On the other hand , we detail an original approach to select the examples , based on the respect of proportions in the datasets . These contributions are validated over a large range of experiments implying several datasets and tasks , including named entity recognition , chunking , phonetization , word sense disambiguation . Experimental results show that the accuracy of extractions improves after each iteration . In particular , our model obtained a 15 % error reduction on named entity recognition over individual models . Even these errors can be corrected , to a large extend , using various techniques . Our method was tested on the real-world application of named entities recognition obtaining results comparable to other state of the art classification methods This paper addresses a particular task of information extraction , typically called named entity recognition , which deals with the references to entities made by names occurring in the texts . This paper presents the results of a study of how the named entity recognition problem manifests itself in digital library metadata . In particular , we present the main differences between performing named entity recognition in natural language and in the text within metadata . A vector representation that uses the term frequency shows promising performance . It can recognize news characteristics correctly 96.74 % of 2516 documents across phrase detection and named entity recognition process . Text Chunking : We use the CoNLL - 2000 data tjong2000introduction . Named Entity Recognition ( NER ) : We use the CoNLL - 2003 data tjong2003introduction . In our experiments with the OntoNotes coreference annotations from the CoNLL 2012 shared task Pradhan2012CoNLL2012ST , adding ELMo improved the average F by 3.2 % from 67.2 to 70.4 , establishing a new state of the art , again improving over the previous best ensemble result by 1.6 % F . Named entity extraction The CoNLL 2003 NER task Adding ELMo to the output from the biLSTM in addition to the biLSTM input reduced F by approximately 0.7 % ( not shown ) . subsection : Named Entity Recognition Our baseline NER model concatenates 50 dimensional pre - trained Senna vectors NLPfromScratch : However , this also gives the task a somewhat artificial character . On the one hand , systems are greatly helped by entity recognition and coreference having already been performed ; on the other , they suffer when either of these modules fail , as they do ( in Figure [ reference ] , “ the character ” should probably be coreferent with @entity14 ; clearer examples of failure appear later on in our data analysis ) . Moreover , this inability to use world knowledge also makes it much more difficult for a human to do this task We compare several methods to deal with nested entities in the dataset . We showed that combining tags of entities at all levels for training a sequence labeling model ( joint-tag model ) improved the accuracy of nested named-entity recognition . In the past year , two new models for naming entity recognition have been proposed , namely : Leveraging Linguistic Structures for Named Entity Recognition with Bidirectional Recursive Neural Networks ; A FOFE-based Local Detection Approach for Named Entity Recognition and Mention Detection . In this article , we design a co-decoding scheme that can combine the outputs ( views ) of the two systems to produce an output that is more accurate than the outputs of individual systems . At the same time , it adds the word feature vector to the input layer to complete the named entity recognition in the military text . Compared with the results of traditional methods , this method can complete the task of naming entity recognition in texts of the military field , and finally the F-value on test corpus can reach 81.32 % . We propose two neural network architectures for nested named entity recognition ( NER ) , a setting in which named entities may overlap and also be labeled with more than one label . We encode the nested labels using a linearized scheme . In this paper , we propose a novel bipartite flat-graph network ( BiFlaG ) for nested named entity recognition ( NER ) , which contains two subgraph modules : a flat NER module for outermost entities and a graph module for all the entities located in inner layers . Bidirectional LSTM ( BiLSTM ) and graph convolutional network ( GCN ) are adopted to jointly learn flat entities and their inner dependencies . Named entity recognition plays an important role in many natural language processing applications . While considerable attention has been pain in the past to research issues related to named entity recognition , few studies have been reported on the recognition of nested named entities . This paper presents a morpheme-based method to Chinese nested named entity recognition . The lack of human annotations has been one of the main obstacles for neural named entity recognition in low-resource domains . To address this problem , there have been many efforts on automatically generating silver annotations according to domain-specific dictionaries . Since the Message Understanding Conferences on Information Extraction in the 80 's and 90 's , Named Entity ReCognition ( NERC ) is a well-established task in the Natural Language Processing ( NLP ) community . However , very different systems seem to perform very similarly when applied to the same corpus . As a basic task of NLP , named entity recognition has always been the focus of researchers . At the same time , the word vector representation which is a necessary part of many named entity recognition neural network models has been more and more important . Recently , the emergence of a new type word representation , BERT , has greatly promoted many NLP tasks . Fine-grained Named Entity Recognition is a task whereby we detect and classify entity mentions to a large set of types . These types can span diverse domains such as finance , healthcare , and politics . To fully understand each entity , one needs to detect their mentions , categorize them into semantic types , connect them with their knowledge base entries , and identify their attributes as well as the relationships with others . In this dissertation , we first present the problem of fine-grained entity recognition . Unlike most traditional named entity recognition systems using a small set of entity classes , e.g. , person , organization , location or miscellaneous , we define a novel set of over one hundred fine-grained entity types . Fine-grained Entity Recognition ( FgER ) is the task of detecting and classifying entity mentions into more than 100 types . The type set can span various domains including biomedical ( e.g. , disease , gene ) , sport ( e.g. , sports event , sports player ) , religion and mythology ( e.g. , religion , god ) and entertainment ( e.g. , movies , music ) . Automatically populating ontology with named entities extracted from the unstructured text has become a key issue for Semantic Web . This issue naturally consists of two subtasks : ( 1 ) for the entity mention whose mapping entity does not exist in the ontology , attach it to the right category in the ontology ( i.e. , fine-grained named entity classification ) , and ( 2 ) for the entity mention whose mapping entity is contained in the ontology , link it with its mapping real world entity in the ontology ( i.e. , entity linking ) . Previous studies only focus on one of the two subtasks . Automatically populating ontology with named entities extracted from the unstructured text has become a key issue for Semantic Web and knowledge management techniques . This issue naturally consists of two subtasks : ( 1 ) for the entity mention whose mapping entity does not exist in the ontology , attach it to the right category in the ontology ( i.e. , fine-grained named entity classification ) , and ( 2 ) for the entity mention whose mapping entity is contained in the ontology , link it with its mapping real world entity in the ontology ( i.e. , entity linking ) . Previous studies only focus on one of the two subtasks and can not solve this task of populating ontology with named entities integrally . Systems for structured knowledge extraction and inference have made giant strides in the last decade . Starting from shallow linguistic tagging and coarse-grained recognition of named entities at the resolution of people , places , organizations , and times , modern systems link billions of pages of unstructured text with knowledge graphs having hundreds of millions of entities belonging to tens of thousands of types , and related by tens of thousands of relations . Via deep learning , systems build continuous representations of words , entities , types , and relations , and use these to continually discover new facts to add to the knowledge graph , and support search systems that go far beyond page-level " ten blue links ' ' . Our work involves researching new ways to process this type of data in order to perform entity disambiguation . Our approach uses multi-level clustering and includes fine-grained entity type recognition , contextualization of entities , online processing of which can be supported by a parallel architecture . Various models for nested named entity recognition are designed explicitly and usually do not perform well on non-overlapping named entity recognition compared to sequence labeling models . To tackle the aforementioned problem , we propose a novel model named Attention-Based Entity Region Networks ( AERNs ) for multi-grained entity recognition where multiple entities in a sentence could be non-overlapping or nested . AERNs consist of an entity region recognizer that examines all possible entity regions and an entity region classifier for classifying the regions . This paper presents a novel framework , MGNER , for Multi-Grained Named Entity Recognition where multiple entities or entity mentions in a sentence could be non-overlapping or totally nested . Different from traditional approaches regarding NER as a sequential labeling task and annotate entities consecutively , MGNER detects and recognizes entities on multiple granularities : it is able to recognize named entities without explicitly assuming non-overlapping or totally nested structures . However , existing named entity recognition models used by text mining tools such as tmTool and ezTag are not effective enough , and can not accurately discover new entities . Also , the traditional text mining tools do not consider overlapping entities , which are frequently observed in multi-type named entity recognition results . We propose a neural biomedical named entity recognition and multi-type normalization tool called BERN . We applied state-of-the-art deep learning methods using pre-trained models as input to a bidirectional LSTM network and well-established conditional random fields for de-identification of German medical reports . We performed an extensive evaluation for de-identification and multiclass named entity recognition . Using rule based and out of domain machine learning methods as a baseline , the conditional random field improved F2-score from 70 to 93 % for de-identification , the neural approach reached 96 % in F2-score while keeping balanced precision and recall rates . Recognizing fine-grained named entities , i.e. , street and city instead of just the coarse type location , has been shown to increase task performance in several contexts . Fine-grained types , however , amplify the problem of data sparsity during training , which is why larger amounts of training data are needed . This paper presents an approach of Multi Space Distribution Hidden Markov Model ( MSD-HMM ) for Vietnamese recognition . An MSD-HMM prototype with four independent streams is proposed for modeling the Vietnamese phonemes which embedded tonal information corresponding to its syllable . In this paper , a method based on deep learning for extracting bottleneck features for Vietnamese large vocabulary speech recognition is presented . Deep bottleneck features ( DBNFs ) is able to achieve significant improvements over a number of base bottleneck features which was reported previously . The experiments are carried out on the dataset containing speeches on Voice of Vietnam channel ( VOV ) . The results show that adding tonal feature as input feature of the network reached around 20 % relative recognition performance . The DBNF extraction for Vietnamese recognition decrease the error rate by 51 % , compared to the MFCC baseline . Ripple Down Rules ( RDR ) is considered a good systematic approach to address this for classification problems . In this paper , we present a system using RDR to build the set of rules for Vietnamese Named Entity Recognition which is important for many natural language processing tasks . Experimental results on comparing the proposed approach with a standard method where rules are added in an ad-hoc manner prove to be very promising . In this paper , we propose an approach based on HMM and linguistics for the Vietnamese recognition problem , including handwritten and speech recognition . The main contribution is that our method could be used to model all Vietnamese isolated words by a small number of HMMs . In this paper , we propose an approach based on hidden Markov model ( HMM ) and linguistics for the Vietnamese recognition problem , including handwritten word and speech recognition . The main contribution is that our method can be used to model all Vietnamese isolated words by a small number of HMMs . In this paper we first represent our developing method for a multiobjective Vietnamese language corpus , namely VnCorpus , together with the description of various kinds of sources from which we have used to build up this database . It then goes on to describe some first experiences in using this corpus for the segmentation of sentences into Vietnamese words and for the recognition of Vietnamese continuous speech . Upon completion the corpus will constitute a valuable resource for research in the fields of computational linguistics , language and speech technologies . Second , it introduces NLP techniques that we propose to analyze Vietnamese texts , focusing on advanced word segmentation which accounts for a number of complex linguistic phenomena as well as pre-tagging , part-of- speech ( POS ) tagging , and post-tagging tasks for proper noun recognition . Finally , the paper discusses the results of several experiments that have been performed to study the impact of the strategy chosen for the recognition of Vietnamese proper nouns and the further works we intend to deal with . Index_Terms— Vietnamese , natural language processing , word segmentation , tagger , tagset , part-of-speech , POS tagging , text corpus , pre-tagging , post- tagging , corpus , syntax , context of texts , proper names , proper noun recognition . In this paper , we provide a deep analysis on the effect of linguistic features and ontological features for the Vietnamese named entity recognition ( NER ) task . Plugged in into an off-the-shelf learning framework , we show that , simple lexical words and bi-gram features allow to encode dependencies amongst possible NE labels in Vietnamese language .
On the other hand , we detail an original approach to select the examples , based on the respect of proportions in the datasets . These contributions are validated over a large range of experiments implying several datasets and tasks , including named entity recognition , chunking , phonetization , word sense disambiguation .
Experimental results show that the accuracy of extractions improves after each iteration . In particular , our model obtained a 15 % error reduction on named entity recognition over individual models .
Even these errors can be corrected , to a large extend , using various techniques . Our method was tested on the real-world application of named entities recognition obtaining results comparable to other state of the art classification methods
This paper addresses a particular task of information extraction , typically called named entity recognition , which deals with the references to entities made by names occurring in the texts . This paper presents the results of a study of how the named entity recognition problem manifests itself in digital library metadata . In particular , we present the main differences between performing named entity recognition in natural language and in the text within metadata .
A vector representation that uses the term frequency shows promising performance . It can recognize news characteristics correctly 96.74 % of 2516 documents across phrase detection and named entity recognition process .
Text Chunking : We use the CoNLL - 2000 data tjong2000introduction . Named Entity Recognition ( NER ) : We use the CoNLL - 2003 data tjong2003introduction .
In our experiments with the OntoNotes coreference annotations from the CoNLL 2012 shared task Pradhan2012CoNLL2012ST , adding ELMo improved the average F by 3.2 % from 67.2 to 70.4 , establishing a new state of the art , again improving over the previous best ensemble result by 1.6 % F . Named entity extraction The CoNLL 2003 NER task
Adding ELMo to the output from the biLSTM in addition to the biLSTM input reduced F by approximately 0.7 % ( not shown ) . subsection : Named Entity Recognition Our baseline NER model concatenates 50 dimensional pre - trained Senna vectors NLPfromScratch :
However , this also gives the task a somewhat artificial character . On the one hand , systems are greatly helped by entity recognition and coreference having already been performed ; on the other , they suffer when either of these modules fail , as they do ( in Figure [ reference ] , “ the character ” should probably be coreferent with @entity14 ; clearer examples of failure appear later on in our data analysis ) . Moreover , this inability to use world knowledge also makes it much more difficult for a human to do this task
We compare several methods to deal with nested entities in the dataset . We showed that combining tags of entities at all levels for training a sequence labeling model ( joint-tag model ) improved the accuracy of nested named-entity recognition .
In the past year , two new models for naming entity recognition have been proposed , namely : Leveraging Linguistic Structures for Named Entity Recognition with Bidirectional Recursive Neural Networks ; A FOFE-based Local Detection Approach for Named Entity Recognition and Mention Detection . In this article , we design a co-decoding scheme that can combine the outputs ( views ) of the two systems to produce an output that is more accurate than the outputs of individual systems .
At the same time , it adds the word feature vector to the input layer to complete the named entity recognition in the military text . Compared with the results of traditional methods , this method can complete the task of naming entity recognition in texts of the military field , and finally the F-value on test corpus can reach 81.32 % .
We propose two neural network architectures for nested named entity recognition ( NER ) , a setting in which named entities may overlap and also be labeled with more than one label . We encode the nested labels using a linearized scheme .
In this paper , we propose a novel bipartite flat-graph network ( BiFlaG ) for nested named entity recognition ( NER ) , which contains two subgraph modules : a flat NER module for outermost entities and a graph module for all the entities located in inner layers . Bidirectional LSTM ( BiLSTM ) and graph convolutional network ( GCN ) are adopted to jointly learn flat entities and their inner dependencies .
Named entity recognition plays an important role in many natural language processing applications . While considerable attention has been pain in the past to research issues related to named entity recognition , few studies have been reported on the recognition of nested named entities . This paper presents a morpheme-based method to Chinese nested named entity recognition .
The lack of human annotations has been one of the main obstacles for neural named entity recognition in low-resource domains . To address this problem , there have been many efforts on automatically generating silver annotations according to domain-specific dictionaries .
Since the Message Understanding Conferences on Information Extraction in the 80 's and 90 's , Named Entity ReCognition ( NERC ) is a well-established task in the Natural Language Processing ( NLP ) community . However , very different systems seem to perform very similarly when applied to the same corpus .
As a basic task of NLP , named entity recognition has always been the focus of researchers . At the same time , the word vector representation which is a necessary part of many named entity recognition neural network models has been more and more important . Recently , the emergence of a new type word representation , BERT , has greatly promoted many NLP tasks .
Fine-grained Named Entity Recognition is a task whereby we detect and classify entity mentions to a large set of types . These types can span diverse domains such as finance , healthcare , and politics .
To fully understand each entity , one needs to detect their mentions , categorize them into semantic types , connect them with their knowledge base entries , and identify their attributes as well as the relationships with others . In this dissertation , we first present the problem of fine-grained entity recognition . Unlike most traditional named entity recognition systems using a small set of entity classes , e.g. , person , organization , location or miscellaneous , we define a novel set of over one hundred fine-grained entity types .
Fine-grained Entity Recognition ( FgER ) is the task of detecting and classifying entity mentions into more than 100 types . The type set can span various domains including biomedical ( e.g. , disease , gene ) , sport ( e.g. , sports event , sports player ) , religion and mythology ( e.g. , religion , god ) and entertainment ( e.g. , movies , music ) .
Automatically populating ontology with named entities extracted from the unstructured text has become a key issue for Semantic Web . This issue naturally consists of two subtasks : ( 1 ) for the entity mention whose mapping entity does not exist in the ontology , attach it to the right category in the ontology ( i.e. , fine-grained named entity classification ) , and ( 2 ) for the entity mention whose mapping entity is contained in the ontology , link it with its mapping real world entity in the ontology ( i.e. , entity linking ) . Previous studies only focus on one of the two subtasks .
Automatically populating ontology with named entities extracted from the unstructured text has become a key issue for Semantic Web and knowledge management techniques . This issue naturally consists of two subtasks : ( 1 ) for the entity mention whose mapping entity does not exist in the ontology , attach it to the right category in the ontology ( i.e. , fine-grained named entity classification ) , and ( 2 ) for the entity mention whose mapping entity is contained in the ontology , link it with its mapping real world entity in the ontology ( i.e. , entity linking ) . Previous studies only focus on one of the two subtasks and can not solve this task of populating ontology with named entities integrally .
Systems for structured knowledge extraction and inference have made giant strides in the last decade . Starting from shallow linguistic tagging and coarse-grained recognition of named entities at the resolution of people , places , organizations , and times , modern systems link billions of pages of unstructured text with knowledge graphs having hundreds of millions of entities belonging to tens of thousands of types , and related by tens of thousands of relations . Via deep learning , systems build continuous representations of words , entities , types , and relations , and use these to continually discover new facts to add to the knowledge graph , and support search systems that go far beyond page-level " ten blue links ' ' .
Our work involves researching new ways to process this type of data in order to perform entity disambiguation . Our approach uses multi-level clustering and includes fine-grained entity type recognition , contextualization of entities , online processing of which can be supported by a parallel architecture .
Various models for nested named entity recognition are designed explicitly and usually do not perform well on non-overlapping named entity recognition compared to sequence labeling models . To tackle the aforementioned problem , we propose a novel model named Attention-Based Entity Region Networks ( AERNs ) for multi-grained entity recognition where multiple entities in a sentence could be non-overlapping or nested . AERNs consist of an entity region recognizer that examines all possible entity regions and an entity region classifier for classifying the regions .
This paper presents a novel framework , MGNER , for Multi-Grained Named Entity Recognition where multiple entities or entity mentions in a sentence could be non-overlapping or totally nested . Different from traditional approaches regarding NER as a sequential labeling task and annotate entities consecutively , MGNER detects and recognizes entities on multiple granularities : it is able to recognize named entities without explicitly assuming non-overlapping or totally nested structures .
However , existing named entity recognition models used by text mining tools such as tmTool and ezTag are not effective enough , and can not accurately discover new entities . Also , the traditional text mining tools do not consider overlapping entities , which are frequently observed in multi-type named entity recognition results . We propose a neural biomedical named entity recognition and multi-type normalization tool called BERN .
We applied state-of-the-art deep learning methods using pre-trained models as input to a bidirectional LSTM network and well-established conditional random fields for de-identification of German medical reports . We performed an extensive evaluation for de-identification and multiclass named entity recognition . Using rule based and out of domain machine learning methods as a baseline , the conditional random field improved F2-score from 70 to 93 % for de-identification , the neural approach reached 96 % in F2-score while keeping balanced precision and recall rates .
Recognizing fine-grained named entities , i.e. , street and city instead of just the coarse type location , has been shown to increase task performance in several contexts . Fine-grained types , however , amplify the problem of data sparsity during training , which is why larger amounts of training data are needed .
This paper presents an approach of Multi Space Distribution Hidden Markov Model ( MSD-HMM ) for Vietnamese recognition . An MSD-HMM prototype with four independent streams is proposed for modeling the Vietnamese phonemes which embedded tonal information corresponding to its syllable .
In this paper , a method based on deep learning for extracting bottleneck features for Vietnamese large vocabulary speech recognition is presented . Deep bottleneck features ( DBNFs ) is able to achieve significant improvements over a number of base bottleneck features which was reported previously . The experiments are carried out on the dataset containing speeches on Voice of Vietnam channel ( VOV ) . The results show that adding tonal feature as input feature of the network reached around 20 % relative recognition performance . The DBNF extraction for Vietnamese recognition decrease the error rate by 51 % , compared to the MFCC baseline .
Ripple Down Rules ( RDR ) is considered a good systematic approach to address this for classification problems . In this paper , we present a system using RDR to build the set of rules for Vietnamese Named Entity Recognition which is important for many natural language processing tasks . Experimental results on comparing the proposed approach with a standard method where rules are added in an ad-hoc manner prove to be very promising .
In this paper , we propose an approach based on HMM and linguistics for the Vietnamese recognition problem , including handwritten and speech recognition . The main contribution is that our method could be used to model all Vietnamese isolated words by a small number of HMMs .
In this paper , we propose an approach based on hidden Markov model ( HMM ) and linguistics for the Vietnamese recognition problem , including handwritten word and speech recognition . The main contribution is that our method can be used to model all Vietnamese isolated words by a small number of HMMs .
In this paper we first represent our developing method for a multiobjective Vietnamese language corpus , namely VnCorpus , together with the description of various kinds of sources from which we have used to build up this database . It then goes on to describe some first experiences in using this corpus for the segmentation of sentences into Vietnamese words and for the recognition of Vietnamese continuous speech . Upon completion the corpus will constitute a valuable resource for research in the fields of computational linguistics , language and speech technologies .
Second , it introduces NLP techniques that we propose to analyze Vietnamese texts , focusing on advanced word segmentation which accounts for a number of complex linguistic phenomena as well as pre-tagging , part-of- speech ( POS ) tagging , and post-tagging tasks for proper noun recognition . Finally , the paper discusses the results of several experiments that have been performed to study the impact of the strategy chosen for the recognition of Vietnamese proper nouns and the further works we intend to deal with . Index_Terms— Vietnamese , natural language processing , word segmentation , tagger , tagset , part-of-speech , POS tagging , text corpus , pre-tagging , post- tagging , corpus , syntax , context of texts , proper names , proper noun recognition .
In this paper , we provide a deep analysis on the effect of linguistic features and ontological features for the Vietnamese named entity recognition ( NER ) task . Plugged in into an off-the-shelf learning framework , we show that , simple lexical words and bi-gram features allow to encode dependencies amongst possible NE labels in Vietnamese language .
Strategies to Select Examples for Active Learning with Conditional Random Fields Iterative joint extraction of entities, relationships and coreferences from text sources A Very Fast and Efficient Linear Classification Algorithm An analysis of the named entity recognition problem in digital library metadata Fake News Identification Characteristics Using Named Entity Recognition and Phrase Detection Semi-Supervised Sequence Modeling with Cross-View Training Deep contextualized word representations A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task A Feature-Based Model for Nested Named-Entity Recognition at VLSP-2018 NER Evaluation Campaign A Joint Decoding Algorithm for Named Entity Recognition Military Text Named Entity Recognition Based on Deep Learning Neural Architectures for Nested NER through Linearization Bipartite Flat-Graph Network for Nested Named Entity Recognition Morpheme-based chinese nested named entity recognition A Graph Attention Model for Dictionary-Guided Named Entity Recognition Boosting Robustness of a Named Entity Recognizer Chinese Named Entity Recognition with Bert Fine-Grained Named Entity Recognition using ELMo and Wikidata Entity Analysis with Weak Supervision: Typing, Linking, and Attribute Extraction FgER: Fine-Grained Entity Recognition APOLLO: a general framework for populating ontology with named entities via random walks on graphs A graph-based approach for ontology population with named entities Knowledge Extraction and Inference from Text: Shallow, Deep, and Everything in Between Entity Disambiguation for Wild Big Data Using Multi-Level Clustering AERNs: Attention-Based Entity Region Networks for Multi-Grained Named Entity Recognition Multi-Grained Named Entity Recognition A Neural Named Entity Recognition and Multi-Type Normalization Tool for Biomedical Text Mining Deep Learning Approaches Outperform Conventional Strategies in De-Identification of German Medical Reports In-Memory Distributed Training of Linear-Chain Conditional Random Fields with an Application to Fine-Grained Named Entity Recognition Vietnamese recognition using tonal phoneme based on multi space distribution Improving Acoustic Model for Vietnamese Large Vocabulary Continuous Speech Recognition System Using Deep Bottleneck Features Ripple Down Rules for Vietnamese Named Entity Recognition A Generic Approach for the Vietnamese Handwritten and Speech Recognition Problems A robust method for the Vietnamese handwritten and speech recognition Towards a Multi-Objective Corpus for Vietnamese Language Vietnamese proper noun recognition Linguistically Motivated and Ontological Features for Vietnamese Named Entity Recognition
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CICLing (1) RCIS JCDL EMNLP NAACL-HLT ACL DSC ACL (1) FSKD AAAI WWW (Companion Volume) CIKM SIGIR DC@ISWC ICTAI ACL (1) GMDS GSCL KSE ICCCI (1) IEA/AIE ICPR (3) PACLIC RIVF RIVF
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