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Integrating and Evaluating Neural Word Embeddings in Information Retrieval
2015
Proceedings of the 20th Australasian Document Computing Symposium on ZZZ - ADCS '15
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To this aim, we use neural word embeddings within the well known translation language model for information retrieval. ...
Motivated by these observations, in this paper, we set out to determine how these word embeddings can be used within a retrieval model and what the benefit might be. ...
The hypothesis of this paper is that these word embeddings can be exploited in information retrieval. ...
doi:10.1145/2838931.2838936
dblp:conf/adcs/ZucconKBA15
fatcat:2z6gtv42t5f57chcojzjio3yde
Question Retrieval for Community-based Question Answering via Heterogeneous Network Integration Learning
[article]
2016
arXiv
pre-print
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The challenges in this task are the lexical gaps between questions for the word ambiguity and word mismatch problem. Furthermore, limited words in queried sentences cause sparsity of word features. ...
More specifically, we apply random walk based learning method with recurrent neural network to match the similarities between askers question and historical questions proposed by other users. ...
Among these six baselines, the VSM and BM25 methods are the traditional algorithms used in information retrieval and learn the question model only based on bag-of-words contents. ...
arXiv:1611.08135v1
fatcat:vptyyihojbgnvg2zgbys6c5loq
Deep Neural Networks for Query Expansion using Word Embeddings
[article]
2018
arXiv
pre-print
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Query expansion is a method for alleviating the vocabulary mismatch problem present in information retrieval tasks. ...
We then introduce an artificial neural network classifier to predict the usefulness of query expansion terms. This classifier uses term word embeddings as inputs. ...
Experiment
Comparison Approaches For evaluation, we only compare to methods that select expansion term candidates based on word embeddings and not other information sources such as the top retrieved ...
arXiv:1811.03514v1
fatcat:nu2iw4u6frcepnvmsum4oahiwy
Entity-Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval
[article]
2018
arXiv
pre-print
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The two components are learned end-to-end, making EDRM a natural combination of entity-oriented search and neural information retrieval. ...
EDRM represents queries and documents by their words and entity annotations. ...
as well as the China-Singapore Joint Research Project of the National Natural Science Foundation of China (No. 61661146007) under the umbrella of the NexT Joint Research Center of Tsinghua University and ...
arXiv:1805.07591v2
fatcat:ns3hh65qhbbmvfmku6nm4ky6d4
Convolutional Neural Network: Text Classification Model for Open Domain Question Answering System
[article]
2019
arXiv
pre-print
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Neural network model is trained on top of word embedding. Softmax layer is applied to calculate loss and mapping of semantically related words. ...
A typical Question Answering System is fairly an information retrieval system, which matches documents or text and retrieve the most accurate one. ...
Text categorization is the research focus and key technology in the field of information retrieval and data mining since the amount of electronic text information has been rapidly increasing [9] . ...
arXiv:1809.02479v2
fatcat:uvhmz27tlfcljkvytjvks55x5i
Entity-Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval
2018
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
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The two components are learned end-to-end, making EDRM a natural combination of entityoriented search and neural information retrieval. ...
EDRM represents queries and documents by their words and entity annotations. ...
as well as the China-Singapore Joint Research Project of the National Natural Science Foundation of China (No. 61661146007) under the umbrella of the NexT Joint Research Center of Tsinghua University and ...
doi:10.18653/v1/p18-1223
dblp:conf/acl/SunLXL18
fatcat:uoal5wyyzvhylgahyrnisfltpi
Principle-to-Program: Neural Methods for Similar Question Retrieval in Online Communities
[chapter]
2020
Lecture Notes in Computer Science
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Similar question retrieval is a challenge due to lexical gap between query and candidates in archive and is very different from traditional IR methods for duplicate detection, paraphrase identification ...
and semantic equivalence. ...
We discuss: - [11] combines FrameNet with neural networks through ensemble and embedding approaches for question retrieval with constituent matching, - [13] integrates shallow lexical mismatching information ...
doi:10.1007/978-3-030-45442-5_88
fatcat:rdzo4nrrzbhgvd54zkykt4zi4a
The Cross-Lingual Arabic Information REtrieval (CLAIRE) System
[article]
2021
arXiv
pre-print
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of Arabic and various supporting information in English is provided to aid retrieval experience. ...
Despite advances in neural machine translation, cross-lingual retrieval tasks in which queries and documents live in different natural language spaces remain challenging. ...
Rank fusion is the natural and popular technique to integrate ranking information from dierent sources. ...
arXiv:2107.13751v1
fatcat:nit7lsgg4jdkvmcrz2f6tuk3lu
Getting Started with Neural Models for Semantic Matching in Web Search
[article]
2016
arXiv
pre-print
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The vocabulary mismatch problem is a long-standing problem in information retrieval. Semantic matching holds the promise of solving the problem. ...
Recent advances in language technology have given rise to unsupervised neural models for learning representations of words as well as bigger textual units. ...
Reflections on evaluation Evaluation of word embedding-based approaches has been performed on different retrieval data sets with different choices of corpora for learning word embeddings. ...
arXiv:1611.03305v1
fatcat:agdgj7allbczxcyteuomswn574
Neural word and entity embeddings for ad hoc retrieval
2018
Information Processing & Management
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A B S T R A C T Learning low dimensional dense representations of the vocabularies of a corpus, known as neural embeddings, has gained much attention in the information retrieval community. ...
In this paper, we perform a methodical study on how neural embeddings in uence the ad hoc document retrieval task. ...
Word embedding based generalized language model for information retrieval. Proceedings of the thirty-eighth international ACM SIGIR conference on research and development in information retrieval. ...
doi:10.1016/j.ipm.2018.04.007
fatcat:ctlnqrysnfgytfdkpux5avqg6y
Neural Embeddings for the Elicitation of Jurisprudence Principles: The Case of Arabic Legal Texts
2022
Applied Sciences
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In addition, we explored an approach that integrates task-oriented word embeddings (ToWE) with document embeddings (paragraph vectors). ...
The findings of this study have significant implications for the understanding of how Arabic legal texts can be modeled and how the semantics of jurisprudence principles can be elicited using neural embeddings ...
Sci. 2022, 12, 4188 13 of 14 thank the domain experts who contributed in the human evaluation phase of this study, namely Sulaiman Al-Turki, Luluah AlYahya, Nada Qushami, and Amal Alnafesah. ...
doi:10.3390/app12094188
fatcat:o45fnnez7jhftmwidlsk32nfhu
Joint Learning of Sentence Embeddings for Relevance and Entailment
[article]
2016
arXiv
pre-print
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We compare several variants of neural networks for sentence embeddings in a setting of decision-making based on evidence of varying relevance. ...
, and show the importance of evaluating strong baselines. ...
Acknowledgments This work was co-funded by the Augur Project of the Forecast Foundation and financially supported by the Grant Agency of the Czech Technical University in Prague, grant No. ...
arXiv:1605.04655v2
fatcat:55vus7erzffazkwgl32jfj7qm4
Joint Learning of Sentence Embeddings for Relevance and Entailment
2016
Proceedings of the 1st Workshop on Representation Learning for NLP
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We compare several variants of neural networks for sentence embeddings in a setting of decision-making based on evidence of varying relevance. ...
, and show the importance of evaluating strong baselines. ...
University in Prague, grant No. ...
doi:10.18653/v1/w16-1602
dblp:conf/rep4nlp/BaudisSS16
fatcat:chonwkmuujcwbo6h7iexalhsnu
Evaluating semantic relations in neural word embeddings with biomedical and general domain knowledge bases
2018
BMC Medical Informatics and Decision Making
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In the past few years, neural word embeddings have been widely used in text mining. ...
We trained multiple word embeddings using health-related articles in Wikipedia and then evaluated their performance in the analogy and semantic relation term retrieval tasks. ...
Acknowledgements We would like to thank Sihui Liu from Department of Mathematics at Florida State University for her help in processing the medical-related semantic relations from the Unified Medical Language ...
doi:10.1186/s12911-018-0630-x
pmid:30066651
pmcid:PMC6069806
fatcat:nsuli7yixbdmncnrpuijavt7eu
Text mining policy: Classifying forest and landscape restoration policy agenda with neural information retrieval
[article]
2019
arXiv
pre-print
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An unsupervised neural information retrieval architecture is introduced that leverages transfer learning and word embeddings to create high-dimensional representations of paragraphs. ...
Policy agenda labels are recast as information retrieval queries in order to classify policies with a cosine similarity threshold between paragraphs and query embeddings. ...
This paper addresses these issues facing text mining in policy analysis by introducing a neural information retrieval approach to classifying policy agenda with word embeddings. ...
arXiv:1908.02425v1
fatcat:w6vcrq7bbrbvxma5p5nqqfx4hq
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