S-Match: an Algorithm and an Implementation of Semantic Matching

Objective methods for competence evaluating of scientists in the subject domain pertinent to the specific scientific product – research project, publication, etc. are proposed. These methods are based on the semantic matching of the description of scientific product and documents that confirm the competence of its authors or experts in the domain of this product. In addition, the use of knowledge acquired from the Web open environment – Wiki-resources, scientometric databases, organization official website, domain ontologies is proposed. Specialized ontology of scientific activity which allows to standardize the terminological base for describing the qualifications of researchers is developed.

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FEATURES OF THE TRANSLATION OF V. MAYAKOVSKY'S POEMS INTO PERSIAN

RSUH/RGGU Bulletin. "Literary Theory. Linguistics. Cultural Studies" Series ◽

10.28995/2686-7249-2020-9-53-61 ◽

2020 ◽

pp. 53-61

Author(s):

Nahid Akbarzadeh ◽

Keyword(s):

Comparative Research ◽

Semantic Matching ◽

Vladimir Mayakovsky

This article is dedicated to the translation of poems written by the popular and famous Russian poet Vladimir Mayakovsky into Persian, as well as to their features and general trends of the translation of Futurist poems. The work was performed in the technique of comparative research, where one of the most acute issues is formulated as the degree of translatability / untranslatability of the text. The purpose is to find appropriate approaches for its solution and consider the issue of semantic matching.

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Visual and auditory verbal working memory affects visual attention in the semantic matching

Acta Psychologica Sinica ◽

10.3724/sp.j.1041.2018.00483 ◽

2018 ◽

Vol 50 (5) ◽

pp. 483 ◽

Cited By ~ 3

Keyword(s):

Working Memory ◽

Visual Attention ◽

Verbal Working Memory ◽

Semantic Matching

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Hybrid Deep Neural Model for Duplicate Question Detection in Transliterated Bi-lingual Data

Recent Patents on Computer Science ◽

10.2174/2213275912666190710152709 ◽

2019 ◽

Vol 12 ◽

Author(s):

Seema Rani ◽

Avadhesh Kumar ◽

Naresh Kumar

Keyword(s):

Question Answering ◽

Neural Model ◽

Manhattan Distance ◽

Semantic Matching ◽

English Only ◽

Detection Model ◽

Proposed Model ◽

Reported Study ◽

Social Media Platforms ◽

Efficient Information

Background: Duplicate content often corrupts the filtering mechanism in online question answering. Moreover, as users are usually more comfortable conversing in their native language questions, transliteration adds to the challenges in detecting duplicate questions. This compromises with the response time and increases the answer overload. Thus, it has now become crucial to build clever, intelligent and semantic filters which semantically match linguistically disparate questions. Objective: Most of the research on duplicate question detection has been done on mono-lingual, majorly English Q&A platforms. The aim is to build a model which extends the cognitive capabilities of machines to interpret, comprehend and learn features for semantic matching in transliterated bi-lingual Hinglish (Hindi + English) data acquired from different Q&A platforms. Method: In the proposed DQDHinglish (Duplicate Question Detection) Model, firstly language transformation (transliteration & translation) is done to convert the bi-lingual transliterated question into a mono-lingual English only text. Next a hybrid of Siamese neural network containing two identical Long-term-Short-memory (LSTM) models and Multi-layer perceptron network is proposed to detect semantically similar question pairs. Manhattan distance function is used as the similarity measure. Result: A dataset was prepared by scrapping 100 question pairs from various social media platforms, such as Quora and TripAdvisor. The performance of the proposed model on the basis of accuracy and F-score. The proposed DQDHinglish achieves a validation accuracy of 82.40%. Conclusion: A deep neural model was introduced to find semantic match between English question and a Hinglish (Hindi + English) question such that similar intent questions can be combined to enable fast and efficient information processing and delivery. A dataset was created and the proposed model was evaluated on the basis of performance accuracy. To the best of our knowledge, this work is the first reported study on transliterated Hinglish semantic question matching.

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Single-shot Semantic Matching Network for Moment Localization in Videos

ACM Transactions on Multimedia Computing Communications and Applications ◽

10.1145/3441577 ◽

2021 ◽

Vol 17 (3) ◽

pp. 1-14

Author(s):

Xinfang Liu ◽

Xiushan Nie ◽

Junya Teng ◽

Li Lian ◽

Yilong Yin

Keyword(s):

Natural Language ◽

Fixed Number ◽

Single Shot ◽

Semantic Matching ◽

Matching Network ◽

Attention Model ◽

Semantic Relationships ◽

Natural Language Query ◽

Benchmark Datasets ◽

Short Memory

Moment localization in videos using natural language refers to finding the most relevant segment from videos given a natural language query. Most of the existing methods require video segment candidates for further matching with the query, which leads to extra computational costs, and they may also not locate the relevant moments under any length evaluated. To address these issues, we present a lightweight single-shot semantic matching network (SSMN) to avoid the complex computations required to match the query and the segment candidates, and the proposed SSMN can locate moments of any length theoretically. Using the proposed SSMN, video features are first uniformly sampled to a fixed number, while the query sentence features are generated and enhanced by GloVe, long-term short memory (LSTM), and soft-attention modules. Subsequently, the video features and sentence features are fed to an enhanced cross-modal attention model to mine the semantic relationships between vision and language. Finally, a score predictor and a location predictor are designed to locate the start and stop indexes of the query moment. We evaluate the proposed method on two benchmark datasets and the experimental results demonstrate that SSMN outperforms state-of-the-art methods in both precision and efficiency.

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