OTTHO (On the Tip of my THOught) is an information seeking system designed for solving a language game which demands knowledge covering a broad range of topics, such as movies, politics, literature, history, proverbs, and popular culture. OTTHO implements a knowledge infusion process in order to provide a background knowledge which allows a deeper understanding of the items it deals with. The knowledge infusion process consists of two steps: 1) extracting and modeling relationships between words extracted from several knowledge sources; 2) reasoning on the induced models in order to generate new knowledge. OTTHO extracts knowledge from several sources, such as a dictionary, news, Wikipedia, and various unstructured repositories and creates a memory of linguistic knowledge and world facts. Starting from some external stimuli (e.g. words) depending on the task to be accomplished, the reasoning mechanism allows retrieving some specific pieces of knowledge from the memory created in the previous step. OTTHO has a great potential for more practical applications besides solving a language game. It could be used for implementing an alternative paradigm for associative information retrieval, for computational advertising and recommender systems.
Encoding syntactic dependencies by vector permutationPierpaolo Basile
Distributional approaches are based on a simple hypothesis: the meaning of a word can be inferred from its usage. The application of that idea to the vector space model makes possible the construction of a WordSpace in which words are represented by mathematical points in a geometric space. Similar words are represented close in this space and the definition of ``word usage'' depends on the definition of the context used to build the space, which can be the whole document, the sentence in which the word occurs, a fixed window of words, or a specific syntactic context. However, in its original formulation WordSpace can take into account only one definition of context at a time. We propose an approach based on vector permutation and Random Indexing to encode several syntactic contexts in a single WordSpace. Moreover, we propose some operations in this space and report the results of an evaluation performed using the GEMS 2011 Shared Evaluation data.
Microstructure prediction in cutting of TitaniumHongtao Ding
The document summarizes research on using a dislocation density-based material model and finite element modeling to predict nanocrystalline microstructure changes during machining of commercially pure titanium. The model captures grain size evolution by simulating the generation, interaction, and annihilation of dislocations. Simulation results for strain, temperature, and grain size during orthogonal cutting matched well with experimental measurements and showed that cutting parameters like rake angle can be optimized to achieve the desired microstructure.
Word Sense Disambiguation and Intelligent Information AccessPierpaolo Basile
The document outlines Pierpaolo Basile's work on word sense disambiguation and intelligent information access. It introduces key concepts like word sense disambiguation, outlines Basile's JIGSAW algorithm for WSD that uses WordNet senses and different strategies for part of speech tags, and discusses applications of WSD in areas like information retrieval, question answering and knowledge acquisition to enhance intelligent information access.
Nanocrytalline Microstructure during Metal CuttingHongtao Ding
This document summarizes research on using a dislocation density-based material model to predict nanocrystalline microstructure changes during metal cutting. A coupled Eulerian-Lagrangian model simulates chip formation and predicts grain refinement during orthogonal cutting, matching experimental measurements. The model also simulates multi-pass cold rolling, accurately predicting increasing grain misorientation with strain accumulation. This numerical framework provides a useful tool for predicting grain refinement under various cutting conditions and cold working processes.
Topic Modeling for Information Retrieval and Word Sense Disambiguation tasksLeonardo Di Donato
Experimental work done regarding the use of Topic Modeling for the implementation and the improvement of some common tasks of Information Retrieval and Word Sense Disambiguation.
First of all it describes the scenario, the pre-processing pipeline realized and the framework used. After we we face a discussion related to the investigation of some different hyperparameters configurations for the LDA algorithm.
This work continues dealing with the retrieval of relevant documents mainly through two different approaches: inferring the topics distribution of the held out document (or query) and comparing it to retrieve similar collection’s documents or through an approach driven by probabilistic querying. The last part of this work is devoted to the investigation of the word sense disambiguation task.
OTTHO (On the Tip of my THOught) is an information seeking system designed for solving a language game which demands knowledge covering a broad range of topics, such as movies, politics, literature, history, proverbs, and popular culture. OTTHO implements a knowledge infusion process in order to provide a background knowledge which allows a deeper understanding of the items it deals with. The knowledge infusion process consists of two steps: 1) extracting and modeling relationships between words extracted from several knowledge sources; 2) reasoning on the induced models in order to generate new knowledge. OTTHO extracts knowledge from several sources, such as a dictionary, news, Wikipedia, and various unstructured repositories and creates a memory of linguistic knowledge and world facts. Starting from some external stimuli (e.g. words) depending on the task to be accomplished, the reasoning mechanism allows retrieving some specific pieces of knowledge from the memory created in the previous step. OTTHO has a great potential for more practical applications besides solving a language game. It could be used for implementing an alternative paradigm for associative information retrieval, for computational advertising and recommender systems.
Encoding syntactic dependencies by vector permutationPierpaolo Basile
Distributional approaches are based on a simple hypothesis: the meaning of a word can be inferred from its usage. The application of that idea to the vector space model makes possible the construction of a WordSpace in which words are represented by mathematical points in a geometric space. Similar words are represented close in this space and the definition of ``word usage'' depends on the definition of the context used to build the space, which can be the whole document, the sentence in which the word occurs, a fixed window of words, or a specific syntactic context. However, in its original formulation WordSpace can take into account only one definition of context at a time. We propose an approach based on vector permutation and Random Indexing to encode several syntactic contexts in a single WordSpace. Moreover, we propose some operations in this space and report the results of an evaluation performed using the GEMS 2011 Shared Evaluation data.
Microstructure prediction in cutting of TitaniumHongtao Ding
The document summarizes research on using a dislocation density-based material model and finite element modeling to predict nanocrystalline microstructure changes during machining of commercially pure titanium. The model captures grain size evolution by simulating the generation, interaction, and annihilation of dislocations. Simulation results for strain, temperature, and grain size during orthogonal cutting matched well with experimental measurements and showed that cutting parameters like rake angle can be optimized to achieve the desired microstructure.
Word Sense Disambiguation and Intelligent Information AccessPierpaolo Basile
The document outlines Pierpaolo Basile's work on word sense disambiguation and intelligent information access. It introduces key concepts like word sense disambiguation, outlines Basile's JIGSAW algorithm for WSD that uses WordNet senses and different strategies for part of speech tags, and discusses applications of WSD in areas like information retrieval, question answering and knowledge acquisition to enhance intelligent information access.
Nanocrytalline Microstructure during Metal CuttingHongtao Ding
This document summarizes research on using a dislocation density-based material model to predict nanocrystalline microstructure changes during metal cutting. A coupled Eulerian-Lagrangian model simulates chip formation and predicts grain refinement during orthogonal cutting, matching experimental measurements. The model also simulates multi-pass cold rolling, accurately predicting increasing grain misorientation with strain accumulation. This numerical framework provides a useful tool for predicting grain refinement under various cutting conditions and cold working processes.
Topic Modeling for Information Retrieval and Word Sense Disambiguation tasksLeonardo Di Donato
Experimental work done regarding the use of Topic Modeling for the implementation and the improvement of some common tasks of Information Retrieval and Word Sense Disambiguation.
First of all it describes the scenario, the pre-processing pipeline realized and the framework used. After we we face a discussion related to the investigation of some different hyperparameters configurations for the LDA algorithm.
This work continues dealing with the retrieval of relevant documents mainly through two different approaches: inferring the topics distribution of the held out document (or query) and comparing it to retrieve similar collection’s documents or through an approach driven by probabilistic querying. The last part of this work is devoted to the investigation of the word sense disambiguation task.