@Proceedings{proc_ce_challenge@msm2013@www2013,
  title = {Proceedings, Concept Extraction Challenge at the 3rd Workshop on Making Sense of Microposts {(\#MSM2013)}: Big things come in small packages, Rio de Janeiro, Brazil, 13 May 2013},
  year = 2013,
  booktitle = {Making Sense of Microposts {(\#MSM2013)} Concept Extraction Challenge},
  editor = {Amparo E. Cano Basave and Matthew Rowe and Milan Stankovic and Aba-Sah Dadzie},
  month = {May},
  url = {http://ceur-ws.org/Vol-1019},
}

@InProceedings{cano.ea:2013,
  author = {Amparo Elizabeth Cano Basave and Andrea Varga and Matthew Rowe and Milan Stankovic and Aba-Sah Dadzie},
  title = {Making Sense of Microposts ({\#MSM2013}) Concept Extraction Challenge},
  crossref = {proc_ce_challenge@msm2013@www2013},
  pages = {1--15},
  booktitle = {Making Sense of Microposts {(\#MSM2013)} Concept Extraction Challenge},
  year = 2013,
  abstract = {Microposts are small fragments of social media content that have been published using a lightweight paradigm (e.g. Tweets, Facebook likes, foursquare check-ins). Microposts have been used for a variety of applications (e.g., sentiment analysis, opinion mining, trend analysis), by gleaning useful information, often using third-party concept extraction tools. There has been very large uptake of such tools in the last few years, along with the creation and adoption of new methods for concept extraction. However, the evaluation of such efforts has been largely consigned to document corpora (e.g. news articles), questioning the suitability of concept extraction tools and methods for Micropost data. This report describes the Making Sense of Microposts Workshop (\#MSM2013) Concept Extraction Challenge, hosted in conjunction with the 2013 World Wide Web conference (WWW'13). The Challenge dataset comprised a manually annotated training corpus of Microposts and an unlabelled test corpus. Participants were set the task of engineering a concept extraction system for a defined set of concepts. Out of a total of 22 complete submissions 13 were accepted for presentation at the workshop; the submissions covered methods ranging from sequence mining algorithms for attribute extraction to part of speech tagging for Micropost cleaning and rule-based and discriminative models for token classification. In this report we describe the evaluation process and explain the performance of different approaches in different contexts. 
},
  keywords = {Microposts, Evaluation, Extraction},
  url = {http://ceur-ws.org/Vol-1019/msm2013-challenge-report.pdf},
}

@InProceedings{habib.ea:2013,
  author = {Mena Habib and Maurice Van Keulen and Zhemin Zhu},
  title = {Concept Extraction Challenge: {U}niversity of {T}wente at {\#MSM2013}},
  crossref = {proc_ce_challenge@msm2013@www2013},
  pages = {17--20},
  booktitle = {Making Sense of Microposts {(\#MSM2013)} Concept Extraction Challenge},
  year = 2013,
  abstract = {Twitter messages are a potentially rich source of continuously and instantly updated information. Shortness and informality of such messages are challenges for Natural Language Processing tasks. In this paper we present a hybrid approach for Named Entity Extraction (NEE) and Classification (NEC) for tweets. The system uses the power of the Conditional Random Fields (CRF) and the Support Vector Machines (SVM) in a hybrid way to achieve better results. For named entity type classification we use AIDA disambiguation system to disambiguate the extracted named entities and hence find their type.},
  keywords = {Named Entity Extraction, Named Entity Classification, Social Media Analysis, Twitter Messages},
  url = {http://ceur-ws.org/Vol-1019/paper_14.pdf},
}

@InProceedings{dlugolinsky.ea:2013,
  author = {\v{S}tefan Dlugolinsk\'y and Peter Krammer and Marek Ciglan and Michal Laclav\'ik},
  title = {{MSM2013} {IE} {C}hallenge: {A}nnotowatch},
  crossref = {proc_ce_challenge@msm2013@www2013},
  pages = {21--26},
  booktitle = {Making Sense of Microposts {(\#MSM2013)} Concept Extraction Challenge},
  year = 2013,
  abstract = {Twitter messages are a potentially rich source of continuously and instantly updated information. Shortness and informality of such messages are challenges for Natural Language Processing tasks. In this paper we present a hybrid approach for Named Entity Extraction (NEE) and Classification (NEC) for tweets. The system uses the power of the Conditional Random Fields (CRF) and the Support Vector Machines (SVM) in a hybrid way to achieve better results. For named entity type classification we use AIDA \cite{YosefHBSW11} disambiguation system to disambiguate the extracted named entities and hence find their type. 
},
  keywords = {Information extraction, Machine Learning, Named Entity Recognition, Microposts},
  url = {http://ceur-ws.org/Vol-1019/paper_21.pdf},
}

@InProceedings{vanErp.ea:2013,
  author = {Marieke Van Erp and Giuseppe Rizzo and Rapha\"el Troncy},
  title = {Learning with the Web: Spotting Named Entities on the Intersection of {NERD} and Machine Learning},
  crossref = {proc_ce_challenge@msm2013@www2013},
  pages = {27--30},
  booktitle = {Making Sense of Microposts {(\#MSM2013)} Concept Extraction Challenge},
  year = 2013,
  abstract = {Microposts shared on social platforms instantaneously report facts, opinions or emotions. In these posts, entities are often used but they are continuously changing depending on what is currently trending. In such a scenario, recognising these named entities is a challenging task, for which off-the-shelf approaches are not well equipped. We propose NERD-ML, an approach that unifies the benefits of a crowd entity recognizer through Web entity extractors combined with the linguistic strengths of a machine learning classifier.
},
  keywords = {Named Entity Recognition, NERD, Machine Learning},
  url = {http://ceur-ws.org/Vol-1019/paper_15.pdf},
}

@InProceedings{cortis:2013,
  author = {Keith Cortis},
  title = {{ACE}: A Concept Extraction Approach using Linked Open Data},
  crossref = {proc_ce_challenge@msm2013@www2013},
  pages = {31--35},
  booktitle = {Making Sense of Microposts {(\#MSM2013)} Concept Extraction Challenge},
  year = 2013,
  abstract = {Given the increase in popularity of several social networks, numerous users tend to express themselves or reach out to their followers via online posts, normally in the form of microposts. Dealing with such data of short textual content can be quite intricate due to several factors such as misspellings, slang, emoticons, etc. In this paper we present an approach towards extracting several concepts from microposts, where the main challenge is to classify them into specific entity types. This will help in discovering knowledge from possible semi-structured/unstructured data after taking into account several factors. In our approach we extend a state-of-the-art information extraction system which we call ACE, and make use of a dataset that is part of the Linked Open Data cloud, in order to improve the named entity extraction process. 
},
  keywords = {Microposts, Natural Language Processing, Named Entity Recognition, Linked Open Data},
  url = {http://ceur-ws.org/Vol-1019/paper_20.pdf},
}

@InProceedings{godin.ea:2013,
  author = {Fr\'ederic Godin and Pedro Debevere and Erik Mannens and Wesley De Neve and Rik Van de Walle},
  title = {Leveraging Existing Tools for Named Entity Recognition in Microposts},
  crossref = {proc_ce_challenge@msm2013@www2013},
  pages = {36--39},
  booktitle = {Making Sense of Microposts {(\#MSM2013)} Concept Extraction Challenge},
  year = 2013,
  abstract = {With the increasing popularity of microblogging services, new research challenges arise in the area of text processing. In this paper, we hypothesize that already existing services for Named Entity Recognition (NER), or a combination thereof, perform well on microposts, despite the fact that these NER services have been developed for processing long-form text documents that are well-structured and well-spelled. We test our hypothesis by applying four already existing NER services to the set of microposts of the MSM2013 IE Challenge.},
  keywords = {Microposts, NER, Text Processing},
  url = {http://ceur-ws.org/Vol-1019/paper_25.pdf},
}

@InProceedings{bosch.ea:2013,
  author = {Antal van Den Bosch and Toine Bogers},
  title = {Memory-based Named Entity Recognition in Tweets},
  crossref = {proc_ce_challenge@msm2013@www2013},
  pages = {40--43},
  booktitle = {Making Sense of Microposts {(\#MSM2013)} Concept Extraction Challenge},
  year = 2013,
  abstract = {We present a memory-based named entity recognition system that participated in the MSM-2013 Concept Extraction Challenge. The system expands the training set of annotated tweets with part-of-speech tags and seedlist information, and then generates a sequential memory-based tagger comprised of separate modules for known and unknown words. Two taggers are trained: one on the original capitalized data, and one on a lowercased version of the training data. The intersection of named entities in the predictions of the two taggers is kept as the final output.},
  keywords = {Named Entity Recognition, Memory-based Tagging},
  url = {http://ceur-ws.org/Vol-1019/paper_03.pdf},
}

@InProceedings{munozGarcia.ea:2013,
  author = {\'Oscar Mu\~noz-Garc\'ia and Andr\'es Garc\'ia-Silva and \'Oscar Corcho},
  title = {Towards Concept Identification using a Knowledge-Intensive Approach},
  crossref = {proc_ce_challenge@msm2013@www2013},
  pages = {45--49},
  booktitle = {Making Sense of Microposts {(\#MSM2013)} Concept Extraction Challenge},
  year = 2013,
  abstract = {This paper presents a method for identifying concepts in microposts and classifying them into a predefined set of categories. The method relies on the DBpedia knowledge base to identify the types of the concepts detected in the messages. For those concepts that are not classified in the ontology we infer their types via the ontology properties which characterise the type.
},
  keywords = {Concept Identification, Microposts, DBpedia},
  url = {http://ceur-ws.org/Vol-1019/paper_29.pdf},
}

@InProceedings{genc.ea:2013,
  author = {Yegin Genc and Winter Mason and Jeffrey V. Nickerson},
  title = {Classifying Short Messages using Collaborative Knowledge Bases: Reading Wikipedia to Understand Twitter},
  crossref = {proc_ce_challenge@msm2013@www2013},
  pages = {50--53},
  booktitle = {Making Sense of Microposts {(\#MSM2013)} Concept Extraction Challenge},
  year = 2013,
  abstract = {Extracting concepts from text requires noticing information that fits into any of the desired categories and ignore the others. Tweets, short and often cryptic text messages, provide little context for the task. To this end, we apply semantic transforms using Collaborative Knowledge Bases - namely Wikipedia: specifically, we map text to Wikipedia ontology by extracting and disambiguating entities, and finally classifying them by categories they belong to.},
  keywords = {Concept Extraction, Semantic Transforms, Collaborative Knowledge Bases},
  url = {http://ceur-ws.org/Vol-1019/paper_28.pdf},
}

@InProceedings{jadidinejad:2013,
  author = {Amir Hossein Jadidinejad},
  title = {Unsupervised Information Extraction using {BabelNet} and {DBpedia}},
  crossref = {proc_ce_challenge@msm2013@www2013},
  pages = {54--56},
  booktitle = {Making Sense of Microposts {(\#MSM2013)} Concept Extraction Challenge},
  year = 2013,
  abstract = {Using linked data in real world applications is a hot topic in the field of Information Retrieval. In this paper we leveraged two valuable knowledge bases in the task of information extraction. BabelNet is used to automatically recognize and disambiguate concepts in a piece of unstructured text. After extracting all possible concepts, DBpedia is leveraged to reason about the type of each concept using SPARQL.},
  keywords = {Concept Extraction, Linked Data, BabelNet, DBpedia, SPARQL},
  url = {http://ceur-ws.org/Vol-1019/paper_32.pdf},
}

@InProceedings{mendes.ea:2013,
  author = {Pablo Mendes and Dirk Weissenborn and Chris Hokamp},
  title = {{DBpedia} {S}potlight at the {MSM2013} Challenge},
  crossref = {proc_ce_challenge@msm2013@www2013},
  pages = {57--61},
  booktitle = {Making Sense of Microposts {(\#MSM2013)} Concept Extraction Challenge},
  year = 2013,
  abstract = {DBpedia Spotlight is a tool for performing semantic annotation of entities and concepts in text. Although it does not focus on Named Entities, it is able to perform Named Entity Recognition and Disambiguation, relying on a number of different techniques to perform each step. We have tried our dictionary-based as well as our probabilistic sequence labeler implementations and we report our results with the MSM2013 Challenge datasets.},
  keywords = {Linked Data, Named Entity Recognition, Information Extraction},
  url = {http://ceur-ws.org/Vol-1019/paper_30.pdf},
}

@InProceedings{das.ea:2013,
  author = {Amitava Das and Utsab Burman and Balamurali Ar and Sivaji Bandyopadhyay},
  title = {{NER} from Tweets: {SRI-JU} System {@MSM} 2013},
  crossref = {proc_ce_challenge@msm2013@www2013},
  pages = {62--66},
  booktitle = {Making Sense of Microposts {(\#MSM2013)} Concept Extraction Challenge},
  year = 2013,
  abstract = {Now a day Twitter has become an interesting source of experiment for different NLP experiments like entity extraction, user opinion analysis and more. Due to the noisy nature of user generated content it is hard to run standard NLP tools to obtain a better result. The task of named entity extraction from tweets is one of them. Traditional NER approaches on tweets do not perform well. Tweets are usually informal in nature and short (up to 140 characters). They often contain grammatical errors, misspellings, and unreliable capitalization. These unreliable linguistic features cause traditional methods to perform poorly on tweets. This article reports the author's participation in the Concept Extraction Challenge, Making Sense of micro posts (#MSM2013). Three different systems runs have been submitted. The first run is the baseline, second run is with capitalization and syntactic feature and the last run is with dictionary features. The last run yielded than all other. The accuracy of the final run has been checked is 79.57 (precision), 71.00 (recall) and 74.79 (f-measure) respectively.},
  keywords = {Concept Extraction, Microposts, NER},
  url = {http://ceur-ws.org/Vol-1019/paper_33.pdf},
}


@InProceedings{sachidanandan.ea:2013,
  author = {Sandhya Sachidanandan and Prathyush Sambaturu and Kamalakar Karlapalem},
  title = {{NERTUW}: Named Entity Recognition on Tweets using Wikipedia},
  crossref = {proc_ce_challenge@msm2013@www2013},
  pages = {67--70},
  booktitle = {Making Sense of Microposts {(\#MSM2013)} Concept Extraction Challenge},
  year = 2013,
  abstract = {We propose an approach to recognize named entities in \\tweets, disambiguate and classify them into four categories namely person, organization, location and miscellaneous using Wikipedia. Our approach annotates the tweets on the fly, ie, it does not require any training data.},
  keywords = {Named Entity Recognition, Entity Disambiguation, Entity Classification},
  url = {http://ceur-ws.org/Vol-1019/paper_35.pdf},
}

@InProceedings{deOliveira.ea:2013,
  author = {Diego de Oliveira and Alberto Laender and Adiano Veloso and Altigran Da Silva},
  title = {Filter-Stream Named Entity Recognition: A Case Study at the {\#MSM2013} Concept Extraction Challenge},
  crossref = {proc_ce_challenge@msm2013@www2013},
  pages = {71--75},
  booktitle = {Making Sense of Microposts {(\#MSM2013)} Concept Extraction Challenge},
  year = 2013,
  abstract = {Microblog platforms such as Twitter are being increasingly adopted by Web users, yielding an important source of data for web search and mining applications. Tasks such as Named Entity Recognition are at the core of many of these applications, but the effectiveness of existing tools is seriously compromised when applied to Twitter data, since messages are terse, poorly worded and posted in many different languages. In this paper, we briefly describe a novel NER approach, called FS-NER (Filter Stream Named Entity Recognition) to deal with Twitter data, and present the results of a preliminary performance evaluation conducted to assess it in the context of the Concept Extraction Challenge proposed by the 2013 Workshop on Making Sense of Microposts - MSM2013. FS-NER is characterized by the use of filters that process unlabeled Twitter messages, being much more practical than existing supervised CRF-based approaches. Such filters can be combined either in sequence or in parallel in a flexible way. Our results show that, despite the simplicity of the filters used, our approach outperformed the baseline with improvements of 4.9\% on average, while being much faster.},
  keywords = {Twitter, Named Entity Recognition, FS-NER, CRF},
  url = {http://ceur-ws.org/Vol-1019/paper_34.pdf},
}
