), lexical decisions for the target word were facilitated when it could be integrated with the prime word. Moreover, integrative priming was dissociated from both associative priming and semantic priming but was comparable in terms of both prevalence (across participants) and magnitude (within participants). This observation of integrative priming challenges present models of lexical priming, such as spreading activation, distributed representation, expectancy, episodic retrieval, and compound cue models. The authors suggest that integrative priming may be explained by a role activation model of relational integration

Measuring associational thinking through word embeddings

[EN] The development of a model to quantify semantic similarity and relatedness between words has been the major focus of many studies in various fields, e.g. psychology, linguistics, and natural language processing. Unlike the measures proposed by most previous research, this article is aimed at estimating automatically the strength of associative words that can be semantically related or not. We demonstrate that the performance of the model depends not only on the combination of independently constructed word embeddings (namely, corpus- and network-based embeddings) but also on the way these word vectors interact. The research concludes that the weighted average of the cosine-similarity coefficients derived from independent word embeddings in a double vector space tends to yield high correlations with human judgements. Moreover, we demonstrate that evaluating word associations through a measure that relies on not only the rank ordering of word pairs but also the strength of associations can reveal some findings that go unnoticed by traditional measures such as Spearman's and Pearson's correlation coefficients.s Financial support for this research has been provided by the Spanish Ministry of Science, Innovation and Universities [grant number RTC 2017-6389-5], the Spanish ¿Agencia Estatal de Investigación¿ [grant number PID2020-112827GB-I00 / AEI / 10.13039/501100011033], and the European Union¿s Horizon 2020 research and innovation program [grant number 101017861: project SMARTLAGOON]. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.Periñán-Pascual, C. (2022). Measuring associational thinking through word embeddings. Artificial Intelligence Review. 55(3):2065-2102. https://doi.org/10.1007/s10462-021-10056-62065210255

Exploiting semantics for improving clinical information retrieval

Clinical information retrieval (IR) presents several challenges including terminology mismatch and granularity mismatch. One of the main objectives in clinical IR is to fill the semantic gap among the queries and documents and going beyond keywords matching. To address these issues, in this study we attempt to use semantic information to improve the performance of clinical IR systems by representing queries in an expressive and meaningful context. In this study we propose query context modeling to improve the effectiveness of clinical IR systems. To model query contexts we propose two novel approaches to modeling medical query contexts. The first approach concerns modeling medical query contexts based on mining semantic-based AR for improving clinical text retrieval. The query context is derived from the rules that cover the query and then weighted according to their semantic relatedness to the query concepts. In our second approach we model a representative query context by developing query domain ontology. To develop query domain ontology we extract all the concepts that have semantic relationship with the query concept(s) in UMLS ontologies. Query context represents concepts extracted from query domain ontology and weighted according to their semantic relatedness to the query concept(s). The query context is then exploited in the patient records query expansion and re-ranking for improving clinical retrieval performance. We evaluate this approach on the TREC Medical Records dataset. Results show that our proposed approach significantly improves the retrieval performance compare to classic keyword-based IR model