SlowGuess/ACL_2020_OCR huggingface dataset:
Considering that the upper bounds of  $\epsilon_T(h)$ $(\epsilon_T(h_1))$ $\epsilon_{\alpha}(h)$ $(\epsilon_{\alpha}(h^{*}))$  and  $\lambda (\lambda^{*})$  are small when training converges, our goal is to reduce  $d_{\mathcal{H}\Delta \mathcal{H}}(\mathcal{D}_S,\mathcal{D}_T)$ . In particular, we define a model  $h$  is a composition function  $h = g\circ f$ , where  $f$  represents the multi-cell LSTM model and  $g$  represents the CRF layer,  $\circ$  denotes function composition. We assume  $h^\prime$  and  $h''$  share the same multi-cell LSTM model, namely  $h^{\prime} = g^{\prime}\circ f$  and  $h^{\prime \prime} = g^{\prime \prime}\circ f$ , we have
$$
\begin{array}{l} d _ {\mathcal {H} \Delta \mathcal {H}} (\mathcal {D} _ {S}, \mathcal {D} _ {T}) = 2 \sup  _ {g ^ {\prime}, g ^ {\prime \prime} \in \mathcal {G}} \left| \Pr_ {x \sim \mathcal {D} _ {S}} \left[ g ^ {\prime} \circ f (x) \neq g ^ {\prime \prime} \circ f (x) \right] \right. \\ - \operatorname * {P r} _ {x \sim \mathcal {D} _ {T}} \left[ g ^ {\prime} \circ f (x) \neq g ^ {\prime \prime} \circ f (x) \right] \\ \end{array}
$$
To obtain the supremum of the right term, we may wish to assume that both  $g'$  and  $g''$  can classify correctly in the source domain, then
$$
d _ {\mathcal {H} \triangle \mathcal {H}} (\mathcal {D} _ {S}, \mathcal {D} _ {T}) \approx 2 \sup  _ {g ^ {\prime}, g ^ {\prime \prime} \in \mathcal {G}} \left| \Pr_ {x \sim \mathcal {D} _ {T}} \left[ g ^ {\prime} \circ f (x) \neq g ^ {\prime \prime} \circ f (x) \right] \right|
$$
The optimization objective is as follows:
$$
\min  _ {f \in \mathcal {F}} \sup  _ {g ^ {\prime}, g ^ {\prime \prime} \in \mathcal {G}} \left| \operatorname * {P r} _ {x \sim \mathcal {D} _ {T}} \left[ g ^ {\prime} \circ f (x) \neq g ^ {\prime \prime} \circ f (x) \right] \right|
$$
Aiming to  $\min_{f\in \mathcal{F}}d_{\mathcal{H}\Delta \mathcal{H}}(\mathcal{D}_S,\mathcal{D}_T)$ , we decompose the unified feature space into several entity typed distributions using multi-cell LSTM, resulting in that source- and target-domain features belonging to the same entity type are clustered together. The proof is mainly based on the cluster assumption (Chapelle and Zien, 2005), which is equivalent to the low density separation assumption, states that the decision boundary should lie on a low-density region. According to the cluster assumption, both  $g^{\prime}$  and  $g''$  tend to cross the low-density regions in the shared
$l$  is the total number of entity types in the source and target domains, such as  $\{\mathrm{O},\mathrm{PER},\mathrm{LOC},\mathrm{ORG},\mathrm{MISC}\}$ . Our discussion also makes sense in the case that source domain and target domain have different entity types.
Dataset	Entity Type	Size	Train	Dev	Test
CoNLL-2003	PER, LOC	#Sentence	15.0K	3.5K	3.7K
ORG, MISC	#Entity	23.5K	5.9K	5.6K
Broad Twitter	PER, LOC	#Sentence	6.3K	1.0K	2.0K
ORG	#Entity	8.8K	1.7K	4.4K
Twitter	PER, LOC	#Sentence	4.3K	1.4K	1.5K
ORG, MISC	#Entity	7.5K	2.5K	2.5K
BioNLP13PC	CHEM, CC	#Sentence	2.5K	0.9K	1.7K
GGP, etc.	#Entity	7.9K	2.7K	5.3K
BioNLP13CG	CHEM, CC	#Sentence	3.0K	1.0K	1.9K
GGP, etc.	#Entity	10.8K	3.6K	6.9K
CBS News	PER, LOC	#Sentence	-	-	2.0K
ORG, MISC	#Entity	-	-	3.4K
Table 1: Statistic of datasets.
feature space of both source and target domains. This results in  $\operatorname{Pr}_{x\sim \mathcal{D}_T}[g'\circ f(x)\neq g''\circ f(x)]\approx$ $\operatorname{Pr}_{x\sim \mathcal{D}_S}[g'\circ f(x)\neq g''\circ f(x)]\approx 0$  , which well meets the above optimization objective.
Datasets. We take six publicly available datasets for experiments, including BioNLP13PC and BioNLP13CG (Nédellec et al., 2013), CoNLL-2003 English dataset (Sang and Meulder, 2003), Broad Twitter dataset (Derczynski et al., 2016), Twitter dataset (Lu et al., 2018) and CBS SciTech News dataset (Jia et al., 2019). Statistics of the datasets are shown in Table 1. In unsupervised domain adaptation experiments, 398,990 unlabeled sentences from CBS SciTech News collected by Jia et al. (2019) are used for target-domain LM training, a named entity dictionary from Web resource collected by Peng et al. (2019) is used for target-domain auxiliary tasks training.
The CoNLL-2003, Twitter and CBS News have the same four types of entities, namely PER (person), LOC (location), ORG (organization) and Misc (miscellaneous). The Broad Twitter dataset consists of three types: PER, LOC and ORG. BioNLP13CG mainly consists of five types, namely CHEM (simple chemical), CC (cellular component), GGP (gene and gene product), SPE (species) and CELL (cell), BioNLP13PC mainly consists of three types: CHEM, CC and GGP.
Hyperparameters. We choose NCRF++ (Yang and Zhang, 2018) for developing the models. The multi-task baselines are based on Jia et al. (2019). Our hyperparameter settings largely follow Yang et al. (2018); word embeddings for all models are initialized with PubMed 200-dimension vectors (Chiu et al., 2016) in BioNLP experiments and
Figure 2: Performances of the main NER and auxiliary tasks against the total number of training iterators.
GloVe 100-dimension vectors (Pennington et al., 2014) in other experiments. All word embeddings are fine-tuned during training. Character embeddings are randomly initialized.
4.2 Development Experiments
Figure 2 shows the performances of the main target-domain NER task and the auxiliary entity prediction and attention scoring tasks on the development sets of BioNLP13CG and Twitter when the number of training iterations increases. As can be seen from the figure, all the three tasks have the same trend of improvement without potential conflicts between tasks, which shows that all the three tasks take the feature space of the same form.
4.3 Supervised Domain Adaptation
We conduct supervised domain adaptation on BioNLP dataset, Broad Twitter dataset and Twitter dataset, respectively. In particular, for the BioNLP dataset, BioNLP13CG is used as the target-domain NER dataset and BioNLP13PC as the source-domain dataset. These two datasets have some different entity types. In the Broad Twitter dataset, Broad Twitter is used as the target-domain dataset and the CoNLL-2003 as the source-domain dataset. These two datasets have a different entity type MISC. In the Twitter dataset, Twitter is used as the target-domain dataset and the CoNLL-2003 as the source-domain dataset. These two datasets have the same entity types. The overall results are listed in Table 2.
Target-domain only settings. In comparison with target-domain only models BiLSTM and MULTI-
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Methods	Datasets
BioNLP	Broad Twitter	Twitter
F1	#Params	F1	#Params	F1	#Params
Crichton et al. (2017)	78.90	-	-	-	-	-
Lu et al. (2018)	-	-	-	-	80.75	-
Wang et al. (2019)	82.48	-	-	-	-	-
Jia et al. (2019)	79.86	-	-	-	-	-
BILSTM+ELMO (Peters et al., 2018)	-	-	76.48	94,590K	82.83	94,631K
BILSTM+BIOELMO (Peters et al., 2018)	85.61	94,605K	-	-	-	-
BERT-BASE (Devlin et al., 2019)	-	-	77.28	108M	83.77	108M
BIOBERT-BASE (Lee et al., 2020)	85.72	108M	-	-	-	-
BILSTM	79.24	304K	72.98	210K	77.18	211K
MULTI-CELL LSTM	78.76	2,704K	72.54	641K	77.05	743K
MULTI-TASK (LSTM)	81.06	309K	73.84	214K	79.55	215K
MULTI-TASK (LSTM)[REPRO]*	81.45	312K	73.82	214K	79.90	215K
MULTI-TASK+PGN	81.17	4,533K	73.70	3,238K	80.07	3,239K
MULTI-TASK+GRAD	81.63	447K	74.12	342K	79.72	344K
OURS	83.12†	2,929K	74.82†	827K	81.37†	828K
OURS+ELMO/BIOELMO	86.65	105M	76.36	97,090K	84.31	97,091K
OURS+BERT-BASE/BIOBERT-BASE	86.96†‡	117M	78.43†‡	111M	85.80†‡	111M
CELL LSTM, all of the multi-task models obtain significantly better results on all of the three datasets. This shows the effectiveness of multi-task learning in few-shot transfer.
Cross-domain settings. We make comparisons with the traditional parameter sharing mechanism MULTI-TASK(LSTM) (Yang et al., 2017) together with two improved methods, MULTI-TASK+PGN (Jia et al., 2019), which adds an parameter generation networks (PGN) to generate parameters for source- and target-domain LSTMs and MULTI-TASK+GRAD (Zhou et al., 2019), which adds a generalized resource-adversarial discriminator (GRAD) and leverages adversarial training. The results show that our method can significantly outperform these multi-task methods on the same datasets, which shows the effectiveness of our multi-cell structure in cross-domain settings.
Comparison with the state-of-the-art models. Results show that our model outperforms cross-domain method of Jia et al. (2019), cross-type method of Wang et al. (2019) and methods using addition features (Crichton et al., 2017; Lu et al., 2018). Recently, LM pre-training based methods such as ELMO/BIOELMO (Peters et al., 2018), BERT (Devlin et al., 2019) and BIOBERT (Lee et al., 2020) achieve state-of-the-art results on NER. However, these methods use additional large-scale language resources, thus it is unfair to make direct comparisons with our method. Thus we leverage the outputs of LM pre-training meth
Table 2: Results on three few-shot datasets. * indicates that we reproduce the baseline bi-directional LSTM in a similar way to our model for fair comparisons. † indicates statistical significance compared to target-domain settings and cross-domain settings with  $p < 0.01$  by t-test. ‡ indicates statistical significance compared to LM pre-training based methods with  $p < 0.01$  by t-test.
Methods	F1	#Params	#Raw
Jia et al. (2019)	73.59	12,916K	18,474K
BERT-BASE (Devlin et al., 2019)	74.23	108M	3,700M
BILSTM	70.73	211K	-
MULTI-CELL LSTM	70.03	743K	-
BILSTM+LM	71.30	211K	1,931K
BILSTM+LM+DICT	72.49	212K	1,931K
MULTI-CELL LSTM+LM	72.81	743K	1,931K
MULTI-CELL LSTM+LM(ALL)	73.56	743K	8,664K
MULTI-CELL LSTM+LM+DICT	75.19†	743K	1,931K
Table 3: Results on CBS News datasets. #Raw indicates number of words in raw data used in the experiment.  $\dagger$  indicates statistical significance compared with all of the baselines with  $p <   0.01$  by t-test.
ods as contextualized word embeddings. In particular, we use the same batch size as our method and the Adam optimizer with an initial learning rate 3e-5 in BERT fine-tuning baselines. Results show that our method benefits from these LM pretraining output features and outperforms these LM pre-training based methods.
4.4 Unsupervised Domain Adaptation
We conduct unsupervised domain adaptation on the CBS SciTech News test set, using CoNLL-2003 as the source-domain dataset. The overall results are listed in Table 3.
Adding target-domain LM training. Only using the source-domain NER data, BILSTM and MULTICELL LSTM give comparable results,  $70.73\%$ $\mathrm{F}_1$  and  $70.03\%$ $\mathrm{F}_1$ , respectively. In comparison with the source-domain only models, all of the models
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Figure 3: t-SNE visualization of ET cell states  $\{\mathbf{c}_k\}_{k = 1}^l$  on the CoNLL-2003 test set and Broad Twitter test set, differentiated by signal star and dot, respectively. Different entity types are represented by different colours.
using LM obtain significantly better results, which shows the effectiveness of using target-domain LM in zero-shot transfer. When using the same amount of target-domain raw data as Jia et al. (2019), The result of MULTI-CELL LSTM+LM(ALL) is comparable to the state-of-the-art (Jia et al., 2019)  $(73.56\% \mathrm{F}_1$  v.s.  $73.59\% \mathrm{F}_1)$ , which uses both source-domain LM and target-domain LM. This shows the effectiveness of multi-cell structure for zero-shot transfer.
Adding a named entity dictionary. With the named entity dictionary collected by Peng et al. (2019), the results show a significant improvement  $(75.19\% \mathrm{F}_1$  v.s.  $72.81\% \mathrm{F}_1)$ . To make fair comparison, we add the entity dictionary information to BiLSTM+LM by doing an entity type prediction task together with the target-domain LM. BiLSTM+LM+DICT achieves better result than BiLSTM+LM  $(72.49\% \mathrm{F}_1$  v.s.  $71.30\% \mathrm{F}_1)$ , but it still cannot be comparable to our results. This shows that the auxiliary tasks can help learn entity knowledge from raw data, even if the named entity dictionary can not label all entities in a sentence.
Visualization. In the proposed multi-cell LSTM, both ET cells and C cell play important roles in constructing a shared feature spaces across domains. We visualize feature spaces of ET cells and C cell in the Broad Twitter experiments.
Figure 3 uses t-SNE (Maaten and Hinton, 2008) to visualize the ET cell states  $\{\mathbf{c}_k\}_{k=1}^l$ . From the figure we can see that different ET cells can generate different feature distributions (gathering in different clusters of different colours), and states
Entity group	CHEM	CC	GGP	CELL	SPE	All
Is in Source?	✓	✓	✓	×	×	-
LSTM	F1	69.13	78.29	82.79	85.00	79.08	79.23
Δ	-	-	-	-	-	-
MULTI	F1	73.57	79.67	85.83	85.14	79.47	81.05
Δ	+4.44	+1.38	+3.04	+0.14	+0.39	+1.82
Ours	F1	74.95	80.00	86.67	87.10	81.92	82.70
Δ	+5.82	+1.71	+3.88	+2.10	+2.84	+3.47
Table 4: Fine-grained comparisons on BioNLP.
of the same ET cell gather together across domains. This indicates that our model can learn cross-domain entity typed knowledge with the help of ET cells, which are more robust across domains.
Figure 4 visualize the hidden vectors of the target-domain only baseline, the multi-task baseline and the proposed model. From the figure, we can see that both the multi-task baseline and ours can obtain similar feature distributions across domains compared with the target-domain only baseline. In comparison with the multi-task baseline, our model also shows strong matches across domains in an entity type level, which can better narrow the gap between source and target domains as discussed in §3.4.
Fine-grained comparison. We make fine-grained comparisons between our model and the multi-task baseline on the BioNLP dataset, aiming to show how our model achieves better results on the entity type level. Following Crichton et al. (2017) and Jia et al. (2019), we study five well studied entity groups (not including all entity types) in BioNLP13CG. As shown in Table 4, both MULTI (Multi-Task baseline) and Ours achieve significant  $\mathrm{F}_1$  improvement over the target-domain only baseline LSTM on the biochemistry entity groups that appear in both the target and the source datasets, such as CHEM, CC and GGP, which is consistent with intuition.
But for biology entity groups not appearing in the source dataset, such as CELL and SPE, MULTI using traditional parameter sharing hardly improves the performances  $(+0.14\% \mathrm{F}_1$  for CELL and  $+0.39\% \mathrm{F}_1$  for SPE v.s.  $+1.82\% \mathrm{F}_1$  for All). In contrast, Ours achieves relatively strong improvements  $(+2.10\% \mathrm{F}_1$  for CELL and  $+2.84\% \mathrm{F}_1$  for SPE). This benefits from the distinct feature distributions across entity types by the multi-cell LSTM structure, which can effectively prevent the confusions drawn in a unified feature space.
Ablation study. We conduct ablation studies on auxiliary tasks and model parameters. The results
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(a) Target-domain only.
(b) Multi-Task baseline.
Figure 4: t-SNE visualization of hidden vectors on the CoNLL-2003 test set and Broad Twitter test set, represented by signal star and dot, respectively. Different entity types are represented by different colours.
(c) Ours.
Methods	Datasets
BioNLP	Broad Twitter	CBS News
F1	Δ	F1	Δ	F1	Δ
OURS	83.15	-	74.82	-	75.19	-
-Lent	82.71	-0.44	73.97	-0.85	74.95	-0.24
-Latten	81.65	-1.50	73.25	-1.57	73.04	-2.15
-Lent-Latten	81.74	-1.41	73.64	-1.18	72.59	-2.60
BILSTM-BASED	81.06	-2.09	73.84	-0.98	72.49	-2.70
STACKED BILSTMS	80.61	-2.54	73.86	-0.96	69.62	-5.57
HIDDEN EXPANSION	80.32	-2.83	72.34	-2.48	73.17	-2.02
are listed in Table 5.
Auxiliary tasks. When we only ablate  $\mathcal{L}_{ent}$ , the results on all of the three datasets suffer significant decline ( $-0.44\%$ $\mathrm{F}_1$  on BioNLP dataset,  $-0.85\%$ $\mathrm{F}_1$  on Broad Twitter dataset and  $-0.24\%$ $\mathrm{F}_1$  on CBS News dataset, respectively). When we only ablate  $\mathcal{L}_{atten}$ , the results on all of the three datasets suffer significant decline (over  $-1.5\%$ $\mathrm{F}_1$  on all of the three datasets). When we both ablate  $\mathcal{L}_{ent}$  and  $\mathcal{L}_{atten}$ , our model achieves similar results as the BILSTM-BASED baseline. This indicates that domain transfer of our model depends heavily on both auxiliary tasks.
Number of parameters. We use two strategies to make the number of parameters of BiLSTM-BASED baseline comparable to that of our model: (i) STACKED BiLSTMs, stacking multi-layer BiLSTMs and enlarging the hidden size. (ii) HIDDEN EXPANSION, with similar model structure, just enlarging the hidden size. Our model still significantly outperforms these baselines, which shows that the effects of our model do not arise from a larger number of parameters.
Case study. Table 6 shows a case study, "WHO" is an organization and "Nipah" is a virus. Without using target-domain raw data, BI-LSTM baseline miclassifies "Nipah" as ORG. Both Ours and
Table 5: Ablation studies on BioNLP, Broad Twitter and CBS SciTech News datasets.
Sentence	The World Health Organization ( WHO ) describes Nipah infection as a “newly emerging zoonosis that causes severe disease in both animals and humans.”
BiLSTM	The World Health Organization ( WHO ) describes Nipah ORG
BiLSTM+LM	The World Health Organization ( WHO ) describes Nipah Misc
Ours	The World Health Organization ( WHO ) describes Nipah Misc
Table 6: Example from CBS News test. Red and green represent incorrect and correct entities, respectively.
BILSTM+LM give the correct results because this entity is mentioned in raw data. Using the multicell structure, our method learns the pattern "ORG, O, ORG, O" from source data without confusions by target-domain specific entities, thus Ours recognizes "WHO" correctly.
We have investigated a multi-cell compositional LSTM structure for cross-domain NER under the multi-task learning strategy. Theoretically, our method benefits from the distinct feature distributions for each entity type across domains. Results on a range of cross-domain datasets show that multi-cell compositional LSTM outperforms BiLSTM under the multi-task learning strategy.
We thank the anonymous reviewers for their helpful comments and suggestions. We gratefully acknowledge funding from the National Natural Science Foundation of China (NSFC No.61976180) and the Westlake University and Bright Dream Joint Institute for Intelligent Robotics. Yue Zhang is the corresponding author.
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman
Vaughan. 2010. A theory of learning from different domains. Machine Learning, 79(1-2):151-175.
Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao, and Shengping Liu. 2018. Adversarial transfer learning for chinese named entity recognition with self-attention mechanism. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 182–192. Association for Computational Linguistics.
Olivier Chapelle and Alexander Zien. 2005. Semi-supervised classification by low density separation. In AISTATS, volume 2005, pages 57-64. CiteSeer.
Billy Chiu, Gamal Crichton, Anna Korhonen, and Sampo Pyysalo. 2016. How to train good word embeddings for biomedical nlp. In Proceedings of the 15th workshop on biomedical natural language processing, pages 166-174. Association for Computational Linguistics.
Koby Crammer, Michael Kearns, and Jennifer Wortman. 2008. Learning from multiple sources. Journal of Machine Learning Research, 9(Aug):1757-1774.
Gamal Crichton, Sampo Pyysalo, Billy Chiu, and Anna Korhonen. 2017. A neural network multi-task learning approach to biomedical named entity recognition. BMC Bioinformatics, 18(1):368.
Leon Derczynski, Kalina Bontcheva, and Ian Roberts. 2016. Broad twitter corpus: A diverse named entity recognition resource. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1169-1179.
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171-4186. Association for Computational Linguistics.
Bahdanau Dzmitry, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. In ICLR, pages 1-15.
Alex Graves and Jürgen Schmidhuber. 2005. Frameswise phoneme classification with bidirectional LSTM and other neural network architectures. Neural networks, 18(5-6):602-610.
Chen Jia, Xiaobo Liang, and Yue Zhang. 2019. Cross-domain ner using cross-domain language modeling. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2464-2474. Association for Computational Linguistics.
Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang. 2020. Biobert: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4):1234-1240.
Bill Yuchen Lin and Wei Lu. 2018. Neural adaptation layers for cross-domain named entity recognition. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2012-2022. Association for Computational Linguistics.
Di Lu, Leonardo Neves, Vitor Carvalho, Ning Zhang, and Heng Ji. 2018. Visual attention model for name tagging in multimodal social media. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1990-1999. Association for Computational Linguistics.
Xuezhe Ma and Eduard Hovy. 2016. End-to-end sequence labeling via bi-directional LSTM-cnns-crf. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Long Papers), volume 1, pages 1064-1074. Association for Computational Linguistics.
Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-sne. Journal of machine learning research, 9:2579-2605.
Margaret Mitchell, Jacqui Aguilar, Theresa Wilson, and Benjamin Van Durme. 2013. Open domain targeted sentiment. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 1643-1654. Association for Computational Linguistics.
Raymond J Mooney and Razvan C Bunescu. 2006. Subsequence kernels for relation extraction. In Advances in neural information processing systems, pages 171-178.
Claire Nédellec, Robert Bossy, Jin-Dong Kim, Jung-Jae Kim, Tomoko Ohta, Sampo Pyysalo, and Pierre Zweigenbaum. 2013. Overview of bionlp shared task 2013. In Proceedings of the BioNLP Shared Task 2013 Workshop, pages 1-7. Association for Computational Linguistics.
Minlong Peng, Xiaoyu Xing, Qi Zhang, Jinlan Fu, and Xuanjing Huang. 2019. Distantly supervised named entity recognition using positive-unlabeled learning. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2409-2419, Florence, Italy. Association for Computational Linguistics.
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. Glove: Global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, volume 4, pages 1532-1543. Association for Computational Linguistics.
Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep contextualized word representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 2227-2237. Association for Computational Linguistics.
Ana-Maria Popescu, Marco Pennacchiotti, and Deepa Paranjpe. 2011. Extracting events and event descriptions from twitter. In Proceedings of the 20th international conference companion on World wide web., pages 105-106.
Juan Diego Rodriguez, Adam Caldwell, and Alex Liu. 2018. Transfer learning for entity recognition of novel classes. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1974-1985. Association for Computational Linguistics.