Experiments | We now consider the problem of named entity recognition ( NER ) to evaluate how our model performs in a large-scale prediction task. |
Experiments | In traditional NER , the goal is to determine whether each word is a person, organization, location, or not a named entity (‘other’). |
Experiments | For training, we use a large, noisy NER dataset collected by Jenny Finkel. |
Introduction | In experiments on a large, noisy NER dataset, we find that this method can provide an improvement over standard logistic regression when annotation errors are present. |
Experiments | Table 2: Comparison of the performance of event extraction using different NER method. |
Experiments | We experimented with two approaches for named entity recognition ( NER ) in preprocessing. |
Experiments | One is to use the NER tool trained specifically on the Twitter data (Ritter et al., 2011), denoted as “TW-NER” in Table 2. |
Methodology | Named entity recognition ( NER ) is a crucial step since the results would directly impact the final extracted 4-tuple (y,d, l, It is not easy to accurately identify named entities in the Twitter data since tweets contain a lot of misspellings and abbreviations. |
Methodology | First, a traditional NER tool such as the Stanford Named Entity Recognizer2 is used to identify named entities from the news articles crawled from BBC and CNN during the same period that the tweets were published. |