Semantic Service Retrieval Based on Natural Language Querying and Semantic Similarity

2011 ◽  
Author(s):  
Richard Eckart de Castilho ◽  
Iryna Gurevych
Keyword(s):  
Natural Language ◽  
Semantic Similarity ◽  
Semantic Service

scholarly journals A Natural Language Processing Approach to Measuring Treatment Adherence and Consistency Using Semantic Similarity

AERA Open ◽  
2021 ◽  
Vol 7 ◽  
pp. 233285842110286
Author(s):  
Kylie L. Anglin ◽  
Vivian C. Wong ◽  
Arielle Boguslav
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Semantic Similarity ◽  
Language Processing ◽  
Intervention Implementation ◽  
Proof Of Concept ◽  
Coaching Intervention ◽  
Processing Techniques ◽  
Teacher Coaching ◽  
The Impact

Though there is widespread recognition of the importance of implementation research, evaluators often face intense logistical, budgetary, and methodological challenges in their efforts to assess intervention implementation in the field. This article proposes a set of natural language processing techniques called semantic similarity as an innovative and scalable method of measuring implementation constructs. Semantic similarity methods are an automated approach to quantifying the similarity between texts. By applying semantic similarity to transcripts of intervention sessions, researchers can use the method to determine whether an intervention was delivered with adherence to a structured protocol, and the extent to which an intervention was replicated with consistency across sessions, sites, and studies. This article provides an overview of semantic similarity methods, describes their application within the context of educational evaluations, and provides a proof of concept using an experimental study of the impact of a standardized teacher coaching intervention.

scholarly journals Evolution of Semantic Similarity—A Survey

2021 ◽  
Vol 54 (2) ◽  
pp. 1-37
Author(s):  
Dhivya Chandrasekaran ◽  
Vijay Mago
Keyword(s):  
Natural Language ◽  
Semantic Similarity ◽  
Language Processing ◽  
Hybrid Methods ◽  
Research Work ◽  
Similarity Measures ◽  
Text Data ◽  
Knowledge Based ◽  
Open Research ◽  
Research Problems

Estimating the semantic similarity between text data is one of the challenging and open research problems in the field of Natural Language Processing (NLP). The versatility of natural language makes it difficult to define rule-based methods for determining semantic similarity measures. To address this issue, various semantic similarity methods have been proposed over the years. This survey article traces the evolution of such methods beginning from traditional NLP techniques such as kernel-based methods to the most recent research work on transformer-based models, categorizing them based on their underlying principles as knowledge-based, corpus-based, deep neural network–based methods, and hybrid methods. Discussing the strengths and weaknesses of each method, this survey provides a comprehensive view of existing systems in place for new researchers to experiment and develop innovative ideas to address the issue of semantic similarity.

LIS4: Lesk Inspired Sense Specific Semantic Similarity using WordNet

2021 ◽  
pp. 2150006
Author(s):  
Saravanakumar Kandasamy ◽  
Aswani Kumar Cherukuri
Keyword(s):  
Information Retrieval ◽  
Natural Language Processing ◽  
Natural Language ◽  
Semantic Similarity ◽  
Language Processing ◽  
Gold Standard ◽  
Question Answering ◽  
Knowledge Based ◽  
Benchmark Datasets ◽  
Processing Information

Semantic similarity quantification between concepts is one of the inevitable parts in domains like Natural Language Processing, Information Retrieval, Question Answering, etc. to understand the text and their relationships better. Last few decades, many measures have been proposed by incorporating various corpus-based and knowledge-based resources. WordNet and Wikipedia are two of the Knowledge-based resources. The contribution of WordNet in the above said domain is enormous due to its richness in defining a word and all of its relationship with others. In this paper, we proposed an approach to quantify the similarity between concepts that exploits the synsets and the gloss definitions of different concepts using WordNet. Our method considers the gloss definitions, contextual words that are helping in defining a word, synsets of contextual word and the confidence of occurrence of a word in other word’s definition for calculating the similarity. The evaluation based on different gold standard benchmark datasets shows the efficiency of our system in comparison with other existing taxonomical and definitional measures.

Mining information from sentences through Semantic Web data and Information Extraction tasks

2020 ◽  
pp. 016555152093438
Author(s):  
Jose L. Martinez-Rodriguez ◽  
Ivan Lopez-Arevalo ◽  
Ana B. Rios-Alvarado
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
Information Extraction ◽  
Knowledge Base ◽  
Semantic Similarity ◽  
Similarity Measure ◽  
Real World ◽  
Semantic Similarity Measure ◽  
Web Standards ◽  
Extract Information

The Semantic Web provides guidelines for the representation of information about real-world objects (entities) and their relations (properties). This is helpful for the dissemination and consumption of information by people and applications. However, the information is mainly contained within natural language sentences, which do not have a structure or linguistic descriptions ready to be directly processed by computers. Thus, the challenge is to identify and extract the elements of information that can be represented. Hence, this article presents a strategy to extract information from sentences and its representation with Semantic Web standards. Our strategy involves Information Extraction tasks and a hybrid semantic similarity measure to get entities and relations that are later associated with individuals and properties from a Knowledge Base to create RDF triples (Subject–Predicate–Object structures). The experiments demonstrate the feasibility of our method and that it outperforms the accuracy provided by a pattern-based method from the literature.

Discourse cohesion in text and tutorial dialogue

2007 ◽  
Vol 15 (3) ◽  
pp. 199-213 ◽  
Author(s):  
Arthur C. Graesser ◽  
Moongee Jeon ◽  
Yan Yan ◽  
Zhiqiang Cai
Keyword(s):  
College Students ◽  
Natural Language ◽  
Semantic Similarity ◽  
Software Tool ◽  
Pedagogical Agent ◽  
Newtonian Physics ◽  
Tutorial Dialogue ◽  
Logical Operators ◽  
Different Types ◽  
Human Tutoring

Discourse cohesion is presumably an important facilitator of comprehension when individuals read texts and hold conversations. This study investigated components of cohesion and language in different types of discourse about Newtonian physics: A textbook, textoids written by experimental psychologists, naturalistic tutorial dialoguebetween expert human tutors and college students, andAutoTutor tutorial dialogue between a computer tutor and students (AutoTutor is an animated pedagogical agent that helps students learn about physics by holding conversations in natural language). We analyzed the four types of discourse with Coh-Metrix, a software tool that measures discourse on different components of cohesion, language, and readability. The cohesion indices included co-reference, syntactic and semantic similarity, causal cohesion, incidence of cohesion signals (e.g., connectives, logical operators), and many other measures. Cohesion data were quite similar for the two forms of discourse in expository monologue (textbooks and textoids) and for the two types of tutorial dialogue (i.e., students interacting with human tutors and AutoTutor), but very different between the discourse of expository monologue and tutorial dialogue. Coh-Metrix was also able to detect subtle differences in the language and discourse of AutoTutor versus human tutoring.

Port-Based Ontology Semantic Similarities for Module Concept Creation

2009 ◽  
Author(s):  
Dongxing Cao ◽  
Karthik Ramani ◽  
Ming Wang Fu ◽  
Runli Zhang
Keyword(s):  
Natural Language ◽  
Semantic Similarity ◽  
Similarity Measures ◽  
Concept Generation ◽  
Syntactic Structures ◽  
Ontology Language ◽  
Modular Concept ◽  
Semantic Link ◽  
Functional Blocks ◽  
Physical Components

The modularity indicates a one-to-one mapping between functional concepts and physical components. It can allow us to generate more product varieties at lower costs. Functional concepts can be described by precise syntactic structures with functional terms. Different semantic measures can be used to evaluate the strength of the semantic link between two functional concepts from port ontology. In this paper, different methods of modularity based on ontology are first investigated. Secondly, the primitive concepts are presented based on port ontology by using natural language, and then their semantic synthesis is used to describe component ontology. The taxonomy of port-based ontology are built to map the component connections and interactions in order to build functional blocks. Next, propose an approach to computing semantic similarity by mapping terms to functional ontology and by examining their relationships based on port ontology language. Furthermore, several modules are partitioned on the basis of similarity measures. The process of module construction is described and its elements are related to the similarity values between concepts. Finally, a case is studied to show the efficiency of port ontology semantic similarity for modular concept generation.

A non-negative tensor factorization model for selectional preference induction

2010 ◽  
Vol 16 (4) ◽  
pp. 417-437 ◽  
Author(s):  
TIM VAN DE CRUYS
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Semantic Similarity ◽  
Language Processing ◽  
Tensor Factorization ◽  
Promising Tool ◽  
Occurrence Data ◽  
Factorization Model ◽  
Distributional Similarity ◽  
Factorization Methods

AbstractThe distributional similarity methods have proven to be a valuable tool for the induction of semantic similarity. Until now, most algorithms use two-way co-occurrence data to compute the meaning of words. Co-occurrence frequencies, however, need not be pairwise. One can easily imagine situations where it is desirable to investigate co-occurrence frequencies of three modes and beyond. This paper will investigate tensor factorization methods to build a model of three-way co-occurrences. The approach is applied to the problem of selectional preference induction, and automatically evaluated in a pseudo-disambiguation task. The results show that tensor factorization, and non-negative tensor factorization in particular, is a promising tool for Natural Language Processing (nlp).

scholarly journals DIBERT: Dependency Injected Bidirectional Encoder Representations from Transformers

2021 ◽  
Author(s):  
Abdul Wahab ◽  
Rafet Sifa
Keyword(s):  
Natural Language ◽  
Sentiment Analysis ◽  
Semantic Similarity ◽  
Syntactic Structure ◽  
Language Modeling ◽  
Benchmark Dataset ◽  
Fine Tuning ◽  
New Model ◽  
Dependency Tree ◽  
Better Than

<div> <div> <div> <p> </p><div> <div> <div> <p>In this paper, we propose a new model named DIBERT which stands for Dependency Injected Bidirectional Encoder Representations from Transformers. DIBERT is a variation of the BERT and has an additional third objective called Parent Prediction (PP) apart from Masked Language Modeling (MLM) and Next Sentence Prediction (NSP). PP injects the syntactic structure of a dependency tree while pre-training the DIBERT which generates syntax-aware generic representations. We use the WikiText-103 benchmark dataset to pre-train both BERT- Base and DIBERT. After fine-tuning, we observe that DIBERT performs better than BERT-Base on various downstream tasks including Semantic Similarity, Natural Language Inference and Sentiment Analysis. </p> </div> </div> </div> </div> </div> </div>

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