Abstract | Experiments on benchmark datasets show that our approach outperforms previous state-of-the-art systems, with error reductions of 13% to 21% in end-to-end performance. |
Experimental Setup | For end-to-end performance, value F1 is the primary metric. |
Experimental Setup | Comparison Systems We compare our system primarily to HeidelTime (Stro'tgen and Gertz, 2013), which is state of the art in the end-to-end task. |
Formal Overview | We compare to the state-of-the-art systems for end-to-end resolution (Strotgen and Gertz, 2013) and resolution given gold mentions (Bethard, 2013b), both of which do not use any machine learning techniques. |
Introduction | On these benchmark datasets, we present new state-of-the-art results, with error reductions of up to 28% for the detection task and 21% for the end-to-end task. |
Results | End-to-end results Figure 4 shows development and test results for TempEval-3. |
Results | Precision vs. Recall Our probabilistic model of time expression resolution allows us to easily tradeoff precision and recall for end-to-end performance by varying the resolution probability threshold. |
Results | We also manually categorized all resolution errors for end-to-end performance with 10-fold cross validation of the TempEval-3 Dev dataset, |
Abstract | Experiments on Automatic Content Extraction (ACE)1 corpora demonstrate that our joint model significantly outperforms a strong pipelined baseline, which attains better performance than the best-reported end-to-end system. |
Background | Most previous research on relation extraction assumed that entity mentions were given In this work we aim to address the problem of end-to-end entity mention and relation extraction from raw texts. |
Conclusions and Future Work | In this paper we introduced a new architecture for more powerful end-to-end entity mention and relation extraction. |
Experiments | Furthermore, we combine these two criteria to evaluate the performance of end-to-end entity mention and relation extraction. |
Experiments | The human F1 score on end-to-end relation extraction is only about 70%, which indicates it is a very challenging task. |
Experiments | For end-to-end entity mention and relation extraction, both the joint approach and the pipelined baseline outperform the best results reported by (Chan and Roth, 2011) under the same setting. |
Introduction | The goal of end-to-end entity mention and relation extraction is to discover relational structures of entity mentions from unstructured texts. |
Related Work | We extended the similar idea to our end-to-end task by incrementally predicting relations along with entity mention segments. |
Abstract | In this paper, we propose a novel recursive recurrent neural network (RZNN) to model the end-to-end decoding process for statistical machine translation. |
Introduction | Different from the work mentioned above, which applies DNN to components of conventional SMT framework, in this paper, we propose a novel RZNN to model the end-to-end decoding process. |
Our Model | In this section, we leverage DNN to model the end-to-end SMT decoding process, using a novel recursive recurrent neural network (RZNN), which is different from the above mentioned work applying DNN to components of conventional SMT framework. |
Our Model | Our R2NN is used to model the end-to-end translation process, with recurrent global information added. |
Related Work | Unfortunately, the better word alignment result generated by this model, cannot bring significant performance improvement on a end-to-end SMT evaluation task. |
Conclusions | 5 We currently do not evaluate the end-to-end system over different corpora. |
Conclusions | Table 4: Extrinsic evaluation, where we plugged the two merging models into an end-to-end feedback detection system by Swanson and Yamangil. |
Correction Detection | In comparison, phrase extraction systems aim to improve the end-to-end MT or paraphrasing systems. |
Experimental Setup | In addition to evaluating the merging algorithms on the standalone task of correction detection, we have also plugged in the merging algorithms into an end-to-end system in which every automatically detected correction is further classified into an error type. |
Introduction | Our model is also the first to directly learn relational patterns as part of the process of training an end-to-end taxonomic induction system, rather than using patterns that were hand-selected or learned via pairwise classifiers on manually annotated co-occurrence patterns. |
Introduction | Finally, it is the first end-to-end (i.e., non-incremental) system to include sibling (e.g., coordination) patterns at all. |
Related Work | Our model also automatically learns relational patterns as a part of the taxonomic training phase, instead of relying on handpicked rules or pairwise classifiers on manually annotated co-occurrence patterns, and it is the first end-to-end (i.e., non-incremental) system to include heterogeneous relational information via sibling (e.g., coordination) patterns. |
Experiments | The relatively low performance of the baseline system TagMe demonstrates that only relying on prior popularity and topical information within a single tweet is not enough for an end-to-end wikification system for the short tweets. |
Experiments | However, when ,u 2 0.4, the system performance dramatically decreases, showing that prior popularity is not enough for an end-to-end wikification system. |
Introduction | An end-to-end wikification system needs to solve two sub-problems: (i) concept mention detection, (ii) concept mention disambiguation. |
Abstract | We evaluate our proposed method on two end-to-end SMT tasks (phrase table pruning and decoding with phrasal semantic similarities) which need to measure semantic similarity between a source phrase and its translation candidates. |
Conclusions and Future Work | Two end-to-end SMT tasks are involved to test the power of the proposed model at learning the semantic phrase embeddings. |
Introduction | Accordingly, we evaluate the BRAE model on two end-to-end SMT tasks (phrase table pruning and decoding with phrasal semantic similarities) which need to check whether a translation candidate and the source phrase are in the same meaning. |