Results 251 to 260 of about 565,012 (288)

TwiCS: Lightweight Entity Mention Detection in Targeted Twitter Streams

IEEE Transactions on Knowledge and Data Engineering, 2021
Microblogging sites, like Twitter, continuously generate a large volume of streaming data. This streaming environment creates new challenges for two concomitant Information Extraction tasks: Entity Mention Detection (EMD) and Entity Detection (ED). The new challenges include (1) continuously evolving topics, which may deprecate model-based approaches ...
Satadisha Saha Bhowmick, Eduard C Dragut, Weiyi Meng   +2 more
openaire   +1 more source

Company Mention Detection for Large Scale Text Mining

Proceedings of the International Conference on Knowledge Discovery and Information Retrieval, 2014
Text mining on a large scale that addresses actionable prediction needs to contend with noisy information in documents, and with interdependencies between the NLP techniques applied and the data representation. This paper presents an initial investigation of the impact of improved company mention detection for financial analytics using Named ...
Boyi Xie, Tifara Ramelson, Rebecca J. Passonneau   +2 more
openaire   +1 more source

Morphology-Based Segmentation Combination for Arabic Mention Detection

ACM Transactions on Asian Language Information Processing, 2009
The Arabic language has a very rich/complex morphology. Each Arabic word is composed of zero or more prefixes , one stem and zero or more suffixes . Consequently, the Arabic data is sparse compared to other languages such as English, and it is necessary to conduct word
Yassine Benajiba, Imed Zitouni
openaire   +1 more source

Detecting Agent Mentions in U.S. Court Decisions

2017
Case law analysis is a significant component of research on almost any legal issue and understanding which agents are involved and mentioned in a decision is integral part of the analysis. In this paper we present a first experiment in detecting mentions of different agents in court decisions automatically.
Šavelka Jaromír, Ashley Kevin D.   +1 more
openaire   +1 more source

A Supervised Approach for Gene Mention Detection

2011
Named Entity Recognition and Classification (NERC) is one of the most fundamental and important tasks in biomedical information extraction. Gene mention detection is concerned with the named entity (NE) extraction of gene and gene product mentions in text.
Sriparna Saha, Asif Ekbal, Sanchita Saha
openaire   +1 more source

Differential evolution based mention detection for anaphora resolution

2013 Annual IEEE India Conference (INDICON), 2013
Mention detection is an important component in anaphora resolution. In this paper we present our works on mention detection based on differential evolution (DE). The proposed technique consists of two steps, viz. feature selection and classifier ensemble.
Utpal Kumar Sikdar, Asif Ekbal, Sriparna Saha   +2 more
openaire   +1 more source

Mention detection in coreference resolution: survey

Applied Intelligence, 2022
Kusum Lata, Pardeep Singh, Kamlesh Dutta
openaire   +1 more source

Detecting and Disambiguating Locations Mentioned in Twitter Messages

2015
Detecting the location entities mentioned in Twitter messages is useful in text mining for business, marketing or defence applications. Therefore, techniques for extracting the location entities from the Twitter textual content are needed. In this work, we approach this task in a similar manner to the Named Entity Recognition (NER) task focused only on
Diana Inkpen, Ji Liu, Atefeh Farzindar, Farzaneh Kazemi, Diman Ghazi   +4 more
openaire   +1 more source

Detecting, categorizing and clustering entity mentions in Chinese text

Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, 2007
The work presented in this paper is motivated by the practical need for content extraction, and the available data source and evaluation benchmark from the ACE program. The Chinese Entity Detection and Recognition (EDR) task is of particular interest to us.
Wenjie Li, Donglei Qian, Qin Lu, Chunfa Yuan   +3 more
openaire   +1 more source

Event mention detection using rough set and semantic similarity

Proceedings of the 1st Amrita ACM-W Celebration on Women in Computing in India, 2010
This paper proposes a method which correctly identifies the sentences that describe an event of interest to extract its participants. It uses rough set based on the attribute, number of sentences in which the term appears and semantic similarity between terms for pruning the terms further to form a list of event trigger.
S. Sangeetha, Michael Arock, R. S. Thakur   +2 more
openaire   +1 more source