UdL at SemEval-2017 Task 1: Semantic Textual Similarity Estimation
            of English Sentence Pairs Using Regression Model over Pairwise
            Features

Al-Natsheh, Hussein T.; Martinet, Lucie; Muhlenbach, Fabrice; Zighed, Djamel A.

doi:10.18653/v1/s17-2013

Cited by 11 publications

(8 citation statements)

References 7 publications

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“…Also in the SemEval 2017 workshop, [14] introduced the UdL model to determine the semantic relatedness of a set of sentence pairs in English. This model utilizes random forest ensemble learning with word embedding vectors to align Part of Speech (PoS) and Named Entities (NE) tagged tokens of each sentence pair-the model experiments with a character-based range of n-grams instead of words to calculate the features.…”

Section: Sentences Comparisonmentioning

confidence: 99%

“…[17], on their review specifically about short text similarity (STS) tasks, broadens this classification to a more generic overview, to which he classifies the tasks as string-based, corpus-based, knowledge-based, and hybrid-based. Our work will use a hybrid approach (corpus-based and string-based), using the work of [14] as a reference method. To evaluate the method proposed, we will use SemEval's STSBenchmark dataset.…”

Section: Tweet Similarity Approach 41 Overviewmentioning

confidence: 99%

“…For this paper, we decided to base our approach on [14] since the implementation of the presented solution is available online. However, upon attempting to replicate the code from the author's GitHub page, we discovered that some libraries were either unavailable or incompatible with current Python technologies.…”

Section: Tweet Similarity Approach 41 Overviewmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Approach for Tweet Similarity in a Context-Aware Fake News Detection Model

Bezerra,

Kozierkiewicz,

Pietranik

2024

Preprint

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In this article, we address two problems: the detection of fake news using a multimodal, content, and context-driven approach and the evaluation of short-text messages (in our case, tweets) to compare how similar or different they are. For the first problem, we developed a framework for detecting fake news using a multilayered, multimodal approach consisting of a three-tiered model: the topic layer, the social layer, and the context layer, helping to establish a methodology for detecting fake news. Within the topic layer, one of its tasks is the calculation of how similar two messages are to each other. We developed an improved version of an existing model, adapted it to our framework, and included calculating certain words and their positions as features and a novel embedding method using Cosine Similarity and POS (Parts of Speech) tags. We used the dataset from STSBenchmark and the correlation value to measure the quality of our model and performed a 50-fold evaluation over the validation data. Ultimately, our model has a better correlation value (median 0.673528) than the benchmarked model. Our main contribution is the delivery of a reliable, formal, and adaptable framework for identifying fake news; we also present a means of comparing tweets by their content, utilizing FastText models, cosine similarity, and other measures, making our contribution practical and effective.

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Section: Sentences Comparisonmentioning

confidence: 99%

Section: Tweet Similarity Approach 41 Overviewmentioning

confidence: 99%

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A Novel Approach for Tweet Similarity in a Context-Aware Fake News Detection Model

Bezerra,

Kozierkiewicz,

Pietranik

2024

Preprint

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show abstract

“…The size of each set was 200 × 182 = 36, 400 pairs. In order to estimate the semantic relatedness score of each pair for each of the 3 sets, we used a pre-trained model 3 [31] which provides an estimation score between 0.0 to 5.0. This model was trained on an open access datasets 4 .…”

Section: E Sentence Semantic Relatedness Measurementioning

confidence: 99%

Semantic Search-by-Examples for Scientific Topic Corpus Expansion in Digital Libraries

Al-Natsheh¹,

Martinet²,

Muhlenbach³

et al. 2017

2017 IEEE International Conference on Data Mining Workshops (ICDMW)

Self Cite

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Abstract-In this article we address the problem of expanding the set of papers that researchers encounter when conducting bibliographic research on their scientific work. Using classical search engines or recommender systems in digital libraries, some interesting and relevant articles could be missed if they do not contain the same search key-phrases that the researcher is aware of. We propose a novel model that is based on a supervised active learning over a semantic features transformation of all articles of a given digital library. Our model, named Semantic Search-byExamples (SSbE), shows better evaluation results over a similar purpose existing method, More-Like-This query, based on the feedback annotation of two domain experts in our experimented use-case. We also introduce a new semantic relatedness evaluation measure to avoid the need of human feedback annotation after the active learning process. The results also show higher diversity and overlapping with related scientific topics which we think can better foster transdisciplinary research.

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“…Semantic Web techniques and natural language processing (NLP) provide computers with the ability to decipher the meaning of information and process phrases written or spoken in natural language [19]. The combination of these techniques can allow users to search for data using natural language sentences [20,21] in multiple languages, and they can also be used to find semantic similarities among disparate documents from different disciplines [22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

A Virtual Community for Disability Advocacy: Development of a Searchable Artificial Intelligence–Supported Platform

Morr¹,

Maret²,

Muhlenbach³

et al. 2021

JMIR Form Res

Self Cite

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Background The lack of availability of disability data has been identified as a major challenge hindering continuous disability equity monitoring. It is important to develop a platform that enables searching for disability data to expose systemic discrimination and social exclusion, which increase vulnerability to inequitable social conditions. Objective Our project aims to create an accessible and multilingual pilot disability website that structures and integrates data about people with disabilities and provides data for national and international disability advocacy communities. The platform will be endowed with a document upload function with hybrid (automated and manual) paragraph tagging, while the querying function will involve an intelligent natural language search in the supported languages. Methods We have designed and implemented a virtual community platform using Wikibase, Semantic Web, machine learning, and web programming tools to enable disability communities to upload and search for disability documents. The platform data model is based on an ontology we have designed following the United Nations Convention on the Rights of Persons with Disabilities (CRPD). The virtual community facilitates the uploading and sharing of validated information, and supports disability rights advocacy by enabling dissemination of knowledge. Results Using health informatics and artificial intelligence techniques (namely Semantic Web, machine learning, and natural language processing techniques), we were able to develop a pilot virtual community that supports disability rights advocacy by facilitating uploading, sharing, and accessing disability data. The system consists of a website on top of a Wikibase (a Semantic Web–based datastore). The virtual community accepts 4 types of users: information producers, information consumers, validators, and administrators. The virtual community enables the uploading of documents, semiautomatic tagging of their paragraphs with meaningful keywords, and validation of the process before uploading the data to the disability Wikibase. Once uploaded, public users (information consumers) can perform a semantic search using an intelligent and multilingual search engine (QAnswer). Further enhancements of the platform are planned. Conclusions The platform ontology is flexible and can accommodate advocacy reports and disability policy and legislation from specific jurisdictions, which can be accessed in relation to the CRPD articles. The platform ontology can be expanded to fit international contexts. The virtual community supports information upload and search. Semiautomatic tagging and intelligent multilingual semantic search using natural language are enabled using artificial intelligence techniques, namely Semantic Web, machine learning, and natural language processing.

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UdL at SemEval-2017 Task 1: Semantic Textual Similarity Estimation of English Sentence Pairs Using Regression Model over Pairwise Features

Cited by 11 publications

References 7 publications

A Novel Approach for Tweet Similarity in a Context-Aware Fake News Detection Model

A Novel Approach for Tweet Similarity in a Context-Aware Fake News Detection Model

Semantic Search-by-Examples for Scientific Topic Corpus Expansion in Digital Libraries

A Virtual Community for Disability Advocacy: Development of a Searchable Artificial Intelligence–Supported Platform

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