Incorporating Semantic Word Representations into Query Expansion for Microblog Information Retrieval

Xu, Bo; Lin, Hongfei; Lin, Yuan; Wang, Lin; Gao, Ji-ping

doi:10.5755/j01.itc.48.4.22487

Cited by 4 publications

(5 citation statements)

References 29 publications

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“…These word representations or embeddings are automatically learned either during or before the training phase using methods such as Word2Vec (Mikolov et al 2013), GloVe (Pennington, Socher and Manning 2014) and fastText (Bojanowski et al 2017). The incorporation of morphology into this type of word embeddings was proposed for language modeling (Luong, Socher and Manning 2013; Santos and Zadrozny 2014; Bhatia, Guthrie and Eisenstein 2016; Lankinen et al 2016; Xu and Liu 2017) and for morphological tagging and segmentation (Shen et al 2016; Cotterell and Schütze 2017).…”

Section: Related Workmentioning

confidence: 99%

The effect of morphology in named entity recognition with sequence tagging

2018

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This work proposes a sequential tagger for named entity recognition in morphologically rich languages. Several schemes for representing the morphological analysis of a word in the context of named entity recognition are examined. Word representations are formed by concatenating word and character embeddings with the morphological embeddings based on these schemes. The impact of these representations is measured by training and evaluating a sequential tagger composed of a conditional random field layer on top of a bidirectional long short-term memory layer. Experiments with Turkish, Czech, Hungarian, Finnish and Spanish produce the state-of-the-art results for all these languages, indicating that the representation of morphological information improves performance.

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Section: Related Workmentioning

confidence: 99%

The effect of morphology in named entity recognition with sequence tagging

2018

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“…References [1,2] suggested the combinational sparsing of semantic features stored as inverted index Vs re-ranking features. The resultant from both techniques provides an optimized solution but overlooks the approach of the indexing technique in storing the resultant sets and data retrieval [3]. suggested the dominant color composition (DCC) signature of the zoned DCD signature extraction scheme.…”

Section: Related Workmentioning

confidence: 99%

Deep Root Memory Optimized Indexing Methodology for Image Search Engines

Karthikeyan¹,

Kavida²,

Suresh³

2022

Computer Systems Science and Engineering

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Digitization has created an abundance of new information sources by altering how pictures are captured. Accessing large image databases from a web portal requires an opted indexing structure instead of reducing the contents of different kinds of databases for quick processing. This approach paves a path toward the increase of efficient image retrieval techniques and numerous research in image indexing involving large image datasets. Image retrieval usually encounters difficulties like a) merging the diverse representations of images and their Indexing, b) the low-level visual characters and semantic characters associated with an image are indirectly proportional, and c) noisy and less accurate extraction of image information (semantic and predicted attributes). This work clearly focuses and takes the base of reverse engineering and de-normalizing concept by evaluating how data can be stored effectively. Thus, retrieval becomes straightforward and rapid. This research also deals with deep root indexing with a multidimensional approach about how images can be indexed and provides improved results in terms of good performance in query processing and the reduction of maintenance and storage cost. We focus on the schema design on a non-clustered index solution, especially cover queries. This schema provides a filter predication to make an index with a particular content of rows and an index table called filtered indexing. Finally, we include non-key columns in addition to the key columns. Experiments on two image data sets 'with and without' filtered indexing show low query cost. We compare efficiency as regards accuracy in mean average precision to measure the accuracy of retrieval with the developed coherent semantic indexing. The results show that retrieval by using deep root indexing is simple and fast.

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“…Paper [17] presented a classification system for identifying the type of disaster tweet. Research in the field of query processing can be classified, based on the source of expansion terms, into three groups: query expansion based on relevance feedback, query expansion based on local analysis, and query expansion based on global analysis [22]. Query expansion based on relevance feedback utilises feedback from the initial retrieval to enrich the original query.…”

Section: Related Workmentioning

confidence: 99%

“…The traditional text retrieval field applies the query mentioned above expansion methods. However, it is not easy to achieve the desired performance by directly using these methods in microblog retrieval [10,22]. The reason is that there is a large number of network vocabularies in microblogs and the junk text, without any useful information.…”

Section: Related Workmentioning

confidence: 99%

Classifying Micro-text Document Datasets: Application to Query Expansion of Crisis-Related Tweets

Farokhnejad

Pranesh

Espinosa-Oviedo

2021

Lecture Notes in Computer Science

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Twitter is an active communication channel for spreading information during crises (e.g., earthquake). To exploit this information, civilians require to explore the tweets produced along a crisis period. For instance, for getting information about crisis' related events (e.g. landslide, building collapse), and their associated relief actions (e.g., gathering of food supply, search for victims). However, such Twitter usage demand significant effort and answers must be accurate to support the coordination of actions in response to crisis events (e.g., avoiding a massive concentration of efforts in only one place). This requirement calls for efficient information classification so that people can perform agile and useful relief actions. This paper introduces an approach based on classification and query expansion techniques in the context of micro-texts (i.e., tweets) search. In our approach, a user's query is rewritten using a classified vocabulary derived from top-k results, to reflect her search intent better. For classification purpose, we study and compare different models to find the one that can best provide answers to a user query. Our experimental results show that the use of Multi-Task Deep Neural Network (MT-DNN) models further improves micro-text classification. Also, the experimental results demonstrate that our query expansion method is effective and reduces noise in the expanded query terms when looking for crisis tweets on Twitter datasets.

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Incorporating Semantic Word Representations into Query Expansion for Microblog Information Retrieval

Cited by 4 publications

References 29 publications

The effect of morphology in named entity recognition with sequence tagging

The effect of morphology in named entity recognition with sequence tagging

Deep Root Memory Optimized Indexing Methodology for Image Search Engines

Classifying Micro-text Document Datasets: Application to Query Expansion of Crisis-Related Tweets

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