[PDF] Top 20 Neural Architectures for Fine grained Entity Type Classification
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Neural Architectures for Fine grained Entity Type Classification
... two-part neural classifier that used a recurrent neural network to obtain a vector representation of each entity men- tion and in its second part used a fixed-size win- dow to capture the context of ... See full document
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HYENA live: Fine Grained Online Entity Type Classification from Natural language Text
... very fine-grained type classification in hierarchical tax- ...multi-level type hierarchy with several hundreds of types at different levels, many entities naturally be- long to multiple ... See full document
6
Description Based Zero shot Fine Grained Entity Typing
... shot entity typing has also been explored, where most of the prior methods adopt the idea of learning a shared se- mantic space for representing the entities as well as the types, but differ in how they construct ... See full document
8
Neural Joint Learning for Classifying Wikipedia Articles into Fine-grained Named Entity Types
... fine- grained NE type labels to Wikipedia ...fine-grained type clas- sification, we introduce a multi-task learning framework where type classifiers are all jointly learned by a ... See full document
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An Attentive Fine Grained Entity Typing Model with Latent Type Representation
... a fine-grained entity typing model with a novel attention mechanism and a hy- brid type ...hybrid classification method be- yond binary relevance to exploit type inter- ... See full document
6
Triple Classification Using Regions and Fine-Grained Entity Typing
... Triple Classification is used to deter- mine the truth value of an unknown ...using fine-grained type ...the type chain and the quality of pre-trained entity- embeddings, and ... See full document
9
Fine Grained Entity Type Classification by Jointly Learning Representations and Label Embeddings
... shown in Figure 5. On Wiki dataset, we observed a large variance in performance as compared to other two datasets. This might be because of the fact that Wiki dataset has a very small develop- ment set. From each of ... See full document
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Improving Fine grained Entity Typing with Entity Linking
... Most of the existing approaches proposed for FET are learning based. The features used by these approaches can either be hand-crafted (Ling and Weld, 2012; Gillick et al., 2014) or learned from neural network ... See full document
6
Generating Fine Grained Open Vocabulary Entity Type Descriptions
... of neural language model that, conditioned on occurrences of words in the input table, gen- erates biographical sentences as ...specific entity rela- tionships, and then, given a new relationship in- ... See full document
12
Fine Grained Control of Sentence Segmentation and Entity Positioning in Neural NLG
... Our random approach could be considered as a very naive baseline as randomly assigned identi- fiers might not even make sense in some cases. For example, having only one single slot in the first sentence (which never ... See full document
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Transforming Wikipedia into a Large Scale Fine Grained Entity Type Corpus
... coarse-grained entity type cor- pus made merly from English Wikipedia articles, and pro- pose WiFiNE, a fine-grained entity type corpus annotated with nominal and ... See full document
8
Embedding Methods for Fine Grained Entity Type Classification
... in fine grained entity type classification is the absence of labeled training ex- ...a fine grained type classification system should be ro- bust to such ... See full document
6
An Attentive Neural Architecture for Fine grained Entity Type Classification
... providing fine-grained entity type predictions as ...that fine-grained labels can be used as fea- tures to improve coarse-grained entity type classi- ... See full document
6
Neural Fine Grained Entity Type Classification with Hierarchy Aware Loss
... of Fine-grained Entity Type Clas- sification (FETC) consists of assigning types from a hierarchy to entity mentions in ...multi-label classification followed by ad-hoc ...for ... See full document
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Neural Architectures for Fine Grained Propaganda Detection in News
... Table 3 shows the scores on dev (internal and ex- ternal) for SLC task. Observe that the pre-trained embeddings (FastText or BERT) outperform TF- IDF vector representation. In row r2, we apply logistic regression ... See full document
6
Fine Grained Entity Typing with High Multiplicity Assignments
... Most prior work has focused on unstructured predictors using some form of multiclass logistic regression (Ling and Weld, 2012; Gillick et al., 2014; Shimaoka et al., 2016; Yaghoobzadeh and Sch¨utze, 2016; Yogatama et ... See full document
5
Fine grained Entity Set Refinement with User Feedback
... Entity set expansion is a well-studied problem with several techniques proposed (Bunescu and Mooney 2004, Etzioni et al. 2005, Wang and Cohen 2007, Sarmento et al. 2007, Pasca 2007, Pasca 2004, Pantel et al. 2009, ... See full document
5
Fine Grained Classification of Named Entities
... Initial results revealed that, due to differing contexts, instances of the same name in a single text would often be classified into different subcategories. To deal with this problem, we augmented the classifier with ... See full document
7
Neural Architectures for Named Entity Recognition
... Named entity recognition (NER) is a challenging learning problem. One the one hand, in most lan- guages and domains, there is only a very small amount of supervised training data available. On the other, there are ... See full document
11
Building a Corpus for Japanese Wikification with Fine Grained Entity Classes
... these entity classes may be dominant in articles, other tasks may require information on natural phenomena, product names, and institu- tion ...limit entity classes, linking mentions to any Wikipedia ... See full document
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