A word may have more than one meaning or sense (polysemous). For instance, the word “light” may have a meaning of “a source of lighting or illumination” as in “Turn off the light”. It can also have a meaning of “not heavy” as in “My new phone is small and light”. The meaning can usually be differentiated or disambiguated by observing the context where the word is used. In a written text or sentence, the context can be described in the simplest case as the information of the surrounding words. In a more complex case, as in the use of word bank in “I’ll meet you at the bank”, the disambiguation requires more than just the information from the surrounding words. Since, the bank may have a meaning of “a financial institution (a building)” but can also have a meaning of “river bank”. Both meanings are possible and the correct meaning will depend on the shared knowledge between the speakers. Word sense disambiguation is the task to decide, given a text or sentence, the correct meaning of a certain word inside the text. In this project, we experimented in disambiguating the word senses of three different words: hard, line, and serve. We use the data developed by (Leacock). The data contains sentences with the target word (word to be disambiguated) tagged and disambiguated using the senses from WordNet (Fellbaum). There will be one word that we need to disambiguate in each sentence and hence, the meaning of the word will be disambiguated using this one sentence context. We will use three different machine learning methods to do the classification of the word into their respective sense. The performance of each method will be measured by its accuracy, i.e. the number of sentences with correct prediction divided by the total number of sentences predicted. We will describe the data and the features we use in Section 2. Section 3 will provide theoretical background on the selected machine learning methods. In Section 4, we will describe our experiments and our analysis of the results. The conclusions of the work will be given in Section 5.

Word Sense Disambiguation.

Three-dimensional fatigue crack propagation simulation using extended finite element methods for steel grades S355 and S690 considering mean stress effects

A novel Strain-Energy-Density based fatigue criterion accounting for mean stress and plasticity effects on the medium-to-high-cycle uniaxial fatigue strength of plain and notched components

http://jultika.oulu.fi/files/nbnfi-fe2019081324014.pdf

Secure communication channel architecture for Software Defined Mobile Networks

Ontology can describe real-world objects and relationships. It can be used for searching, web mining, and semantic analysis. However, creating the ontology for a different domain is a labor-intensive and computationally expensive task. This work introduces a new model to automate ontology creation for the real estate domain based on a natural language processing approach. The new model can save significant time and human labor in ontology creation in several domains. Also, the new model can enhance retrieval information systems in advanced semantic web search engines.

Ontology Creation Model based on Attention Mechanism for a Specific Business Domain

Investigation of fracture behaviour and low cycle fatigue properties of cryorolled Al-Mg alloy

Extended Finite Element Simulation on Tensile, Fracture Toughness and Fatigue Crack Growth Behaviour of Additively Manufactured Ti6Al4V Alloy

In this work, mechanical properties and fracture toughness of as-received and solution-treated aluminum alloy 5754 (AA 5754) are experimentally evaluated. Solution heat treatment of the alloy is performed at 530 °C for 2 h, and then, quenching is done in water. Yield strength, ultimate tensile strength, impact toughness, hardness, fatigue life, brittle fracture toughness $$(K_{\text{Ic}} )$$
 and ductile fracture toughness $$(J_{\text{Ic}} )$$
 are evaluated for as-received and solution-treated alloy. Extended finite element method has been used for the simulation of tensile and fracture behavior of material. Heaviside function and asymptotic crack tip enrichment functions are used for modelling of the crack in the geometry. Ramberg-Osgood material model coupled with fracture energy is used to simulate the crack propagation. Fracture surfaces obtained from various mechanical tests are characterized by scanning electron microscopy.

Investigation of Mechanical Properties and Fracture Simulation of Solution-Treated AA 5754

Fatigue and fracture parameters such as sigmoidal curve, fatigue crack growth threshold, stress intensity factor (SIF), and Paris constants are evaluated for thermo-mechanically processed Al-Mg (AA...

Pankaj Kumar

Papers

Investigation of fracture behaviour and low cycle fatigue properties of cryorolled Al-Mg alloy

Extended Finite Element Simulation on Tensile, Fracture Toughness and Fatigue Crack Growth Behaviour of Additively Manufactured Ti6Al4V Alloy

Investigation of Mechanical Properties and Fracture Simulation of Solution-Treated AA 5754

Fatigue crack growth behavior of thermo-mechanically processed AA 5754: Experiment and extended finite element method simulation

Investigation of fatigue and fracture behaviour of sensitized marine grade aluminium alloy AA 5754