منابع مشابه
Recognizing Named Entities in Tweets
The challenges of Named Entities Recognition (NER) for tweets lie in the insufficient information in a tweet and the unavailability of training data. We propose to combine a K-Nearest Neighbors (KNN) classifier with a linear Conditional Random Fields (CRF) model under a semi-supervised learning framework to tackle these challenges. The KNN based classifier conducts pre-labeling to collect globa...
متن کاملCroNER: Recognizing Named Entities in Croatian Using Conditional Random Fields
In this paper we present CroNER, a named entity recognition and classification system for Croatian language based on supervised sequence labeling with conditional random fields (CRF). We use a rich set of lexical and gazetteer-based features and different methods for enforcing document-level label consistency. Extensive evaluation shows that our method achieves state-of-the-art results (MUC F1 ...
متن کاملTagging of named entities in Swedish traffic accident reports
A system has been designed to tag named entities (NEs) from text. The relevant domain is traffic accident reports. The texts are written in the Swedish language. The NEs to be tagged are names of roads, streets, city squares, towns and cities. The system makes use of a rules-based approach. Gazetteers are used to find larger cities, morphological rules are applied to individual words, and conte...
متن کاملWikiSense: Supersense Tagging of Wikipedia Named Entities Based WordNet
In this paper, we introduce a minimally supervised method for learning to classify named-entity titles in a given encyclopedia into broad semantic categories in an existing ontology. Our main idea involves using overlapping entries in the encyclopedia and ontology and a small set of 30 handed tagged parenthetic explanations to automatically generate the training data. The proposed method involv...
متن کاملLearning to recognise named entities in tweets by exploiting weakly labelled data
Named entity recognition (NER) in social media (e.g., Twitter) is a challenging task due to the noisy nature of text. As part of our participation in the W-NUT 2016 Named Entity Recognition Shared Task, we proposed an unsupervised learning approach using deep neural networks and leverage a knowledge base (i.e., DBpedia) to bootstrap sparse entity types with weakly labelled data. To further boos...
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ژورنال
عنوان ژورنال: Slovenščina 2.0: empirical, applied and interdisciplinary research
سال: 2017
ISSN: 2335-2736
DOI: 10.4312/slo2.0.2016.1.20-41