狠狠撸shows by User: FaeghehHasibi
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Sun, 08 Oct 2017 09:25:57 GMT狠狠撸Share feed for 狠狠撸shows by User: FaeghehHasibiSemantic Search and Result Presentation with Entity Cards
/slideshow/semantic-search-and-result-presentation-with-entity-cards-80573073/80573073
seatalk-171008092557 狠狠撸s of Search Engines Amsterdam (SEA) talk
https://www.meetup.com/SEA-Search-Engines-Amsterdam/events/239659928/
Modern search engines have moved from the traditional 鈥渢en blue links鈥� environment towards understanding searchers' intent and providing them with the focused responses; a paradigm that is referred to as 鈥渟emantic search鈥�. Semantic search is an umbrella term that encompasses various techniques, including but not limited to, query understanding, entity retrieval, and result presentation. In this talk, I will give a brief overview on each of these tasks, and further focus on the result presentation aspect. Specifically, I will present methods for generating content for 鈥渆ntity cards鈥濃€�, the informational panels that are presented at the right column of search engine results pages. I will end the talk by introducing a practical toolkit and dataset that are meant to foster research in this area.]]>
狠狠撸s of Search Engines Amsterdam (SEA) talk
https://www.meetup.com/SEA-Search-Engines-Amsterdam/events/239659928/
Modern search engines have moved from the traditional 鈥渢en blue links鈥� environment towards understanding searchers' intent and providing them with the focused responses; a paradigm that is referred to as 鈥渟emantic search鈥�. Semantic search is an umbrella term that encompasses various techniques, including but not limited to, query understanding, entity retrieval, and result presentation. In this talk, I will give a brief overview on each of these tasks, and further focus on the result presentation aspect. Specifically, I will present methods for generating content for 鈥渆ntity cards鈥濃€�, the informational panels that are presented at the right column of search engine results pages. I will end the talk by introducing a practical toolkit and dataset that are meant to foster research in this area.]]>
Sun, 08 Oct 2017 09:25:57 GMT/slideshow/semantic-search-and-result-presentation-with-entity-cards-80573073/80573073FaeghehHasibi@slideshare.net(FaeghehHasibi)Semantic Search and Result Presentation with Entity CardsFaeghehHasibi狠狠撸s of Search Engines Amsterdam (SEA) talk
https://www.meetup.com/SEA-Search-Engines-Amsterdam/events/239659928/
Modern search engines have moved from the traditional 鈥渢en blue links鈥� environment towards understanding searchers' intent and providing them with the focused responses; a paradigm that is referred to as 鈥渟emantic search鈥�. Semantic search is an umbrella term that encompasses various techniques, including but not limited to, query understanding, entity retrieval, and result presentation. In this talk, I will give a brief overview on each of these tasks, and further focus on the result presentation aspect. Specifically, I will present methods for generating content for 鈥渆ntity cards鈥濃€�, the informational panels that are presented at the right column of search engine results pages. I will end the talk by introducing a practical toolkit and dataset that are meant to foster research in this area.<img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/seatalk-171008092557-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> 狠狠撸s of Search Engines Amsterdam (SEA) talk
https://www.meetup.com/SEA-Search-Engines-Amsterdam/events/239659928/
Modern search engines have moved from the traditional 鈥渢en blue links鈥� environment towards understanding searchers' intent and providing them with the focused responses; a paradigm that is referred to as 鈥渟emantic search鈥�. Semantic search is an umbrella term that encompasses various techniques, including but not limited to, query understanding, entity retrieval, and result presentation. In this talk, I will give a brief overview on each of these tasks, and further focus on the result presentation aspect. Specifically, I will present methods for generating content for 鈥渆ntity cards鈥濃€�, the informational panels that are presented at the right column of search engine results pages. I will end the talk by introducing a practical toolkit and dataset that are meant to foster research in this area.
]]>
3754https://cdn.slidesharecdn.com/ss_thumbnails/seatalk-171008092557-thumbnail.jpg?width=120&height=120&fit=boundspresentationBlackhttp://activitystrea.ms/schema/1.0/posthttp://activitystrea.ms/schema/1.0/posted0Dynamic Factual Summaries for Entity Cards
/slideshow/sigir2017dynes/78721297
sigir2017-dynes-170810052716 狠狠撸s for SIGIR 2017 paper "Dynamic Factual Summaries for Entity Cards"
Entity cards are being used frequently in modern web search engines to off聜er a concise overview of an entity directly on the results page. These cards are composed of various elements, one of them being the entity summary: a selection of facts describing the entity
from an underlying knowledge base. 聦These summaries, while presenting a synopsis of the entity, can also directly address users鈥� information needs. In this paper, we make the fi聙rst effort towards generating and evaluating such factual summaries. We introduce
and address the novel problem of dynamic entity summarization for entity cards, and break it down to two specific subtasks: fact ranking and summary generation. We perform an extensive evaluation of our method using crowdsourcing. Our results show the
effectiveness of our fact ranking approach and validate that users prefer dynamic summaries over static ones.]]>
狠狠撸s for SIGIR 2017 paper "Dynamic Factual Summaries for Entity Cards"
Entity cards are being used frequently in modern web search engines to off聜er a concise overview of an entity directly on the results page. These cards are composed of various elements, one of them being the entity summary: a selection of facts describing the entity
from an underlying knowledge base. 聦These summaries, while presenting a synopsis of the entity, can also directly address users鈥� information needs. In this paper, we make the fi聙rst effort towards generating and evaluating such factual summaries. We introduce
and address the novel problem of dynamic entity summarization for entity cards, and break it down to two specific subtasks: fact ranking and summary generation. We perform an extensive evaluation of our method using crowdsourcing. Our results show the
effectiveness of our fact ranking approach and validate that users prefer dynamic summaries over static ones.]]>
Thu, 10 Aug 2017 05:27:16 GMT/slideshow/sigir2017dynes/78721297FaeghehHasibi@slideshare.net(FaeghehHasibi)Dynamic Factual Summaries for Entity CardsFaeghehHasibi狠狠撸s for SIGIR 2017 paper "Dynamic Factual Summaries for Entity Cards"
Entity cards are being used frequently in modern web search engines to off鈥歟r a concise overview of an entity directly on the results page. These cards are composed of various elements, one of them being the entity summary: a selection of facts describing the entity
from an underlying knowledge base. 艗These summaries, while presenting a synopsis of the entity, can also directly address users鈥� information needs. In this paper, we make the fi鈧瑀st effort towards generating and evaluating such factual summaries. We introduce
and address the novel problem of dynamic entity summarization for entity cards, and break it down to two specific subtasks: fact ranking and summary generation. We perform an extensive evaluation of our method using crowdsourcing. Our results show the
effectiveness of our fact ranking approach and validate that users prefer dynamic summaries over static ones.<img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sigir2017-dynes-170810052716-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> 狠狠撸s for SIGIR 2017 paper "Dynamic Factual Summaries for Entity Cards"
Entity cards are being used frequently in modern web search engines to off鈥歟r a concise overview of an entity directly on the results page. These cards are composed of various elements, one of them being the entity summary: a selection of facts describing the entity
from an underlying knowledge base. 艗These summaries, while presenting a synopsis of the entity, can also directly address users鈥� information needs. In this paper, we make the fi鈧瑀st effort towards generating and evaluating such factual summaries. We introduce
and address the novel problem of dynamic entity summarization for entity cards, and break it down to two specific subtasks: fact ranking and summary generation. We perform an extensive evaluation of our method using crowdsourcing. Our results show the
effectiveness of our fact ranking approach and validate that users prefer dynamic summaries over static ones.
]]>
8777https://cdn.slidesharecdn.com/ss_thumbnails/sigir2017-dynes-170810052716-thumbnail.jpg?width=120&height=120&fit=boundspresentationBlackhttp://activitystrea.ms/schema/1.0/posthttp://activitystrea.ms/schema/1.0/posted0Entity Linking in Queries: Efficiency vs. Effectiveness
/slideshow/ecir2017erd/74938705
wednesday-session4-hasibi-170412145521 狠狠撸s for the ECIR 2017 paper "Entity Linking in Queries: Efficiency vs. Effectiveness"
Identifying and disambiguating entity references in queries is one of the core enabling components for semantic search. While there is a large body of work on entity linking in documents, entity linking in queries poses new challenges due to the limited context the query provides coupled with the efficiency requirements of an online setting. Our goal is to gain a deeper understanding of how to approach entity linking in queries, with a special focus on how to strike a balance between effectiveness and efficiency. We divide the task of entity linking in queries to two main steps: candidate entity ranking and disambiguation, and explore both unsupervised and supervised alternatives for each step. Our main
finding is that best overall performance (in terms of efficiency and effectiveness) can be achieved by employing supervised learning for the entity ranking step, while tackling disambiguation with a simple unsupervised algorithm. Using the Entity Recognition and Disambiguation Challenge platform, we further demonstrate that our recommended method achieves state-of-the-art performance.]]>
狠狠撸s for the ECIR 2017 paper "Entity Linking in Queries: Efficiency vs. Effectiveness"
Identifying and disambiguating entity references in queries is one of the core enabling components for semantic search. While there is a large body of work on entity linking in documents, entity linking in queries poses new challenges due to the limited context the query provides coupled with the efficiency requirements of an online setting. Our goal is to gain a deeper understanding of how to approach entity linking in queries, with a special focus on how to strike a balance between effectiveness and efficiency. We divide the task of entity linking in queries to two main steps: candidate entity ranking and disambiguation, and explore both unsupervised and supervised alternatives for each step. Our main
finding is that best overall performance (in terms of efficiency and effectiveness) can be achieved by employing supervised learning for the entity ranking step, while tackling disambiguation with a simple unsupervised algorithm. Using the Entity Recognition and Disambiguation Challenge platform, we further demonstrate that our recommended method achieves state-of-the-art performance.]]>
Wed, 12 Apr 2017 14:55:20 GMT/slideshow/ecir2017erd/74938705FaeghehHasibi@slideshare.net(FaeghehHasibi)Entity Linking in Queries: Efficiency vs. EffectivenessFaeghehHasibi狠狠撸s for the ECIR 2017 paper "Entity Linking in Queries: Efficiency vs. Effectiveness"
Identifying and disambiguating entity references in queries is one of the core enabling components for semantic search. While there is a large body of work on entity linking in documents, entity linking in queries poses new challenges due to the limited context the query provides coupled with the efficiency requirements of an online setting. Our goal is to gain a deeper understanding of how to approach entity linking in queries, with a special focus on how to strike a balance between effectiveness and efficiency. We divide the task of entity linking in queries to two main steps: candidate entity ranking and disambiguation, and explore both unsupervised and supervised alternatives for each step. Our main
finding is that best overall performance (in terms of efficiency and effectiveness) can be achieved by employing supervised learning for the entity ranking step, while tackling disambiguation with a simple unsupervised algorithm. Using the Entity Recognition and Disambiguation Challenge platform, we further demonstrate that our recommended method achieves state-of-the-art performance.<img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/wednesday-session4-hasibi-170412145521-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> 狠狠撸s for the ECIR 2017 paper "Entity Linking in Queries: Efficiency vs. Effectiveness"
Identifying and disambiguating entity references in queries is one of the core enabling components for semantic search. While there is a large body of work on entity linking in documents, entity linking in queries poses new challenges due to the limited context the query provides coupled with the efficiency requirements of an online setting. Our goal is to gain a deeper understanding of how to approach entity linking in queries, with a special focus on how to strike a balance between effectiveness and efficiency. We divide the task of entity linking in queries to two main steps: candidate entity ranking and disambiguation, and explore both unsupervised and supervised alternatives for each step. Our main
finding is that best overall performance (in terms of efficiency and effectiveness) can be achieved by employing supervised learning for the entity ranking step, while tackling disambiguation with a simple unsupervised algorithm. Using the Entity Recognition and Disambiguation Challenge platform, we further demonstrate that our recommended method achieves state-of-the-art performance.
]]>
7974https://cdn.slidesharecdn.com/ss_thumbnails/wednesday-session4-hasibi-170412145521-thumbnail.jpg?width=120&height=120&fit=boundspresentationBlackhttp://activitystrea.ms/schema/1.0/posthttp://activitystrea.ms/schema/1.0/posted0Exploiting Entity Linking in Queries For Entity Retrieval
/slideshow/ictir2016-elr/66065744
ictir2016-elr-160915163341 狠狠撸s 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.]]>
狠狠撸s 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.]]>
Thu, 15 Sep 2016 16:33:41 GMT/slideshow/ictir2016-elr/66065744FaeghehHasibi@slideshare.net(FaeghehHasibi)Exploiting Entity Linking in Queries For Entity RetrievalFaeghehHasibi狠狠撸s 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.<img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ictir2016-elr-160915163341-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> 狠狠撸s 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.
]]>
17977https://cdn.slidesharecdn.com/ss_thumbnails/ictir2016-elr-160915163341-thumbnail.jpg?width=120&height=120&fit=boundspresentationBlackhttp://activitystrea.ms/schema/1.0/posthttp://activitystrea.ms/schema/1.0/posted0On the Reproducibility of the TAGME entity linking system
/slideshow/tagmerep/59891025
ecir2016-tagme-160322162330 狠狠撸 for the ECIR '16 paper: 鈥淥n the reproducibility of the TAGME Entity Linking System鈥�
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.]]>
狠狠撸 for the ECIR '16 paper: 鈥淥n the reproducibility of the TAGME Entity Linking System鈥�
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.]]>
Tue, 22 Mar 2016 16:23:30 GMT/slideshow/tagmerep/59891025FaeghehHasibi@slideshare.net(FaeghehHasibi)On the Reproducibility of the TAGME entity linking systemFaeghehHasibi狠狠撸 for the ECIR '16 paper: 鈥淥n the reproducibility of the TAGME Entity Linking System鈥�
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.<img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ecir2016-tagme-160322162330-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> 狠狠撸 for the ECIR '16 paper: 鈥淥n the reproducibility of the TAGME Entity Linking System鈥�
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.
]]>
18327https://cdn.slidesharecdn.com/ss_thumbnails/ecir2016-tagme-160322162330-thumbnail.jpg?width=120&height=120&fit=boundspresentationBlackhttp://activitystrea.ms/schema/1.0/posthttp://activitystrea.ms/schema/1.0/posted0Being a PhD student: Experiences and Challenges
/slideshow/phdguidance/59686945
phd-guidance-160317163537 These slides provide some guidance to the prospective PhD students. The content reflects my personal experiences together with useful feedbacks I received from my colleagues/friends.]]>
These slides provide some guidance to the prospective PhD students. The content reflects my personal experiences together with useful feedbacks I received from my colleagues/friends.]]>
Thu, 17 Mar 2016 16:35:37 GMT/slideshow/phdguidance/59686945FaeghehHasibi@slideshare.net(FaeghehHasibi)Being a PhD student: Experiences and ChallengesFaeghehHasibiThese slides provide some guidance to the prospective PhD students. The content reflects my personal experiences together with useful feedbacks I received from my colleagues/friends.<img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/phd-guidance-160317163537-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> These slides provide some guidance to the prospective PhD students. The content reflects my personal experiences together with useful feedbacks I received from my colleagues/friends.
]]>
36298https://cdn.slidesharecdn.com/ss_thumbnails/phd-guidance-160317163537-thumbnail.jpg?width=120&height=120&fit=boundspresentationBlackhttp://activitystrea.ms/schema/1.0/posthttp://activitystrea.ms/schema/1.0/posted0Entity Linking in Queries: Tasks and Evaluation
/FaeghehHasibi/icitir2015elq
icitir2015-elq-151205013314-lva1-app6891 狠狠撸s for the ICTIR 2015 paper "Entity Linking in Queries: Tasks and Evaluation"
Annotating queries with entities is one of the core problem areas in query understanding. While seeming similar, the task of entity linking in queries is different from entity linking in documents and requires a methodological departure due to the inherent ambiguity of queries. We differentiate between two specific tasks, semantic mapping and interpretation finding, discuss current evaluation methodology, and propose refinements. We examine publicly available datasets for these tasks and introduce a new manually curated dataset for interpretation finding. To further deepen the understanding of task differences, we present a set of approaches for effectively addressing these tasks and report on experimental results.]]>
狠狠撸s for the ICTIR 2015 paper "Entity Linking in Queries: Tasks and Evaluation"
Annotating queries with entities is one of the core problem areas in query understanding. While seeming similar, the task of entity linking in queries is different from entity linking in documents and requires a methodological departure due to the inherent ambiguity of queries. We differentiate between two specific tasks, semantic mapping and interpretation finding, discuss current evaluation methodology, and propose refinements. We examine publicly available datasets for these tasks and introduce a new manually curated dataset for interpretation finding. To further deepen the understanding of task differences, we present a set of approaches for effectively addressing these tasks and report on experimental results.]]>
Sat, 05 Dec 2015 01:33:14 GMT/FaeghehHasibi/icitir2015elqFaeghehHasibi@slideshare.net(FaeghehHasibi)Entity Linking in Queries: Tasks and EvaluationFaeghehHasibi狠狠撸s for the ICTIR 2015 paper "Entity Linking in Queries: Tasks and Evaluation"
Annotating queries with entities is one of the core problem areas in query understanding. While seeming similar, the task of entity linking in queries is different from entity linking in documents and requires a methodological departure due to the inherent ambiguity of queries. We differentiate between two specific tasks, semantic mapping and interpretation finding, discuss current evaluation methodology, and propose refinements. We examine publicly available datasets for these tasks and introduce a new manually curated dataset for interpretation finding. To further deepen the understanding of task differences, we present a set of approaches for effectively addressing these tasks and report on experimental results.<img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/icitir2015-elq-151205013314-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> 狠狠撸s for the ICTIR 2015 paper "Entity Linking in Queries: Tasks and Evaluation"
Annotating queries with entities is one of the core problem areas in query understanding. While seeming similar, the task of entity linking in queries is different from entity linking in documents and requires a methodological departure due to the inherent ambiguity of queries. We differentiate between two specific tasks, semantic mapping and interpretation finding, discuss current evaluation methodology, and propose refinements. We examine publicly available datasets for these tasks and introduce a new manually curated dataset for interpretation finding. To further deepen the understanding of task differences, we present a set of approaches for effectively addressing these tasks and report on experimental results.
]]>
9479https://cdn.slidesharecdn.com/ss_thumbnails/icitir2015-elq-151205013314-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=boundspresentationBlackhttp://activitystrea.ms/schema/1.0/posthttp://activitystrea.ms/schema/1.0/posted0Yalda night
/slideshow/yalda-night/55839243
yaldanight-151205012819-lva1-app6892 Yalda is one of the most celebrated traditional events in Iran which marks the longest night of the year. Every year, Yalda takes happen at 21st of December, which is close to when Christmas is celebrated.
[Description is taken from https://en.wikipedia.org/wiki/Yald%C4%81]]]>
Yalda is one of the most celebrated traditional events in Iran which marks the longest night of the year. Every year, Yalda takes happen at 21st of December, which is close to when Christmas is celebrated.
[Description is taken from https://en.wikipedia.org/wiki/Yald%C4%81]]]>
Sat, 05 Dec 2015 01:28:19 GMT/slideshow/yalda-night/55839243FaeghehHasibi@slideshare.net(FaeghehHasibi)Yalda nightFaeghehHasibiYalda is one of the most celebrated traditional events in Iran which marks the longest night of the year. Every year, Yalda takes happen at 21st of December, which is close to when Christmas is celebrated.
[Description is taken from https://en.wikipedia.org/wiki/Yald%C4%81]<img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/yaldanight-151205012819-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Yalda is one of the most celebrated traditional events in Iran which marks the longest night of the year. Every year, Yalda takes happen at 21st of December, which is close to when Christmas is celebrated.
[Description is taken from https://en.wikipedia.org/wiki/Yald%C4%81]
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38317https://cdn.slidesharecdn.com/ss_thumbnails/yaldanight-151205012819-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=boundspresentationBlackhttp://activitystrea.ms/schema/1.0/posthttp://activitystrea.ms/schema/1.0/posted0https://cdn.slidesharecdn.com/profile-photo-FaeghehHasibi-48x48.jpg?cb=1620402423hasibi.com/https://cdn.slidesharecdn.com/ss_thumbnails/seatalk-171008092557-thumbnail.jpg?width=320&height=320&fit=boundsslideshow/semantic-search-and-result-presentation-with-entity-cards-80573073/80573073Semantic Search and Re...https://cdn.slidesharecdn.com/ss_thumbnails/sigir2017-dynes-170810052716-thumbnail.jpg?width=320&height=320&fit=boundsslideshow/sigir2017dynes/78721297Dynamic Factual Summar...https://cdn.slidesharecdn.com/ss_thumbnails/wednesday-session4-hasibi-170412145521-thumbnail.jpg?width=320&height=320&fit=boundsslideshow/ecir2017erd/74938705Entity Linking in Quer...