Entity Linking Combining Open Source Annotators
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Poster for 2015 NAACL Demo: Ruiz, Pablo, Thierry Poibeau, and Frédérique Mélanie (2015). ELCO3 : Entity Linking with Corpus Coherence Combining Open Source Annotators. In Proceedings of the Demonstrations at NAACL 2015. Denver, U.S
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![http://www.lattice.cnrs.fr | Demonstrations at NAACL HLT 2015, Conference of the North American Chapter of the Association for Computational Linguistics – Human Language Technologies, Denver, Colorado (US), May 31-June 5
Expression extractions should be improved and implemented on open source software. The careful use of natural language processing
algorithms could provide better filtering metrics and support in expression merging
The manual filtering is crucial because it allows entities to be reduced to a set size appropriate for analysis, but also recovering
important entities that could have been excluded by the automatic filtering.
Expressed in [1] by social scientists from médialab (Paris Institute of Political Studies, SciencesPo)
OOV IV
LATTICE Lab
CNRS – Ecole Normale Supérieure
U Paris 3 Sorbonne Nouvelle
ELCO3: Entity Linking with Corpus Coherence Combining Open Source Annotators
Pablo Ruiz, Thierry Poibeau and Frédérique Mélanie
pablo.ruiz.fabo@ens.fr
Our users’ needs in Entity Linking (EL)
o Target users: social science researchers
o Performance of EL systems varies widely depending on corpus
characteristics and types of entities required
o Difficult for users to choose optimal EL system for their corpora
o Our target users wish to filter EL results, making informed
choices about entities to keep and discard
o Public open source tools
o Combine outputs of several tools to get complementary results
o Providing metrics for users to evaluate quality of an annotation
o Simultaneous access to metrics and text to validate annotations
o Besides manual selection, automatic selection also possible via
weighted voting of annotations
The Problem Our Approach
Demo features
TRAFFIC-LIGHT MATRIX FORMAT
o Annotation confidence scores provided by EL services
o Measures of coherence between an entity and the most
representative entities in the corpus
› Wikipedia Link-based Measure: Relatedness between two entities
as a function of Wikipedia pages linking to both and linking to one only
Milne-Witten [3] coherence between entities e1 and e2 (as in Hoffart et al. [4])
› Other possible measures
• Distance between entities’ categories in a Wikipedia
category graph
Corpus: subset of PoliInformatics [2], about 2008 US financial crisis
(1) Query via Search Text displays:
• Document Panel: Documents matching the query
• Entity Panel: Entities extracted in the documents matching the
query displayed on doc. panel, plus:
(2) Confidence Scores for each annotator, normalized to a 0-1
range. (T=Tagme, S=Spotlight, W=Wikipedia Miner).
(3) Coherence score between the entity and a representative
subset of the corpus entities.
(4) Entities not coherent with the corpus are flagged in red.
(5) Query via Search Entities displays:
• Entity Panel: Entities matching the query.
• Document Panel: Documents containing one of the entities
displayed on the entity panel.
(6) Refine Search: Entities can be selected with a list of types
(like ORG) or selected individually with checkboxes.
(7) The Auto-Selection tab shows the output of an automatic
filtering via weighted voting of annotations.
(8) Charts: examples of co-occurrence networks, created offline
exploiting workflow information (sentence number, confidence, …)
0.0
1.0
Scale
DOC.PANELENTITYPANEL 1
5
3
4
6
2
7
8
System workflows
o User always has access to full results, but the workflow can
select a subset of the annotations automatically.
o Workflow combines, via weighted voting, outputs of:
TagMe2, DBpedia Spotlight, Wikipedia Miner, AIDA, Babelfy
o Votes are weighted according to each annotator’s precision on
two reference corpora (IITB and AIDA/CONLL B), depending on
whether user requires annotations for common-noun entity
mentions or not.
on demo not shown on demo
Evaluation
o Automatic EL system combination improved results over each
individual system’s results ([5], our *SEM poster).
o Assessed with strong annotation match and entity match [6] on
four different corpora: AIDA/CONLL B, IITB, MSNBC, AQUAINT.
[1] T. Venturini & D. Guido. 2012. Once upon a text. An ANT [Actor-Network Theory] Tale in Text
Analytics. Sociologica, 3:1-17. Il Mulino, Bologna.
[2] N. Smith et al. 2014. Overview of the 2014 NLP Unshared Task in PoliInformatics. In Proc. ACL
LACSS Workshop.
[3] D. Milne & I. Witten. 2008. An effective, low-cost measure of semantic relatedness obtained from
Wikipedia links. In Proc AAAI WS on Wikipedia and AI.
[4] J. Hoffart et al. 2011. Robust disambiguation of named entities in text. In Proc. EMNLP.
[5] P. Ruiz & T. Poibeau. 2015. Combining open source annotators for entity linking through
weighted voting. In Proc. *SEM.
[6] M. Cornolti, P. Ferragina & M. Ciaramita. (2013). A framework for benchmarking entity-annotation
systems. In Proc. of WWW, 249-260.
Metrics to assist in manual filtering
Annotation voting for automatic filtering
DEMO LINK: http://129.199.228.10/nav/gui/](https://image.slidesharecdn.com/posterdemonaacllandscapefinal-150624133825-lva1-app6892/85/Entity-Linking-Combining-Open-Source-Annotators-1-320.jpg)
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This document summarizes a research paper that analyzed comments on South Korean politicians' profiles on the social networking site Cyworld. The researchers collected over 200 random comments and categorized them as positive, negative, or irrelevant. They then used machine learning algorithms like naive Bayes and support vector machines to automatically classify new comments. The algorithms achieved accuracies around 70%, outperforming a model that labeled all comments as the most common class. The tools developed in this research could help analyze political communication on social networks and public sentiment toward politicians.
Assignment 5 interoperability slide share
The document discusses metadata interoperability, which allows systems to exchange data with minimal loss of content or functionality. It defines interoperability and describes challenges like ensuring machines can communicate, systems can understand objects from other systems, and structures are in place for correct semantic interpretation. The document also outlines categories of achieving interoperability and concludes that organizations must determine specific obstacles and balance technical standards with usability and flexibility.
Tags as tools for social classification
Presentation held at the 34th Annual Conference of the German Classification Society, Karlsruhe, 21-23 July 2010.
RUNNING HEADER Analytics Ecosystem1Analytics Ecosystem4.docx
RUNNING HEADER: Analytics Ecosystem 1
Analytics Ecosystem 4
Analytics Ecosystem
Lisa Garay
Rasmussen College
Authors Note
This paper is being submitted for Anastasia Rashtchian’s B288 Business Analytics Course.
This paper looks at the nine clusters of the ecosystem. Clustering refers to a system of grouping functions that are similar so as to set them out from others. It begins by highlighting them before proceeding to defining them. It then identifies clusters that represent technology developers and technology users. Peer reviewed materials are used in this endeavor.
They include executive sponsor cluster which contains information that concerns administrators for directing the system. Another one is end-user tools and dashboards cluster that is made of functions that facilitate ability of persons to ultimately engage the system. Data owners cluster is made up of programs that are related to persons who have data in the system. Business users’ cluster is made up of functions that are related to clients of the system. Business applications and systems cluster is made up programs related to features of a given system. Developers cluster is made of programs that are related to the development of programs in the system. Analyst cluster is made up of materials that are related to analysis of data in the system. SME cluster that is made up switches that run SME applications in the system. Lastly, operational data stores that are made up of programs that are concerned with storage of data in a system (Pitelis, 2012).
While developers cluster is made up of technology developers in the system, business users’ cluster is made up of technology users in the system. In conclusion, clustering serves to bring roles together as well as separating roles that are not related in a system (Cameron, Gelbach & Miller, 2012).
They can be represented as follows:-
References
Cameron, A. C., Gelbach, J. B., & Miller, D. L. (2012). Robust inference with multiway clustering. Journal of Business & Economic Statistics.
Pitelis, C. (2012). Clusters, entrepreneurial ecosystem co-creation, and appropriability: a conceptual framework. Industrial and Corporate Change, dts008.
Infrastructure
Executive Sponsor Cluster
End-user tools and dashboards cluster
operational data stores
Data Owners Cluster
Business users' cluster
Business systems and applications cluster
Developers Cluster
Analysts Cluster
SME cluster
4
Running head: Sentiment analysis
Sentiment Analysis
Lisa Garay
Rasmussen College
Authors Note
This paper is being submitted for Anastashia Rashtcian’s B288 Business Analytics course.
Sentiment analysis has played a significant role in the concurrent marketing field, specifically in product marketing. According to Somasundaran, Swapna, (2010), the process’ operational module is structured on a data mining sequence, whereby the end users of given particulars the feedback pertaining a used.
Notes on mining social media updated
This dissertation analyzes social media data and outlines approaches for understanding online communication and collaboration. It presents algorithms for detecting communities using structural and semantic properties. It analyzes blog subscription patterns and the microblogging phenomenon. Systems are developed for opinion retrieval from blogs and identifying influential users. The growth of social media and tagging behavior are also studied through analysis of tags and social graphs.
eventdemo2016
This document describes a system that extracts events from multiple data sources like text, images and videos. It constructs "event cubes" to organize the extracted information by dimensions like location and participants. The system allows users to search for events matching query criteria and recommends related events based on their attributes. It summarizes events and extracts visual concepts and patterns to provide richer event profiles to users.
Keystone Summer School 2015: Mauro Dragoni, Ontologies For Information Retrieval
The presentation provides an overview of what an ontology is and how it can be used for representing information and for retrieving data with a particular focus on the linguistic resources available for supporting this kind of task. Overview of semantic-based retrieval approaches by highlighting the pro and cons of using semantic approaches with respect to classic ones. Use cases are presented and discussed
IRJET- Improved Real-Time Twitter Sentiment Analysis using ML & Word2Vec
This document discusses improving real-time Twitter sentiment analysis using machine learning and Word2Vec. It begins by introducing sentiment analysis and its importance for product analysis and business. Next, it describes extracting Twitter data using APIs, preprocessing it, and applying machine learning algorithms like Naive Bayes, logistic regression, and decision trees to classify tweets as expressing positive, negative or neutral sentiment. It also reviews literature on using techniques like linguistic analysis and ensemble models to improve sentiment analysis from social media sources.
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This ppt contains the production of a fermented food name - sauerkraut
Discovery of An Apparent Red, High-Velocity Type Ia Supernova at 𝐳 = 2.9 wi...
We present the JWST discovery of SN 2023adsy, a transient object located in a host galaxy JADES-GS
+
53.13485
−
27.82088
with a host spectroscopic redshift of
2.903
±
0.007
. The transient was identified in deep James Webb Space Telescope (JWST)/NIRCam imaging from the JWST Advanced Deep Extragalactic Survey (JADES) program. Photometric and spectroscopic followup with NIRCam and NIRSpec, respectively, confirm the redshift and yield UV-NIR light-curve, NIR color, and spectroscopic information all consistent with a Type Ia classification. Despite its classification as a likely SN Ia, SN 2023adsy is both fairly red (
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(
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∼
0.9
) despite a host galaxy with low-extinction and has a high Ca II velocity (
19
,
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2
,
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km/s) compared to the general population of SNe Ia. While these characteristics are consistent with some Ca-rich SNe Ia, particularly SN 2016hnk, SN 2023adsy is intrinsically brighter than the low-
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Ca-rich population. Although such an object is too red for any low-
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cosmological sample, we apply a fiducial standardization approach to SN 2023adsy and find that the SN 2023adsy luminosity distance measurement is in excellent agreement (
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Ca-rich SNe Ia, SN 2023adsy is standardizable and gives no indication that SN Ia standardized luminosities change significantly with redshift. A larger sample of distant SNe Ia is required to determine if SN Ia population characteristics at high-
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truly diverge from their low-
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counterparts, and to confirm that standardized luminosities nevertheless remain constant with redshift.
JAMES WEBB STUDY THE MASSIVE BLACK HOLE SEEDS
The pathway(s) to seeding the massive black holes (MBHs) that exist at the heart of galaxies in the present and distant Universe remains an unsolved problem. Here we categorise, describe and quantitatively discuss the formation pathways of both light and heavy seeds. We emphasise that the most recent computational models suggest that rather than a bimodal-like mass spectrum between light and heavy seeds with light at one end and heavy at the other that instead a continuum exists. Light seeds being more ubiquitous and the heavier seeds becoming less and less abundant due the rarer environmental conditions required for their formation. We therefore examine the different mechanisms that give rise to different seed mass spectrums. We show how and why the mechanisms that produce the heaviest seeds are also among the rarest events in the Universe and are hence extremely unlikely to be the seeds for the vast majority of the MBH population. We quantify, within the limits of the current large uncertainties in the seeding processes, the expected number densities of the seed mass spectrum. We argue that light seeds must be at least 103 to 105 times more numerous than heavy seeds to explain the MBH population as a whole. Based on our current understanding of the seed population this makes heavy seeds (Mseed > 103 M⊙) a significantly more likely pathway given that heavy seeds have an abundance pattern than is close to and likely in excess of 10−4 compared to light seeds. Finally, we examine the current state-of-the-art in numerical calculations and recent observations and plot a path forward for near-future advances in both domains.
The cost of acquiring information by natural selection
This is a short talk that I gave at the Banff International Research Station workshop on Modeling and Theory in Population Biology. The idea is to try to understand how the burden of natural selection relates to the amount of information that selection puts into the genome.
It's based on the first part of this research paper:
The cost of information acquisition by natural selection
Ryan Seamus McGee, Olivia Kosterlitz, Artem Kaznatcheev, Benjamin Kerr, Carl T. Bergstrom
bioRxiv 2022.07.02.498577; doi: https://doi.org/10.1101/2022.07.02.498577
Signatures of wave erosion in Titan’s coasts
The shorelines of Titan’s hydrocarbon seas trace flooded erosional landforms such as river valleys; however, it isunclear whether coastal erosion has subsequently altered these shorelines. Spacecraft observations and theo-retical models suggest that wind may cause waves to form on Titan’s seas, potentially driving coastal erosion,but the observational evidence of waves is indirect, and the processes affecting shoreline evolution on Titanremain unknown. No widely accepted framework exists for using shoreline morphology to quantitatively dis-cern coastal erosion mechanisms, even on Earth, where the dominant mechanisms are known. We combinelandscape evolution models with measurements of shoreline shape on Earth to characterize how differentcoastal erosion mechanisms affect shoreline morphology. Applying this framework to Titan, we find that theshorelines of Titan’s seas are most consistent with flooded landscapes that subsequently have been eroded bywaves, rather than a uniform erosional process or no coastal erosion, particularly if wave growth saturates atfetch lengths of tens of kilometers.
Mechanisms and Applications of Antiviral Neutralizing Antibodies - Creative B...
Neutralizing antibodies, pivotal in immune defense, specifically bind and inhibit viral pathogens, thereby playing a crucial role in protecting against and mitigating infectious diseases. In this slide, we will introduce what antibodies and neutralizing antibodies are, the production and regulation of neutralizing antibodies, their mechanisms of action, classification and applications, as well as the challenges they face.
MICROBIAL INTERACTION PPT/ MICROBIAL INTERACTION AND THEIR TYPES // PLANT MIC...
MICROBIAL INTERACTION PPT/ MICROBIAL INTERACTION AND THEIR TYPES // PLANT MIC...ABHISHEK SONI NIMT INSTITUTE OF MEDICAL AND PARAMEDCIAL SCIENCES , GOVT PG COLLEGE NOIDA
Microbial interaction
Microorganisms interacts with each other and can be physically associated with another organisms in a variety of ways.
One organism can be located on the surface of another organism as an ectobiont or located within another organism as endobiont.
Microbial interaction may be positive such as mutualism, proto-cooperation, commensalism or may be negative such as parasitism, predation or competition
Types of microbial interaction
Positive interaction: mutualism, proto-cooperation, commensalism
Negative interaction: Ammensalism (antagonism), parasitism, predation, competition
I. Mutualism:
It is defined as the relationship in which each organism in interaction gets benefits from association. It is an obligatory relationship in which mutualist and host are metabolically dependent on each other.
Mutualistic relationship is very specific where one member of association cannot be replaced by another species.
Mutualism require close physical contact between interacting organisms.
Relationship of mutualism allows organisms to exist in habitat that could not occupied by either species alone.
Mutualistic relationship between organisms allows them to act as a single organism.
Examples of mutualism:
i. Lichens:
Lichens are excellent example of mutualism.
They are the association of specific fungi and certain genus of algae. In lichen, fungal partner is called mycobiont and algal partner is called
II. Syntrophism:
It is an association in which the growth of one organism either depends on or improved by the substrate provided by another organism.
In syntrophism both organism in association gets benefits.
Compound A
Utilized by population 1
Compound B
Utilized by population 2
Compound C
utilized by both Population 1+2
Products
In this theoretical example of syntrophism, population 1 is able to utilize and metabolize compound A, forming compound B but cannot metabolize beyond compound B without co-operation of population 2. Population 2is unable to utilize compound A but it can metabolize compound B forming compound C. Then both population 1 and 2 are able to carry out metabolic reaction which leads to formation of end product that neither population could produce alone.
Examples of syntrophism:
i. Methanogenic ecosystem in sludge digester
Methane produced by methanogenic bacteria depends upon interspecies hydrogen transfer by other fermentative bacteria.
Anaerobic fermentative bacteria generate CO2 and H2 utilizing carbohydrates which is then utilized by methanogenic bacteria (Methanobacter) to produce methane.
ii. Lactobacillus arobinosus and Enterococcus faecalis:
In the minimal media, Lactobacillus arobinosus and Enterococcus faecalis are able to grow together but not alone.
The synergistic relationship between E. faecalis and L. arobinosus occurs in which E. faecalis require folic acid
Sustainable Land Management - Climate Smart Agriculture
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TOPIC OF DISCUSSION: CENTRIFUGATION SLIDESHARE.pptx
Centrifugation is a powerful technique used in laboratories to separate components of a heterogeneous mixture based on their density. This process utilizes centrifugal force to rapidly spin samples, causing denser particles to migrate outward more quickly than lighter ones. As a result, distinct layers form within the sample tube, allowing for easy isolation and purification of target substances.
Quality assurance B.pharm 6th semester BP606T UNIT 5
Warehousing
Good warehousing practices
Material management
快速办理(UAM毕业证书)马德里自治大学毕业证学位证一模一样
学校原件一模一样【微信：741003700 】《(UAM毕业证书)马德里自治大学毕业证学位证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
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2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
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8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
Direct Seeded Rice - Climate Smart Agriculture
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Pests of Storage_Identification_Dr.UPR.pdf
InIndia-post-harvestlosses-unscientificstorage,insects,rodents,micro-organismsetc.,accountforabout10percentoftotalfoodgrains
Graininfestation
Directdamage
Indirectly
•theexuviae,skin,deadinsects
•theirexcretawhichmakefoodunfitforhumanconsumption
About600speciesofinsectshavebeenassociatedwithstoredgrainproducts
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Compositions of iron-meteorite parent bodies constrainthe structure of the pr...
Magmatic iron-meteorite parent bodies are the earliest planetesimals in the Solar System,and they preserve information about conditions and planet-forming processes in thesolar nebula. In this study, we include comprehensive elemental compositions andfractional-crystallization modeling for iron meteorites from the cores of five differenti-ated asteroids from the inner Solar System. Together with previous results of metalliccores from the outer Solar System, we conclude that asteroidal cores from the outerSolar System have smaller sizes, elevated siderophile-element abundances, and simplercrystallization processes than those from the inner Solar System. These differences arerelated to the formation locations of the parent asteroids because the solar protoplane-tary disk varied in redox conditions, elemental distributions, and dynamics at differentheliocentric distances. Using highly siderophile-element data from iron meteorites, wereconstruct the distribution of calcium-aluminum-rich inclusions (CAIs) across theprotoplanetary disk within the first million years of Solar-System history. CAIs, the firstsolids to condense in the Solar System, formed close to the Sun. They were, however,concentrated within the outer disk and depleted within the inner disk. Future modelsof the structure and evolution of the protoplanetary disk should account for this dis-tribution pattern of CAIs.
Anti-Universe And Emergent Gravity and the Dark Universe
Recent theoretical progress indicates that spacetime and gravity emerge together from the entanglement structure of an underlying microscopic theory. These ideas are best understood in Anti-de Sitter space, where they rely on the area law for entanglement entropy. The extension to de Sitter space requires taking into account the entropy and temperature associated with the cosmological horizon. Using insights from string theory, black hole physics and quantum information theory we argue that the positive dark energy leads to a thermal volume law contribution to the entropy that overtakes the area law precisely at the cosmological horizon. Due to the competition between area and volume law entanglement the microscopic de Sitter states do not thermalise at sub-Hubble scales: they exhibit memory effects in the form of an entropy displacement caused by matter. The emergent laws of gravity contain an additional ‘dark’ gravitational force describing the ‘elastic’ response due to the entropy displacement. We derive an estimate of the strength of this extra force in terms of the baryonic mass, Newton’s constant and the Hubble acceleration scale a0 = cH0, and provide evidence for the fact that this additional ‘dark gravity force’ explains the observed phenomena in galaxies and clusters currently attributed to dark matter.
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Entity Linking Combining Open Source Annotators
- 1. http://www.lattice.cnrs.fr | Demonstrations at NAACL HLT 2015, Conference of the North American Chapter of the Association for Computational Linguistics – Human Language Technologies, Denver, Colorado (US), May 31-June 5 Expression extractions should be improved and implemented on open source software. The careful use of natural language processing algorithms could provide better filtering metrics and support in expression merging The manual filtering is crucial because it allows entities to be reduced to a set size appropriate for analysis, but also recovering important entities that could have been excluded by the automatic filtering. Expressed in [1] by social scientists from médialab (Paris Institute of Political Studies, SciencesPo) OOV IV LATTICE Lab CNRS – Ecole Normale Supérieure U Paris 3 Sorbonne Nouvelle ELCO3: Entity Linking with Corpus Coherence Combining Open Source Annotators Pablo Ruiz, Thierry Poibeau and Frédérique Mélanie pablo.ruiz.fabo@ens.fr Our users’ needs in Entity Linking (EL) o Target users: social science researchers o Performance of EL systems varies widely depending on corpus characteristics and types of entities required o Difficult for users to choose optimal EL system for their corpora o Our target users wish to filter EL results, making informed choices about entities to keep and discard o Public open source tools o Combine outputs of several tools to get complementary results o Providing metrics for users to evaluate quality of an annotation o Simultaneous access to metrics and text to validate annotations o Besides manual selection, automatic selection also possible via weighted voting of annotations The Problem Our Approach Demo features TRAFFIC-LIGHT MATRIX FORMAT o Annotation confidence scores provided by EL services o Measures of coherence between an entity and the most representative entities in the corpus › Wikipedia Link-based Measure: Relatedness between two entities as a function of Wikipedia pages linking to both and linking to one only Milne-Witten [3] coherence between entities e1 and e2 (as in Hoffart et al. [4]) › Other possible measures • Distance between entities’ categories in a Wikipedia category graph Corpus: subset of PoliInformatics [2], about 2008 US financial crisis (1) Query via Search Text displays: • Document Panel: Documents matching the query • Entity Panel: Entities extracted in the documents matching the query displayed on doc. panel, plus: (2) Confidence Scores for each annotator, normalized to a 0-1 range. (T=Tagme, S=Spotlight, W=Wikipedia Miner). (3) Coherence score between the entity and a representative subset of the corpus entities. (4) Entities not coherent with the corpus are flagged in red. (5) Query via Search Entities displays: • Entity Panel: Entities matching the query. • Document Panel: Documents containing one of the entities displayed on the entity panel. (6) Refine Search: Entities can be selected with a list of types (like ORG) or selected individually with checkboxes. (7) The Auto-Selection tab shows the output of an automatic filtering via weighted voting of annotations. (8) Charts: examples of co-occurrence networks, created offline exploiting workflow information (sentence number, confidence, …) 0.0 1.0 Scale DOC.PANELENTITYPANEL 1 5 3 4 6 2 7 8 System workflows o User always has access to full results, but the workflow can select a subset of the annotations automatically. o Workflow combines, via weighted voting, outputs of: TagMe2, DBpedia Spotlight, Wikipedia Miner, AIDA, Babelfy o Votes are weighted according to each annotator’s precision on two reference corpora (IITB and AIDA/CONLL B), depending on whether user requires annotations for common-noun entity mentions or not. on demo not shown on demo Evaluation o Automatic EL system combination improved results over each individual system’s results ([5], our *SEM poster). o Assessed with strong annotation match and entity match [6] on four different corpora: AIDA/CONLL B, IITB, MSNBC, AQUAINT. [1] T. Venturini & D. Guido. 2012. Once upon a text. An ANT [Actor-Network Theory] Tale in Text Analytics. Sociologica, 3:1-17. Il Mulino, Bologna. [2] N. Smith et al. 2014. Overview of the 2014 NLP Unshared Task in PoliInformatics. In Proc. ACL LACSS Workshop. [3] D. Milne & I. Witten. 2008. An effective, low-cost measure of semantic relatedness obtained from Wikipedia links. In Proc AAAI WS on Wikipedia and AI. [4] J. Hoffart et al. 2011. Robust disambiguation of named entities in text. In Proc. EMNLP. [5] P. Ruiz & T. Poibeau. 2015. Combining open source annotators for entity linking through weighted voting. In Proc. *SEM. [6] M. Cornolti, P. Ferragina & M. Ciaramita. (2013). A framework for benchmarking entity-annotation systems. In Proc. of WWW, 249-260. Metrics to assist in manual filtering Annotation voting for automatic filtering DEMO LINK: http://129.199.228.10/nav/gui/