[PDF] Top 20 Multi source named entity typing for social media

... A random subset of 100 entities has been co- annotated by the first author in order to assess inter-agreement rates. Cohen’s kappa agreement scores with respect to each of the target classes are detailed in Table 2. As ... See full document

... Second, we also propose a general modal- ity attention module which chooses per decod- ing step the most informative modality among available ones (in our case, word embeddings, character embeddings, or visual features) ... See full document

... Multimodal Named Entity Disambiguation (MNED) task for multimodal social media posts such as Snapchat or Instagram captions, which are composed of short captions with ac- companying ...images. ... See full document

... Effect of Embeddings We expect improve- ments from embeddings to be larger when there is less training data. Figure 2 shows F1 on dev data for different amounts of training data, from 200 instances up to 1400, for the ... See full document

... Micro-blogs, such as that facilitated by Twitter, have provided a rich source of data for those studying trends and their evolution. Micro-blogs, or “Tweets”, are restricted to 140 characters, and has been likened ... See full document

... in Named Entity Recognition(NER) of re- source rich languages Finkel et ...from social media site Facebook and creating automatic POS Taggers for this corpus, Singh et ...with ... See full document

... Decision Tree algorithm belongs to the family of supervised learning algorithms. Unlike other su- pervised learning algorithms, decision tree algo- rithm can be used for solving regression and clas- sification problems ... See full document

... The study of social media is relatively new. As early as 2005, Park et al. [3] began to analyze the emotion on Twitter. They labeled more than 20,000 tweets and created an emotion polarity dataset by ... See full document

... Named Entity Recognition (NER) aims at iden- tifying different types of entities, such as people names, companies, location, ...Learning Named En- tities (NEs) from social media is a ... See full document

... to social media data due to the great incidence that social networks have in our daily communication (Ritter et ...of named en- tity recognition on noisy ...on named entities from ... See full document

... Named entity recognition (NER) is one of the first and most important steps in Information Extrac- tion ...in social media messages more ... See full document

... Arabic has gained more attention recently due to the increased availability of annotated datasets. Arabic NER systems, as other languages, are do- main dependent and mainly trained on news cor- pora or other well ... See full document

... a typing error in one of the two names because both names differ by only one ...a typing error both names could actually refer to one person named either John or ... See full document

... Named Entity Recognition (NER) is an important tool in almost all Natural Language Processing (NLP) application areas including machine translation, question answering, information retrieval, information ... See full document

... We focus on named entity recognition (NER) for Chinese social media. With massive unlabeled text and quite lim- ited labelled corpus, we propose a semi- supervised learning model based on B- ... See full document

... performance [4]. Ge et al. develop a cost aware route recommendation module in which two cost aware latent factor models are developed to recommend travel packages by considering both the travel cost and the tourist’s ... See full document

... In this paper, we consider the named entity linking (NEL) problem. We assume a set of queries, named entities, that have to be iden- tified within a knowledge base. This know- ledge base is ... See full document

... Previous works have also used combined approaches to im- prove NE annotation. Finkel and Manning describe a strat- egy that applies knowledge acquired from sentence pars- ing to help improve the entity discovery ... See full document

... In addition to the most informative example, we also prefer the most representative example . The representativeness of an example can be evaluated based on how many examples there are similar or near to it. So, the ... See full document

... For future work, it would be interesting to train the LDA model on a larger corpus, to hopefully find a more accurate subject domain context for each tweet or post. It would be useful as well to explore the effects of ... See full document