Exploring Linked Data content through network analysisChristophe Guéret
This document discusses analyzing linked open data through network analysis. It outlines exploring what linked data content is available to analyze, what can be analyzed, and if any aspects are being overlooked. New research directions are being investigated with initial results, including explaining patterns in the data and predicting the impact of changes. The analysis suggests moving beyond just analyzing resource triples to also studying the entities related to publishing and consuming the data.
The document proposes a framework called Term Ambiguity Detection (TAD) to determine whether terms are ambiguous or unambiguous at the term level rather than at the instance level. The TAD framework uses a three step process: (1) an n-gram method to check for common words/phrases, (2) an ontology method using Wiktionary and Wikipedia to check for multiple senses or disambiguation pages, and (3) a clustering method using LDA to check if category terms appear in document clusters. Evaluation on movie, video game, camera and book terms showed the combined framework achieved a high F-measure of 0.96 for ambiguity detection, allowing information extraction systems to achieve high precision by incorporating ambiguity information.
Roy Tennant, Senior Program Officer, OCLC Research
As library collections shift from print materials to digital formats, and as the web enables ubiquitous and instantaneous discovery of information, library users expect to find and access materials online. It’s not enough to have pages “on the web”; library data must be “woven into the web” and integrated into the sites and services that library users frequent daily – Google, Wikipedia, social networks. When information about a library’s collection is locked up behind a specific web site (such as an OPAC), it is often exceedingly difficult for services, such as search engines, to consume that data. Information seekers need to be connected back to their local library resources from wherever they are on the web. The imperative is to make library data available in new data formats that are native to the web, exposing it to the wider web community, making it easily discoverable by other sites, services, and ultimately consumers. Roy Tennant will shed light on what linked data is and how to re-envision, expose and share library data as entities that are part of the web.
A Comparison of NER Tools w.r.t. a Domain-Specific VocabularyTimm Heuss
Presentation hold at the SEMANTiCS 2014, in regard of this paper: http://doi.acm.org/10.1145/2660517.2660520
In this paper we compare several state-of-the-art Linked Data Knowledge Extraction tools, with regard to their ability to recognise entities of a controlled, domain-specific vocabulary. This includes tools that offer APIs as a Service, locally installed platforms as well as an UIMA-based approach as reference. We evaluate under realistic conditions, with natural language source texts from keywording experts of the Städel Museum Frankfurt. The goal is to find first hints which tool approach or strategy is more convincing in case of a domain specific tagging/annotation, towards a working solution that is demanded by GLAMs world-wide.
It is quite often observed that when people use retrieval systems, they do not just search documents or text passages in the first place, but for some information contained inside, which is related to some entities, for instance, person, organization, location, events, time, etc. The goal is to find out various kinds of valuable semantic information about real-world entites embedded in different web pages and databases. But It is a difficult task for us to find out specific or exact information about entities from present search engines. So we need search engines, which will identify our queries across different domains and extract structured information about entities.
On the Reproducibility of the TAGME entity linking systemFaegheh Hasibi
Slide for the ECIR '16 paper: “On the reproducibility of the TAGME Entity Linking System”
Reproducibility is a fundamental requirement of scientific research. In this paper, we examine the repeatability, reproducibility, and generalizability of TAGME, one of the most popular entity linking systems. By comparing results obtained from its public API with (re)implementations from scratch, we obtain the following findings. The results reported in the TAGME paper cannot be repeated due to the unavailability of data sources. Part of the results are reproducible through the provided API, while the rest are not reproducible. We further show that the TAGME approach is generalizable to the task of entity linking in queries. Finally, we provide insights gained during this process and formulate lessons learned to inform future reducibility efforts.
SYNERGY - A Named Entity Recognition System for Resource-scarce Languages suc...Guy De Pauw
The document describes SYNERGY, a named entity recognition system for resource-scarce languages like Swahili that utilizes online machine translation. It does this by translating the text to English, running existing NER tools on the English text, mapping entities back to the original language via alignment, and improving results with post-processing. The system achieves near state-of-the-art performance for Arabic NER and good performance for Swahili NER using freely available tools and no language-specific training data. Future work could explore coreference resolution across translations.
This document discusses two natural language processing techniques: universal topic classification and named entity disambiguation.
For universal topic classification, it proposes using Apache Lucene/Solr's MoreLikeThis query to find related Wikipedia articles based on document terms, and then categorizing the document using the topics of related articles. It also discusses using Wikipedia categories to provide a hierarchical structure.
For named entity disambiguation, it suggests using MoreLikeThis with surrounding context to disambiguate entities mentioned in a document (e.g. determining if "George Bush" refers to George H. W. Bush or George W. Bush). The document outlines work in progress to integrate these techniques into the Stanbol semantic framework.
Exploring Linked Data content through network analysisChristophe Guéret
This document discusses analyzing linked open data through network analysis. It outlines exploring what linked data content is available to analyze, what can be analyzed, and if any aspects are being overlooked. New research directions are being investigated with initial results, including explaining patterns in the data and predicting the impact of changes. The analysis suggests moving beyond just analyzing resource triples to also studying the entities related to publishing and consuming the data.
The document proposes a framework called Term Ambiguity Detection (TAD) to determine whether terms are ambiguous or unambiguous at the term level rather than at the instance level. The TAD framework uses a three step process: (1) an n-gram method to check for common words/phrases, (2) an ontology method using Wiktionary and Wikipedia to check for multiple senses or disambiguation pages, and (3) a clustering method using LDA to check if category terms appear in document clusters. Evaluation on movie, video game, camera and book terms showed the combined framework achieved a high F-measure of 0.96 for ambiguity detection, allowing information extraction systems to achieve high precision by incorporating ambiguity information.
Roy Tennant, Senior Program Officer, OCLC Research
As library collections shift from print materials to digital formats, and as the web enables ubiquitous and instantaneous discovery of information, library users expect to find and access materials online. It’s not enough to have pages “on the web”; library data must be “woven into the web” and integrated into the sites and services that library users frequent daily – Google, Wikipedia, social networks. When information about a library’s collection is locked up behind a specific web site (such as an OPAC), it is often exceedingly difficult for services, such as search engines, to consume that data. Information seekers need to be connected back to their local library resources from wherever they are on the web. The imperative is to make library data available in new data formats that are native to the web, exposing it to the wider web community, making it easily discoverable by other sites, services, and ultimately consumers. Roy Tennant will shed light on what linked data is and how to re-envision, expose and share library data as entities that are part of the web.
A Comparison of NER Tools w.r.t. a Domain-Specific VocabularyTimm Heuss
Presentation hold at the SEMANTiCS 2014, in regard of this paper: http://doi.acm.org/10.1145/2660517.2660520
In this paper we compare several state-of-the-art Linked Data Knowledge Extraction tools, with regard to their ability to recognise entities of a controlled, domain-specific vocabulary. This includes tools that offer APIs as a Service, locally installed platforms as well as an UIMA-based approach as reference. We evaluate under realistic conditions, with natural language source texts from keywording experts of the Städel Museum Frankfurt. The goal is to find first hints which tool approach or strategy is more convincing in case of a domain specific tagging/annotation, towards a working solution that is demanded by GLAMs world-wide.
It is quite often observed that when people use retrieval systems, they do not just search documents or text passages in the first place, but for some information contained inside, which is related to some entities, for instance, person, organization, location, events, time, etc. The goal is to find out various kinds of valuable semantic information about real-world entites embedded in different web pages and databases. But It is a difficult task for us to find out specific or exact information about entities from present search engines. So we need search engines, which will identify our queries across different domains and extract structured information about entities.
On the Reproducibility of the TAGME entity linking systemFaegheh Hasibi
Slide for the ECIR '16 paper: “On the reproducibility of the TAGME Entity Linking System”
Reproducibility is a fundamental requirement of scientific research. In this paper, we examine the repeatability, reproducibility, and generalizability of TAGME, one of the most popular entity linking systems. By comparing results obtained from its public API with (re)implementations from scratch, we obtain the following findings. The results reported in the TAGME paper cannot be repeated due to the unavailability of data sources. Part of the results are reproducible through the provided API, while the rest are not reproducible. We further show that the TAGME approach is generalizable to the task of entity linking in queries. Finally, we provide insights gained during this process and formulate lessons learned to inform future reducibility efforts.
SYNERGY - A Named Entity Recognition System for Resource-scarce Languages suc...Guy De Pauw
The document describes SYNERGY, a named entity recognition system for resource-scarce languages like Swahili that utilizes online machine translation. It does this by translating the text to English, running existing NER tools on the English text, mapping entities back to the original language via alignment, and improving results with post-processing. The system achieves near state-of-the-art performance for Arabic NER and good performance for Swahili NER using freely available tools and no language-specific training data. Future work could explore coreference resolution across translations.
This document discusses two natural language processing techniques: universal topic classification and named entity disambiguation.
For universal topic classification, it proposes using Apache Lucene/Solr's MoreLikeThis query to find related Wikipedia articles based on document terms, and then categorizing the document using the topics of related articles. It also discusses using Wikipedia categories to provide a hierarchical structure.
For named entity disambiguation, it suggests using MoreLikeThis with surrounding context to disambiguate entities mentioned in a document (e.g. determining if "George Bush" refers to George H. W. Bush or George W. Bush). The document outlines work in progress to integrate these techniques into the Stanbol semantic framework.
This document proposes a catalog of 20 patterns for multilingual linked open data (MLOD). The patterns are classified into activities like naming, dereferencing, labeling, and linking. Each pattern contains a name, description, context, example, and discussion. The goal is to provide generic solutions to common problems in MLOD based on experiences with DBPedia internationalization. Future work may involve feedback, additional patterns, and handling large datasets.
Stephen M. Shellman is the president, CEO, and chief scientist of Strategic Analysis Enterprises, Inc. and is an expert in event forecasting, sentiment analysis, and qualitative and quantitative analysis. He uses natural language processing and entity extraction methodologies to generate and analyze valuable organizational data about events and group behavior. Entity extraction involves detecting and classifying elements like names, locations, times, and numbers within text. It can identify both temporal and numerical expressions. Common entity extraction techniques include pattern-based, dictionary-based, hybrid pattern, hierarchical context, and formatting cue methods.
This document discusses named entity recognition for Arabic text. It describes the different types of named entities like people, locations, and organizations. It outlines the challenges of NER in Arabic including the lack of capitalization and ambiguities. The document then presents the methodology, system architecture, and implementation of an NERA system that uses gazetteers, grammar rules, and filtering to identify named entities in Arabic texts. Evaluation results and conclusions are also presented.
This document discusses query entity recognition (QER), which seeks to locate and classify elements in text queries into predefined categories like names, organizations, locations, etc. It describes challenges like differentiating similar entities and balancing free text for training. The document outlines approaches to QER like string matching, probabilistic shallow parsing using conditional random fields, and a hybrid method. It provides details on the features of the QER system, such as processing speed, integration formats, and evaluation metrics. Future directions are mentioned, like expanding QER into a complete query dynamics system.
Text mining techniques can be used to extract information and insights from the exponential growth of scientific literature. Key techniques include information retrieval to find relevant papers, named entity recognition to identify concepts, and information extraction to formalize facts. These techniques can be evaluated using benchmarking against manually annotated corpora, though creating such resources requires significant effort and the pragmatic approach of inspecting text mining outputs is much less work.
Named Entity Recognition - ACL 2011 PresentationRichard Littauer
This document summarizes an analysis of the performance of three named-entity recognition systems: Stanford, LBJ, and IdentifiFinder. The analysis found differences in how each system tokenized text and the total number of entities recognized. It also found ambiguity cases where the same token was assigned different entity types within a single document. To address evaluation issues, the document proposes a standardized unit test with examples of true positive entity types and guidelines for intrinsic evaluation of named-entity recognition.
RDF and other linked data standards — how to make use of big localization dataDave Lewis
The standards and interoperability challenge to using the Resource Description Framework for data resource in linked data. Based on work from CNGL (www.cngl.ie), the FALCON project (www.falcon-project.eu) and the LIDER project (www.lider-project.eu)
This presentation addresses the main issues of Linked Data and scalability. In particular, it provides gives details on approaches and technologies for clustering, distributing, sharing, and caching data. Furthermore, it addresses the means for publishing data trough could deployment and the relationship between Big Data and Linked Data, exploring how some of the solutions can be transferred in the context of Linked Data.
This document discusses interaction with linked data, focusing on visualization techniques. It begins with an overview of the linked data visualization process, including extracting data analytically, applying visualization transformations, and generating views. It then covers challenges like scalability, handling heterogeneous data, and enabling user interaction. Various visualization techniques are classified and examples are provided, including bar charts, graphs, timelines, and maps. Finally, linked data visualization tools and examples using tools like Sigma, Sindice, and Information Workbench are described.
Dynamically Optimizing Queries over Large Scale Data PlatformsINRIA-OAK
Enterprises are adapting large-scale data processing platforms, such as Hadoop, to gain actionable insights from their "big data". Query optimization is still an open challenge in this environment due to the volume and heterogeneity of data, comprising both structured and un/semi-structured datasets. Moreover, it has become common practice to push business logic close to the data via user-defined functions (UDFs), which are usually opaque to the optimizer, further complicating cost-based optimization. As a result, classical relational query optimization techniques do not fit well in this setting, while at the same time, suboptimal query plans can be disastrous with large datasets. In this talk, I will present new techniques that take into account UDFs and correlations between relations for optimizing queries running on large scale clusters. We introduce "pilot runs", which execute part of the query over a sample of the data to estimate selectivities, and employ a cost-based optimizer that uses these selectivities to choose an initial query plan. Then, we follow a dynamic optimization approach, in which plans evolve as parts of the queries get executed. Our experimental results show that our techniques produce plans that are at least as good as, and up to 2x (4x) better for Jaql (Hive) than, the best hand-written left-deep query plans.
LR parsing allows parsers to see the entire right-hand side of a rule and perform lookahead, allowing it to handle a wider range of grammars than LL parsing. The document provides an example of LR parsing a simple expression grammar. It demonstrates the steps of building the LR parsing items, closure, and goto functions to generate the LR parsing table from the grammar. LR parsing tables contain the states, symbols, and parsing actions (reduce, shift, accept).
Enhancing Entity Linking by Combining NER ModelsJulien PLU
The document describes enhancements made to the ADEL entity linking framework. ADEL combines multiple named entity recognition models and uses a combination of linguistic and dictionary-based approaches. New features in ADEL include using a generic API to interface with NLP tools, combining multiple CRF models for entity extraction, clustering nil entities, and developing a new backend using Elasticsearch and Couchbase. The document compares the performance of the original 2015 version of ADEL to the new 2016 version on standard entity linking tasks and datasets.
The document provides an overview of natural language processing (NLP) and its related areas. It discusses the classical view of NLP involving stages of processing like syntax, semantics, pragmatics, etc. It also discusses the statistical/machine learning view of NLP, where NLP tasks are framed as classification problems and cues from language help reduce uncertainty. Finally, it provides examples of lower-level NLP tasks like part-of-speech tagging that can be viewed as sequence labeling problems.
Exploiting Linked Open Data and Natural Language Processing for Classificati...giuseppe_futia
This document discusses using the TellMeFirst topic extraction tool to automatically categorize political speeches from the White House website. TellMeFirst leverages the DBpedia knowledge base and natural language processing techniques to identify topics in text. It was able to accurately categorize US president profiles and extract topics from White House videos, providing insight into what First Lady Michelle Obama discusses in her speeches. Integrating this tool could help citizens more easily understand the content of political speeches.
A Vague Sense Classifier for Detecting Vague Definitions in OntologiesPanos Alexopoulos
The document summarizes a study on developing a classifier to detect vague definitions in ontologies. It describes training a naive Bayes classifier on 2000 WordNet senses labeled as vague or not vague. The classifier achieved 84% accuracy on a test set. It was then used to classify relations in the CiTO ontology, correctly identifying 82% as vague or not vague. While a subjectivity classifier only identified 40% of the same relations accurately. Future work involves improving the classifier and incorporating it into an ontology analysis tool.
This document describes an approach for bridging the gap between natural language queries and linked data concepts using BabelNet. The approach uses BabelNet for word sense disambiguation, named entity recognition and disambiguation. It parses queries, matches terms to ontology concepts and properties, generates candidate triples, and integrates the triples to produce SPARQL queries. The approach was evaluated on test data from QALD-2, achieving a promising 76% of questions answered correctly.
Effective Named Entity Recognition for Idiosyncratic Web CollectionseXascale Infolab
This document presents an approach for named entity recognition (NER) on idiosyncratic web collections. It proposes a two-step method using candidate selection followed by supervised classification. Candidate n-grams are first extracted based on frequency and then classified using decision trees trained on part-of-speech tags and features from knowledge bases. Experiments on scientific paper collections show the approach achieves up to 85% accuracy, outperforming traditional NER and maximum entropy models. Leveraging graphs of scientific concepts and domain-specific resources are found to be important for this task.
Exploiting Entity Linking in Queries For Entity RetrievalFaegheh Hasibi
Slides for the ICTIR 2016 paper: "Exploiting Entity Linking in Queries For Entity Retrieval"
The premise of entity retrieval is to better answer search queries by returning specific entities instead of documents. Many queries mention particular entities; recognizing and linking them to the corresponding entry in a knowledge base is known as the task of entity
linking in queries. In this paper we make a first attempt at bringing together these two, i.e., leveraging entity annotations of queries in the entity retrieval model. We introduce a new probabilistic component and show how it can be applied on top of any term based entity retrieval model that can be emulated in the Markov Random Field framework, including language models, sequential dependence models, as well as their fielded variations. Using a standard entity retrieval test collection, we show that our extension brings consistent improvements over all baseline methods, including the current state-of-the-art. We further show that our extension is robust against parameter settings.
Suitable for: 1. Technical Personnel and Decision-Makers are encouraged to participate in this training. 2. DECISION MAKERS: Technical Directors, Managers, Purchasers. 3. TECHNICAL PERSONNEL: Lecturers, Technical Sales, Marketing, Failure Analysis, Research & Development, Quality Control and Assurance, Production Engineers or Technicians. The characteristic of surface and near-surface regions of materials can be characterised by various surface analysis techniques. Applications of many engineering materials are determined by the surface and near-surface structures. Therefore, the well being of this region is essential in order to obtain a pre-required condition for those materials to be applied for a specific application. Typically, failure of engineering products may be traced back to surface/near-surface contamination or surface reconstruction. In order to obtain more information related to the failure, in-sight of these regions need to be exposed. This course is outlined to introduce basic principles of surface science, which serve as an essential foundation to explain the operation concepts and applications of several important surface analysis techniques. Know-how of interpreting the analysis data is also explained in this “easy-to-follow” and “easy-to-understand” training course. With these and the support of brief but sufficient fundamental theories, skill of selecting a relevant technique with respect to its practical engineering usage will be covered. Ultimate goal for this course is to increase level of knowledge in making a correct technical decision to solve surface related issues and transform knowledge into applications.
The document contains common Persian phrases for asking about and responding to how someone is doing in both informal and formal contexts. Some example phrases translated to English include:
- "How are you?" (informal) - "Chetori?"
- "How are you?" (formal) - "Haaletun chetore?"
- "I'm fine, thanks!" - "Khubam, Merci!"
- "Not bad" - "Bad nistam"
- "How about you?" (informal) - "To chetori?"
- "How about you?" (formal) - "Shomaa chetorin?"
- "
This document proposes a catalog of 20 patterns for multilingual linked open data (MLOD). The patterns are classified into activities like naming, dereferencing, labeling, and linking. Each pattern contains a name, description, context, example, and discussion. The goal is to provide generic solutions to common problems in MLOD based on experiences with DBPedia internationalization. Future work may involve feedback, additional patterns, and handling large datasets.
Stephen M. Shellman is the president, CEO, and chief scientist of Strategic Analysis Enterprises, Inc. and is an expert in event forecasting, sentiment analysis, and qualitative and quantitative analysis. He uses natural language processing and entity extraction methodologies to generate and analyze valuable organizational data about events and group behavior. Entity extraction involves detecting and classifying elements like names, locations, times, and numbers within text. It can identify both temporal and numerical expressions. Common entity extraction techniques include pattern-based, dictionary-based, hybrid pattern, hierarchical context, and formatting cue methods.
This document discusses named entity recognition for Arabic text. It describes the different types of named entities like people, locations, and organizations. It outlines the challenges of NER in Arabic including the lack of capitalization and ambiguities. The document then presents the methodology, system architecture, and implementation of an NERA system that uses gazetteers, grammar rules, and filtering to identify named entities in Arabic texts. Evaluation results and conclusions are also presented.
This document discusses query entity recognition (QER), which seeks to locate and classify elements in text queries into predefined categories like names, organizations, locations, etc. It describes challenges like differentiating similar entities and balancing free text for training. The document outlines approaches to QER like string matching, probabilistic shallow parsing using conditional random fields, and a hybrid method. It provides details on the features of the QER system, such as processing speed, integration formats, and evaluation metrics. Future directions are mentioned, like expanding QER into a complete query dynamics system.
Text mining techniques can be used to extract information and insights from the exponential growth of scientific literature. Key techniques include information retrieval to find relevant papers, named entity recognition to identify concepts, and information extraction to formalize facts. These techniques can be evaluated using benchmarking against manually annotated corpora, though creating such resources requires significant effort and the pragmatic approach of inspecting text mining outputs is much less work.
Named Entity Recognition - ACL 2011 PresentationRichard Littauer
This document summarizes an analysis of the performance of three named-entity recognition systems: Stanford, LBJ, and IdentifiFinder. The analysis found differences in how each system tokenized text and the total number of entities recognized. It also found ambiguity cases where the same token was assigned different entity types within a single document. To address evaluation issues, the document proposes a standardized unit test with examples of true positive entity types and guidelines for intrinsic evaluation of named-entity recognition.
RDF and other linked data standards — how to make use of big localization dataDave Lewis
The standards and interoperability challenge to using the Resource Description Framework for data resource in linked data. Based on work from CNGL (www.cngl.ie), the FALCON project (www.falcon-project.eu) and the LIDER project (www.lider-project.eu)
This presentation addresses the main issues of Linked Data and scalability. In particular, it provides gives details on approaches and technologies for clustering, distributing, sharing, and caching data. Furthermore, it addresses the means for publishing data trough could deployment and the relationship between Big Data and Linked Data, exploring how some of the solutions can be transferred in the context of Linked Data.
This document discusses interaction with linked data, focusing on visualization techniques. It begins with an overview of the linked data visualization process, including extracting data analytically, applying visualization transformations, and generating views. It then covers challenges like scalability, handling heterogeneous data, and enabling user interaction. Various visualization techniques are classified and examples are provided, including bar charts, graphs, timelines, and maps. Finally, linked data visualization tools and examples using tools like Sigma, Sindice, and Information Workbench are described.
Dynamically Optimizing Queries over Large Scale Data PlatformsINRIA-OAK
Enterprises are adapting large-scale data processing platforms, such as Hadoop, to gain actionable insights from their "big data". Query optimization is still an open challenge in this environment due to the volume and heterogeneity of data, comprising both structured and un/semi-structured datasets. Moreover, it has become common practice to push business logic close to the data via user-defined functions (UDFs), which are usually opaque to the optimizer, further complicating cost-based optimization. As a result, classical relational query optimization techniques do not fit well in this setting, while at the same time, suboptimal query plans can be disastrous with large datasets. In this talk, I will present new techniques that take into account UDFs and correlations between relations for optimizing queries running on large scale clusters. We introduce "pilot runs", which execute part of the query over a sample of the data to estimate selectivities, and employ a cost-based optimizer that uses these selectivities to choose an initial query plan. Then, we follow a dynamic optimization approach, in which plans evolve as parts of the queries get executed. Our experimental results show that our techniques produce plans that are at least as good as, and up to 2x (4x) better for Jaql (Hive) than, the best hand-written left-deep query plans.
LR parsing allows parsers to see the entire right-hand side of a rule and perform lookahead, allowing it to handle a wider range of grammars than LL parsing. The document provides an example of LR parsing a simple expression grammar. It demonstrates the steps of building the LR parsing items, closure, and goto functions to generate the LR parsing table from the grammar. LR parsing tables contain the states, symbols, and parsing actions (reduce, shift, accept).
Enhancing Entity Linking by Combining NER ModelsJulien PLU
The document describes enhancements made to the ADEL entity linking framework. ADEL combines multiple named entity recognition models and uses a combination of linguistic and dictionary-based approaches. New features in ADEL include using a generic API to interface with NLP tools, combining multiple CRF models for entity extraction, clustering nil entities, and developing a new backend using Elasticsearch and Couchbase. The document compares the performance of the original 2015 version of ADEL to the new 2016 version on standard entity linking tasks and datasets.
The document provides an overview of natural language processing (NLP) and its related areas. It discusses the classical view of NLP involving stages of processing like syntax, semantics, pragmatics, etc. It also discusses the statistical/machine learning view of NLP, where NLP tasks are framed as classification problems and cues from language help reduce uncertainty. Finally, it provides examples of lower-level NLP tasks like part-of-speech tagging that can be viewed as sequence labeling problems.
Exploiting Linked Open Data and Natural Language Processing for Classificati...giuseppe_futia
This document discusses using the TellMeFirst topic extraction tool to automatically categorize political speeches from the White House website. TellMeFirst leverages the DBpedia knowledge base and natural language processing techniques to identify topics in text. It was able to accurately categorize US president profiles and extract topics from White House videos, providing insight into what First Lady Michelle Obama discusses in her speeches. Integrating this tool could help citizens more easily understand the content of political speeches.
A Vague Sense Classifier for Detecting Vague Definitions in OntologiesPanos Alexopoulos
The document summarizes a study on developing a classifier to detect vague definitions in ontologies. It describes training a naive Bayes classifier on 2000 WordNet senses labeled as vague or not vague. The classifier achieved 84% accuracy on a test set. It was then used to classify relations in the CiTO ontology, correctly identifying 82% as vague or not vague. While a subjectivity classifier only identified 40% of the same relations accurately. Future work involves improving the classifier and incorporating it into an ontology analysis tool.
This document describes an approach for bridging the gap between natural language queries and linked data concepts using BabelNet. The approach uses BabelNet for word sense disambiguation, named entity recognition and disambiguation. It parses queries, matches terms to ontology concepts and properties, generates candidate triples, and integrates the triples to produce SPARQL queries. The approach was evaluated on test data from QALD-2, achieving a promising 76% of questions answered correctly.
Effective Named Entity Recognition for Idiosyncratic Web CollectionseXascale Infolab
This document presents an approach for named entity recognition (NER) on idiosyncratic web collections. It proposes a two-step method using candidate selection followed by supervised classification. Candidate n-grams are first extracted based on frequency and then classified using decision trees trained on part-of-speech tags and features from knowledge bases. Experiments on scientific paper collections show the approach achieves up to 85% accuracy, outperforming traditional NER and maximum entropy models. Leveraging graphs of scientific concepts and domain-specific resources are found to be important for this task.
Exploiting Entity Linking in Queries For Entity RetrievalFaegheh Hasibi
Slides for the ICTIR 2016 paper: "Exploiting Entity Linking in Queries For Entity Retrieval"
The premise of entity retrieval is to better answer search queries by returning specific entities instead of documents. Many queries mention particular entities; recognizing and linking them to the corresponding entry in a knowledge base is known as the task of entity
linking in queries. In this paper we make a first attempt at bringing together these two, i.e., leveraging entity annotations of queries in the entity retrieval model. We introduce a new probabilistic component and show how it can be applied on top of any term based entity retrieval model that can be emulated in the Markov Random Field framework, including language models, sequential dependence models, as well as their fielded variations. Using a standard entity retrieval test collection, we show that our extension brings consistent improvements over all baseline methods, including the current state-of-the-art. We further show that our extension is robust against parameter settings.
Suitable for: 1. Technical Personnel and Decision-Makers are encouraged to participate in this training. 2. DECISION MAKERS: Technical Directors, Managers, Purchasers. 3. TECHNICAL PERSONNEL: Lecturers, Technical Sales, Marketing, Failure Analysis, Research & Development, Quality Control and Assurance, Production Engineers or Technicians. The characteristic of surface and near-surface regions of materials can be characterised by various surface analysis techniques. Applications of many engineering materials are determined by the surface and near-surface structures. Therefore, the well being of this region is essential in order to obtain a pre-required condition for those materials to be applied for a specific application. Typically, failure of engineering products may be traced back to surface/near-surface contamination or surface reconstruction. In order to obtain more information related to the failure, in-sight of these regions need to be exposed. This course is outlined to introduce basic principles of surface science, which serve as an essential foundation to explain the operation concepts and applications of several important surface analysis techniques. Know-how of interpreting the analysis data is also explained in this “easy-to-follow” and “easy-to-understand” training course. With these and the support of brief but sufficient fundamental theories, skill of selecting a relevant technique with respect to its practical engineering usage will be covered. Ultimate goal for this course is to increase level of knowledge in making a correct technical decision to solve surface related issues and transform knowledge into applications.
The document contains common Persian phrases for asking about and responding to how someone is doing in both informal and formal contexts. Some example phrases translated to English include:
- "How are you?" (informal) - "Chetori?"
- "How are you?" (formal) - "Haaletun chetore?"
- "I'm fine, thanks!" - "Khubam, Merci!"
- "Not bad" - "Bad nistam"
- "How about you?" (informal) - "To chetori?"
- "How about you?" (formal) - "Shomaa chetorin?"
- "
This document provides instructions for students to create an audio portfolio and audio journal using the ANVILL platform to practice speaking and writing in German. It outlines four steps for making recordings and uploading audio or video files: 1) recording yourself by clicking the plus sign to create a new entry, 2) filling in information and clicking start to begin recording, 3) uploading files by clicking choose file and then upload, and 4) checking that the uploaded file is visible in the attachments box before saving. Only the student and instructor will be able to view the private posts.
This document provides instructions for students to create audio journal entries in their German language course using the ANVILL platform. It outlines a 4 step process for recording and uploading audio files, as well as adding written reflections. Students can record directly in the system or upload existing audio/video files. Only the student and instructor will be able to view the private journal entries. Assistance is available by writing to the help desk.
The document provides instructions for students to summarize a video or personal story using the ANVILL note-taking platform. It consists of 4 steps: 1) Watch the video and write a summary in the private portfolio entry. 2) Give the summary a title and write it in the provided box or copy from another source. 3) Upload any additional files by clicking "Choose File" and "Upload" buttons. 4) Check that the uploaded file appears in the attachments box and save the entry, which only the instructor can see. Students are asked to follow these steps to share their written summary privately with the instructor using ANVILL's note-taking and file sharing features.
Video can enhance online courses in several ways:
1) It provides context by filling important gaps in meaning, personalities, and cultural situations.
2) Using video of yourself adds familiarity, trust, and clarity to personalize students' experiences.
3) Multiple ways of accessing content through video can improve understanding and learning. Students can narrate videos, take notes, conduct research, and create tutorials or snippets of learning.
4) Video submissions provide compelling evidence of student learning through assignments, role-plays, or ePortfolios.
The document provides instructions for creating a lesson in ANVILL 2 that includes a video prompt and a voiceboard for student responses. It is an 8 step process: 1) Add a new lesson, 2) Name the lesson, 3) Customize the lesson, 4) Add a media file by choosing YouTube, 5) Enter the YouTube URL, 6) Add the video to the lesson, 7) Rearrange content blocks, and 8) Add a voiceboard for student responses. The instructions explain how to drag and drop blocks and add different content types to create interactive oral/aural tasks.
The document discusses notes on developing intercultural competency in virtual exchanges. It references reports that emphasize the importance of developing students' translingual and transcultural competence. It also references publications by the Council of Europe that promote integrating an intercultural dimension into language teaching. The document discusses definitions of intercultural skills put forth by the Common European Framework of Reference for Languages, including the ability to relate different cultures and deal with intercultural misunderstandings. It notes that teachers need skills for teaching intercultural communication, not just language.
The document discusses the benefits of meditation for reducing stress and anxiety. Regular meditation practice can help calm the mind and body by lowering heart rate and blood pressure. Making meditation a part of a daily routine, even if just 10-15 minutes per day, can have mental and physical health benefits over time by helping people feel more relaxed and focused.
ANVILL's "portfolio tool". This short tutorial explains how to add your weekly reflections. These reflections are a dialog between you and your instructor. You're the only ones who can view them.
This document provides instructions for copying and pasting files from a word processor into the course site. It outlines a 4-step process: 1) click the "+" button to add a file, 2) copy text from the word processor document, 3) paste the text using Ctrl-V and click insert, 4) the file will appear with a yellow background and may require a page refresh. Students are asked to document their game journal and ePortfolio for certain tasks in this manner.
This document provides instructions for creating a multimedia quiz in ANVILL by adding different media elements like images, audio, and URLs. It explains how to insert images less than 600x600 pixels by clicking the insert images button and uploading files from your hard drive. Audio files can be used for listening items and should be saved as MP3 files and reduced in size. Videos can also be added by uploading MOV files under 400x300 pixels. URLs can be included by pasting the link. All media is uploaded through the media tools.
The document provides a 6 step tutorial for getting started with integrated discussions on the ANVILL 2 course system. It explains how to post a new message by clicking the "+" button and writing or pasting in a message. It describes how the message will appear with the option to edit it or add formatting and images. It also explains how to comment on other posts and view notifications and messages on the dashboard.
Participating in online course discussions benefits all students by allowing them to share what they're learning. The document then provides step-by-step instructions for posting and commenting on discussion messages, including how to edit or delete your own posts and view notifications of responses from other students. It highlights the discussion board, dashboard, and notifications features for following and contributing to online conversations.
1. The document provides instructions for checking and setting up microphone and audio settings to complete a voice recording assignment using Voiceboards.
2. It guides the user to check their Adobe Flash Player and microphone are installed and working properly.
3. Detailed steps are outlined for recording and submitting an audio message within a group on Voiceboards, including selecting the group, giving the recording a title, testing and recording the audio, and listening to the submission.
Kim's Game is an exercise where students are shown different sets of objects, sounds or images. They then must communicate with a partner to determine which items were the same or different between the sets. It helps develop language and memory skills. This game can also be played asynchronously online using tools that allow students to share media and communicate virtually. Students first see something individually, then must convey it to a partner over video chat or messaging. The pair decides how many items were the same versus different.
5. Albert Einstein, der Theoretiker
1879-1955
“Lichtteilchen” mit ausreichender
Energie können Elektronenemission
verursachen.
“Der photoelektrische Effekt” Dafür erhielt
Einstein den Nobelpreis
6. Kai Siegbahn, der Praktiker
1918-2007
„Kai Manne Börje Siegbahn“ von Jan Collsiöö - [1] Dutch National Archives, The Hague, Fotocollectie Algemeen Nederlands Persbureau (ANEFO), 1945-1989. Lizenziert unter CC BY-SA 3.0 nl über
Wikimedia Commons - https://commons.wikimedia.org/wiki/File:Kai_Manne_B%C3%B6rje_Siegbahn.jpg#/media/File:Kai_Manne_B%C3%B6rje_Siegbahn.jpg
“Wenn wir die Geschwindigkeit der
Photoelektronen messen, wissen wir, welche
Elemente in der Nähe der Oberfläche sind.”
8. Die Röntgenphotoelektronenspektroskopie:
das Spektrometer
der Analysator (Halbkugelanalysator)der Elektronendetektor
(mit einem Sekundärelektronenvervielfacher)
die Elektronik
Vakuumpumpen im Kasten!
die Analysekammer
Hersteller: ThermoScientific