[PDF] Top 20 Classification of Epistles using Distant Supervision

... comparable results on tweets with distant supervision. In order to train a classifier, supervised learning usually re- quires hand-labeled training data. With the large range of topics discussed on Twitter, ... See full document

... evidence classification, ...a distant supervision approach, modeling the task as multi-class text ...with distant supervision: (1) noise generated by incorrect heuristic labeling, and ... See full document

... Distant supervision has obtained great progress on relation classification ...of classification, in this paper, we propose RCEND, a novel framework to enhance Relation Classification by ... See full document

... topic classification is time consuming and error prone, especially in an open domain such as the ...whereby distant supervision is used to ob- tain training data: we use Wikipedia arti- cles labelled ... See full document

... Distant supervision is widely used in rela- tion classification in order to create large-scale training data by aligning a knowledge base with an unlabeled ...for distant supervision ... See full document

... compares distant super- vision methods on a large corpus of tweets that can be used to train sentiment ...fied distant supervised ...as distant supervision methods tested as part of a complete ... See full document

... in unconventional style and without accompany- ing metadata, audio/video signals or access to the author for disambiguation, how can we easily pro- duce a gold-standard labelling for training and/or for evaluation and ... See full document

... In Table 3 we report results for all of the models mentioned above. We indicate averages only for our models, since not all approaches deal with the same sets of emotions and we cannot easily compute them. We discuss ... See full document

... ing Wikipedia and Freebase, respectively. As both of the two collaborative web resources have their respective superiorities, i.e., more context infor- mation and more disambiguated entities, we be- gin to study a new ... See full document

... The second related area is sentiment analysis, which we use as our auxiliary task. Previous stud- ies, e.g., Bhatia et al. (2015); Ji and Smith (2017), have shown that sentiment prediction can be en- hanced by leveraging ... See full document

... sentence. Classification of such pairs is often approached by machine learning (Airola et ...on distant supervision and multiple instance learning (Bunescu and Mooney, 2007; Mintz et ...texts ... See full document

... a classification task and use supervised learning on relation-specific train- ing data, which is very expensive to ...issue, distant supervision is proposed to automatically gen- erate training data ... See full document

... Traditional relation extraction predicts rela- tions within some fixed and finite target schema. Machine learning approaches to this task require either manual annotation or, in the case of distant ... See full document

... RNNs using backpropaga- tion through time (Rumelhart et ...state using input, output and forget gates and thereby learn long-term dependencies (Hochreiter and Schmidhuber, ...relation classification ... See full document

... Datasets: Three datasets were created to train MultiR and evaluate performance. The first (Un- filtered) uses the data obtained using distant su- pervision (Section 3.1) without removing any ex- amples ... See full document

... multi-label classification problem with s- parse and noisy ...the classification problem using matrix completion on factorized matrix of minimized ...relation classification as completing the ... See full document

... Datasets: Three datasets were created to train MultiR and evaluate performance. The first (Un- filtered) uses the data obtained using distant su- pervision (Section 3.1) without removing any ex- amples ... See full document

... In the previous two experiments, SMLI had better recall than our local model, but overall improvement was modest. One possible explanation comes from an error propagation problem endemic to this class of models. Consider ... See full document

... However, we improve this performance slightly by multiplying by the similarity between the entity embedding and the average word embedding of the mention itself; this gives us roughly a further 4% accuracy for both ... See full document

... one of 4 dialect labels with the help of crowd sourcing: Egyptian, Gulf, Iraqi and Levantine. A dialect classifier using unigram features is trained from the labeled data. In the BOLT (Broad Oper- ational Language ... See full document