The inclusion of semantic information in any similarity measures improves the efficiency of the similarity measure and provides human interpretable results for further analysis. The similarity calculation method that focuses on features related to the text's words only, will give less accurate results. This paper presents three different methods that not only focus on the text's words but also incorporates semantic information of texts in their feature vector and computes semantic similarities. These methods are based on corpus-based and knowledge-based methods, which are: cosine similarity using tf-idf vectors, cosine similarity using word embedding and soft cosine similarity using word embedding. Among these three, cosine similarity using tf-idf vectors performed best in finding similarities between short news texts. The similar texts given by the method are easy to interpret and can be used directly in other information retrieval applications.

A Comparison of Semantic Similarity Methods for Maximum Human Interpretability

Most of traditional trajectory clustering algorithms often cluster similar trajectories from a temporal or spatial perspective. One weak point is that the semantic relationship between the trajectories is ignored. In some cases, trajectories with spatio-temporal similarities may be semantically related, and the negligence of semantic information may result in unreasonable trajectory clustering results. In addition, the existing semantic trajectory clustering algorithms only consider the local semantic relationship between adjacent spatio-temporal trajectories, and the overall global semantic relationship between trajectories is still unknown. Considering the disadvantages of the current trajectory clustering methods, we proposed a novel algorithm for semantic trajectory clustering based on community detection (STCCD) in networks, which can better measure the semantic similarity of trajectories and capture global relationship among trajectories from the perspective of the network, and can get better trajectory clustering results compared to some traditional and recently proposed methods. Experimental results demonstrate that the proposed method can effectively mine the trajectory clustering information and related knowledge from the semantic trajectory data.

STCCD: Semantic trajectory clustering based on community detection in networks

Background: Natural Language Understanding enables automatic extraction of relevant information from clinical text data, which are acquired every day in hospitals. In 2018, the language model Bidirectional Encoder Representations from Transformers (BERT) was introduced, generating new state-of-the-art results on several downstream tasks. The National NLP Clinical Challenges (n2c2) is an initiative that strives to tackle such downstream tasks on domain-specific clinical data. In this paper, we present the results of our participation in the 2019 n2c2 and related work completed thereafter.
Objective: The objective of this study was to optimally leverage BERT for the task of assessing the semantic textual similarity of clinical text data.
Methods: We used BERT as an initial baseline and analyzed the results, which we used as a starting point to develop 3 different approaches where we (1) added additional, handcrafted sentence similarity features to the classifier token of BERT and combined the results with more features in multiple regression estimators, (2) incorporated a built-in ensembling method, M-Heads, into BERT by duplicating the regression head and applying an adapted training strategy to facilitate the focus of the heads on different input patterns of the medical sentences, and (3) developed a graph-based similarity approach for medications, which allows extrapolating similarities across known entities from the training set. The approaches were evaluated with the Pearson correlation coefficient between the predicted scores and ground truth of the official training and test dataset.
Results: We improved the performance of BERT on the test dataset from a Pearson correlation coefficient of 0.859 to 0.883 using a combination of the M-Heads method and the graph-based similarity approach. We also show differences between the test and training dataset and how the two datasets influenced the results.
Conclusions: We found that using a graph-based similarity approach has the potential to extrapolate domain specific knowledge to unseen sentences. We observed that it is easily possible to obtain deceptive results from the test dataset, especially when the distribution of the data samples is different between training and test datasets.

/pdf/adapting-bidirectional-encoder-representations-from-4dh50h8gjg.pdf

Adapting Bidirectional Encoder Representations from Transformers (BERT) to Assess Clinical Semantic Textual Similarity: Algorithm Development and Validation Study.

Question-answering platforms serve millions of users seeking knowledge and solutions for their daily life problems. However, many knowledge seekers are facing the challenge to find the right answer among similar answered questions and writer’s responding to asked questions feel like they need to repeat answers many times for similar questions. This research aims at tackling the problem of learning the semantic text similarity among different asked questions by using deep learning. Three models are implemented to address the aforementioned problem: i) a supervised-machine learning model using XGBoost trained with pre-defined features, ii) an adapted Siamese-based deep learning recurrent architecture trained with pre-defined features, and iii) a Pre-trained deep bidirectional transformer based on BERT model. Proposed models were evaluated using a reference Arabic dataset from the mawdoo3.com company. Evaluation results show that the BERT-based model outperforms the other two models with an F1=92. 99%, whereas the Siamese-based model comes in the second place with F1=89.048%, and finally, the XGBoost as a baseline model achieved the lowest result of F1=86.086%.

/pdf/using-deep-learning-models-for-learning-semantic-text-4cjrckzaje.pdf

Using deep learning models for learning semantic text similarity of Arabic questions

Two questions asking the same thing can have di erent set of vocabulary set and syntactic structure. Which makes detecting the semantics equivalence between the sentences challenging. In online user forums like Quora, Stack Over ow, Stack Exchange, etc. its important to maintain high quality knowledge base by ensuring each unique question exists only once. Writers shouldn't have to write the same answer to each of the similar question and the reader must get a single page of the question they are looking for. For example, consider questions like What are the best ways to lose weight?, How can a person reduce weight?, and What are elective weight loss plans? to be duplicate questions because they all have the same intent.

https://iopscience.iop.org/article/10.1088/1742-6596/1000/1/012070/pdf

Survey on Sentence Similarity Evaluation using Deep Learning

This paper provide a brief survey of semantic similarity including semantic similarity between concepts and semantic textual similarity. We classify methods of semantic similarity between into four categories based on background information resource used and classify methods of semantic textual similarity into four categories too. As a basic methodology of text related research and applications, semantic similarity measure is often used in online social network analysis. This survey introduces how similarity computation methods assist in many aspects of social network analysis.

A survey of semantic similarity and its application to social network analysis

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A survey of semantic similarity and its application to social network analysis