Automatically Selecting Complementary Vector Representations for Semantic Textual Similarity

2019 ◽  
pp. 45-60
Author(s):  
Julien Hay ◽  
Tim Van de Cruys ◽  
Philippe Muller ◽  
Bich-Liên Doan ◽  
Fabrice Popineau ◽  
...  
Keyword(s):  
Vector Representations ◽  
Semantic Textual Similarity

scholarly journals Approaches to assessing the semantic similarity of texts in a multilingual space

2020 ◽  
Author(s):  
Aida Hakimova ◽  
Michael Charnine ◽  
Aleksey Klokov ◽  
Evgenii Sokolov
Keyword(s):  
Vector Space ◽  
Semantic Similarity ◽  
Semantic Space ◽  
Optimal Parameters ◽  
Quantitative Indicator ◽  
The Cross ◽  
Cross Lingual ◽  
Vector Representations ◽  
Semantic Textual Similarity

This paper is devoted to the development of a methodology for evaluating the semantic similarity of any texts in different languages is developed. The study is based on the hypothesis that the proximity of vector representations of terms in semantic space can be interpreted as a semantic similarity in the cross-lingual environment. Each text will be associated with a vector in a single multilingual semantic vector space. The measure of the semantic similarity of texts will be determined by the measure of the proximity of the corresponding vectors. We propose a quantitative indicator called Index of Semantic Textual Similarity (ISTS) that measures the degree of semantic similarity of multilingual texts on the basis of identified cross-lingual semantic implicit links. The setting of parameters is based on the correlation with the presence of a formal reference between documents. The measure of semantic similarity expresses the existence of two common terms, phrases or word combinations. Optimal parameters of the algorithm for identifying implicit links are selected on the thematic collection by maximizing the correlation of explicit and implicit connections. The developed algorithm can facilitate the search for close documents in the analysis of multilingual patent documentation.

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

Meerkat Mafia: Multilingual and Cross-Level Semantic Textual Similarity Systems

2014 ◽  
Author(s):  
Abhay Kashyap ◽  
Lushan Han ◽  
Roberto Yus ◽  
Jennifer Sleeman ◽  
Taneeya Satyapanich ◽  
...  
Keyword(s):  
Semantic Textual Similarity

Semantic Textual Similarity using Machine Learning and Conceptual Relatedness

2020 ◽  
Author(s):  
Shivam Varshney ◽  
Priyanka Sharma ◽  
Hira Javed
Keyword(s):  
Machine Learning ◽  
Semantic Textual Similarity

Scholar2vec: Vector Representation of Scholars for Lifetime Collaborator Prediction

2021 ◽  
Vol 15 (3) ◽  
pp. 1-19
Author(s):  
Wei Wang ◽  
Feng Xia ◽  
Jian Wu ◽  
Zhiguo Gong ◽  
Hanghang Tong ◽  
...  
Keyword(s):  
Scientific Collaboration ◽  
Early Stage ◽  
Collaboration Network ◽  
Vector Representation ◽  
Network Embedding ◽  
Machine Learning Methods ◽  
Academic Networks ◽  
Special Relationships ◽  
Real World Datasets ◽  
Vector Representations

While scientific collaboration is critical for a scholar, some collaborators can be more significant than others, e.g., lifetime collaborators. It has been shown that lifetime collaborators are more influential on a scholar’s academic performance. However, little research has been done on investigating predicting such special relationships in academic networks. To this end, we propose Scholar2vec, a novel neural network embedding for representing scholar profiles. First, our approach creates scholars’ research interest vector from textual information, such as demographics, research, and influence. After bridging research interests with a collaboration network, vector representations of scholars can be gained with graph learning. Meanwhile, since scholars are occupied with various attributes, we propose to incorporate four types of scholar attributes for learning scholar vectors. Finally, the early-stage similarity sequence based on Scholar2vec is used to predict lifetime collaborators with machine learning methods. Extensive experiments on two real-world datasets show that Scholar2vec outperforms state-of-the-art methods in lifetime collaborator prediction. Our work presents a new way to measure the similarity between two scholars by vector representation, which tackles the knowledge between network embedding and academic relationship mining.

Semantic Textual Similarity of Sentences with Emojis

2020 ◽  
Author(s):  
Alok Debnath ◽  
Nikhil Pinnaparaju ◽  
Manish Shrivastava ◽  
Vasudeva Varma ◽  
Isabelle Augenstein
Keyword(s):  
Semantic Textual Similarity

Polarity classification using structure-based vector representations of text

2015 ◽  
Vol 74 ◽  
pp. 46-56 ◽  
Author(s):  
Alexander Hogenboom ◽  
Flavius Frasincar ◽  
Franciska de Jong ◽  
Uzay Kaymak
Keyword(s):  
Polarity Classification ◽  
Vector Representations

SPECTRAL DECOMPOSITION OF R-MATRICES FOR EXCEPTIONAL LIE ALGEBRAS

1991 ◽  
Vol 06 (10) ◽  
pp. 923-927 ◽  
Author(s):  
S.M. SERGEEV
Keyword(s):  
Lie Algebras ◽  
Spectral Decomposition ◽  
Spectral Decompositions ◽  
Exceptional Lie Algebras ◽  
Exceptional Algebras ◽  
Vector Representations

In this paper spectral decompositions of R-matrices for vector representations of exceptional algebras are found.

scholarly journals Technological troubleshooting based on sentence embedding with deep transformers

2021 ◽  
Author(s):  
Antonio L. Alfeo ◽  
Mario G. C. A. Cimino ◽  
Gigliola Vaglini
Keyword(s):  
Technical Assistance ◽  
State Of The Art ◽  
Semantic Context ◽  
Retrieval Performance ◽  
Private Company ◽  
Textual Data ◽  
Context Knowledge ◽  
Development And Management ◽  
Preparation Module ◽  
Semantic Textual Similarity

AbstractIn nowadays manufacturing, each technical assistance operation is digitally tracked. This results in a huge amount of textual data that can be exploited as a knowledge base to improve these operations. For instance, an ongoing problem can be addressed by retrieving potential solutions among the ones used to cope with similar problems during past operations. To be effective, most of the approaches for semantic textual similarity need to be supported by a structured semantic context (e.g. industry-specific ontology), resulting in high development and management costs. We overcome this limitation with a textual similarity approach featuring three functional modules. The data preparation module provides punctuation and stop-words removal, and word lemmatization. The pre-processed sentences undergo the sentence embedding module, based on Sentence-BERT (Bidirectional Encoder Representations from Transformers) and aimed at transforming the sentences into fixed-length vectors. Their cosine similarity is processed by the scoring module to match the expected similarity between the two original sentences. Finally, this similarity measure is employed to retrieve the most suitable recorded solutions for the ongoing problem. The effectiveness of the proposed approach is tested (i) against a state-of-the-art competitor and two well-known textual similarity approaches, and (ii) with two case studies, i.e. private company technical assistance reports and a benchmark dataset for semantic textual similarity. With respect to the state-of-the-art, the proposed approach results in comparable retrieval performance and significantly lower management cost: 30-min questionnaires are sufficient to obtain the semantic context knowledge to be injected into our textual search engine.

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