Enhanced word embedding similarity measures using fuzzy rules for query expansion

© 2017 IEEE. Query expansion has been widely used to select additional words that are related to the original query words in the field of information retrieval. In this paper, we present a novel query expansion method that jointly uses fuzzy rules and a word embedding similarity calculation. The expansion words are generated using a word embedding method and selected according to their semantic similarity to the original query. Fuzzy rules are used to enhance the word similarity calculations and reweight expansion words. When measuring and ranking the relevance of a retrieved document, the original query and the expansion words with their weights are considered. We conduct experiments on the query expansion in document ranking tasks. Experimental results from the document ranking task show that the proposed method is able to significantly outperform state-of-the-art baseline methods

Improving average ranking precision in user searches for biomedical research datasets

Availability of research datasets is keystone for health and life science study reproducibility and scientific progress. Due to the heterogeneity and complexity of these data, a main challenge to be overcome by research data management systems is to provide users with the best answers for their search queries. In the context of the 2016 bioCADDIE Dataset Retrieval Challenge, we investigate a novel ranking pipeline to improve the search of datasets used in biomedical experiments. Our system comprises a query expansion model based on word embeddings, a similarity measure algorithm that takes into consideration the relevance of the query terms, and a dataset categorisation method that boosts the rank of datasets matching query constraints. The system was evaluated using a corpus with 800k datasets and 21 annotated user queries. Our system provides competitive results when compared to the other challenge participants. In the official run, it achieved the highest infAP among the participants, being +22.3% higher than the median infAP of the participant's best submissions. Overall, it is ranked at top 2 if an aggregated metric using the best official measures per participant is considered. The query expansion method showed positive impact on the system's performance increasing our baseline up to +5.0% and +3.4% for the infAP and infNDCG metrics, respectively. Our similarity measure algorithm seems to be robust, in particular compared to Divergence From Randomness framework, having smaller performance variations under different training conditions. Finally, the result categorization did not have significant impact on the system's performance. We believe that our solution could be used to enhance biomedical dataset management systems. In particular, the use of data driven query expansion methods could be an alternative to the complexity of biomedical terminologies

Word Representation with Transferable Semantics

University of Technology Sydney. Faculty of Engineering and Information Technology.This thesis is about semantic representation which is a core research problem in text-based machine learning such as natural language processing and information retrieval. The target of this thesis is to improve representation learning methods by utilising transferable semantics extracted from source domains. Specifically, this thesis aims to address four research questions: 1) how to reliably transfer semantics from a structural knowledge base to an unstructured representation space; 2) how to reliably transfer semantics from multiple source domains to a low-resource target domain; 3) how to achieve the reliable and low-cost cross-lingual transfer of semantics; and 4) how to adapt semantic representations for specific applications. To solve these questions, this thesis proposes a set of effective representation methods by exploring and modeling knowledge from 1) knowledge bases; 2) multiple pre-trained embeddings; 3) high-resource languages; and 4) task-related semantics. Comprehensive experiments and case studies have been conducted to evaluate and demonstrate the superior performance of the proposed method compared with baseline methods. To conclude, this thesis proposes a set of effective methods to improve semantic representation by exploring and modeling knowledge beyond raw text and places an emphasis on encoding task-specific features for real-world applications

Terms interrelationship query expansion to improve accuracy of Quran search

Quran retrieval system is becoming an instrument for users to search for needed information. The search engine is one of the most popular search engines that successfully implemented for searching relevant verses queries. However, a major challenge to the Quran search engine is word ambiguities, specifically lexical ambiguities. With the advent of query expansion techniques for Quran retrieval systems, the performance of the Quran retrieval system has problem and issue in terms of retrieving users needed information. The results of the current semantic techniques still lack precision values without considering several semantic dictionaries. Therefore, this study proposes a stemmed terms interrelationship query expansion approach to improve Quran search results. More specifically, related terms were collected from different semantic dictionaries and then utilize to get roots of words using a stemming algorithm. To assess the performance of the stemmed terms interrelationship query expansion, experiments were conducted using eight Quran datasets from the Tanzil website. Overall, the results indicate that the stemmed terms interrelationship query expansion is superior to unstemmed terms interrelationship query expansion in Mean Average Precision with Yusuf Ali 68%, Sarawar 67%, Arberry 72%, Malay 65%, Hausa 62%, Urdu 62%, Modern Arabic 60% and Classical Arabic 59%

Ontology-based approach to semantically enhanced question answering for closed domain: a review

Abstract: For many users of natural language processing (NLP), it can be challenging to obtain concise, accurate and precise answers to a question. Systems such as question answering (QA) enable users to ask questions and receive feedback in the form of quick answers to questions posed in natural language, rather than in the form of lists of documents delivered by search engines. This task is challenging and involves complex semantic annotation and knowledge representation. This study reviews the literature detailing ontology-based methods that semantically enhance QA for a closed domain, by presenting a literature review of the relevant studies published between 2000 and 2020. The review reports that 83 of the 124 papers considered acknowledge the QA approach, and recommend its development and evaluation using different methods. These methods are evaluated according to accuracy, precision, and recall. An ontological approach to semantically enhancing QA is found to be adopted in a limited way, as many of the studies reviewed concentrated instead on NLP and information retrieval (IR) processing. While the majority of the studies reviewed focus on open domains, this study investigates the closed domain

Natural Language Processing in-and-for Design Research

We review the scholarly contributions that utilise Natural Language Processing (NLP) methods to support the design process. Using a heuristic approach, we collected 223 articles published in 32 journals and within the period 1991-present. We present state-of-the-art NLP in-and-for design research by reviewing these articles according to the type of natural language text sources: internal reports, design concepts, discourse transcripts, technical publications, consumer opinions, and others. Upon summarizing and identifying the gaps in these contributions, we utilise an existing design innovation framework to identify the applications that are currently being supported by NLP. We then propose a few methodological and theoretical directions for future NLP in-and-for design research

Enhancing Term-Based Document Retrieval by Word Embedding and Transformer Models

Document retrieval implies the process of obtaining most relevant documents based on some query from a large corpus of documents. Traditional document retrieval methods focus on the existence and/or non-existence of the query terms in a particular document to assess relevance of the document to the query. However, a document can be contextually relevant to some query without containing the exact query words, or the document might contain the query term and still be about some completely different topic. Hence arises the need of context aware document retrieval systems. In this thesis, we focus on enhancing document retrieval methods in order to capture the contextual relevance of a document to a certain query. The primary components used to achieve our goals are word embedding models and transformer-based pre-trained natural language models. We propose three different approaches for enhancing document retrieval methods. We use three different datasets to evaluate our models and compare the results with classical document retrieval models

Entity Linking for the Biomedical Domain

Entity linking is the process of detecting mentions of different concepts in text documents and linking them to canonical entities in a target lexicon. However, one of the biggest issues in entity linking is the ambiguity in entity names. The ambiguity is an issue that many text mining tools have yet to address since different names can represent the same thing and every mention could indicate a different thing. For instance, search engines that rely on heuristic string matches frequently return irrelevant results, because they are unable to satisfactorily resolve ambiguity. Thus, resolving named entity ambiguity is a crucial step in entity linking. To solve the problem of ambiguity, this work proposes a heuristic method for entity recognition and entity linking over the biomedical knowledge graph concerning the semantic similarity of entities in the knowledge graph. Named entity recognition (NER), relation extraction (RE), and relationship linking make up a conventional entity linking (EL) system pipeline (RL). We have used the accuracy metric in this thesis. Therefore, for each identified relation or entity, the solution comprises identifying the correct one and matching it to its corresponding unique CUI in the knowledge base. Because KBs contain a substantial number of relations and entities, each with only one natural language label, the second phase is directly dependent on the accuracy of the first. The framework developed in this thesis enables the extraction of relations and entities from the text and their mapping to the associated CUI in the UMLS knowledge base. This approach derives a new representation of the knowledge base that lends it to the easy comparison. Our idea to select the best candidates is to build a graph of relations and determine the shortest path distance using a ranking approach. We test our suggested approach on two well-known benchmarks in the biomedical field and show that our method exceeds the search engine's top result and provides us with around 4% more accuracy. In general, when it comes to fine-tuning, we notice that entity linking contains subjective characteristics and modifications may be required depending on the task at hand. The performance of the framework is evaluated based on a Python implementation

Representation Learning for Words and Entities

This thesis presents new methods for unsupervised learning of distributed representations of words and entities from text and knowledge bases. The first algorithm presented in the thesis is a multi-view algorithm for learning representations of words called Multiview Latent Semantic Analysis (MVLSA). By incorporating up to 46 different types of co-occurrence statistics for the same vocabulary of english words, I show that MVLSA outperforms other state-of-the-art word embedding models. Next, I focus on learning entity representations for search and recommendation and present the second method of this thesis, Neural Variational Set Expansion (NVSE). NVSE is also an unsupervised learning method, but it is based on the Variational Autoencoder framework. Evaluations with human annotators show that NVSE can facilitate better search and recommendation of information gathered from noisy, automatic annotation of unstructured natural language corpora. Finally, I move from unstructured data and focus on structured knowledge graphs. I present novel approaches for learning embeddings of vertices and edges in a knowledge graph that obey logical constraints.Comment: phd thesis, Machine Learning, Natural Language Processing, Representation Learning, Knowledge Graphs, Entities, Word Embeddings, Entity Embedding