A Semi-Markov Structured Support Vector Machine Model for High-Precision Named Entity Recognition

Arora, Ravneet; Tsai, Cary Chi‐Liang; Tsereteli, Ketevan; Kambadur, Prabhanjan; Yang, Yi

doi:10.18653/v1/p19-1587

Cited by 17 publications

(12 citation statements)

References 11 publications

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“…"weighted" accounts for class imbalance by computing the average of binary metrics in which each class's score was weighted by its presence in the true data sample. We calculated precision (P), recall (R), f1-score (F1) for each class [36], gave the accuracy of the model, and calculated the overall macro-precision (macro-P), macro-recall (macro-R), macro-f1 (macro-F1), weighted-precision (weighted-P), weighted-recall (weighted-R), weighted-f1 (weighted-F1) according to the "weighted" and "macro" criteria. The calculation results are given in Table 7.…”

Section: B Model Validation Resultsmentioning

confidence: 99%

Trip Purpose Identification of Docked Bike-Sharing From IC Card Data Using a Continuous Hidden Markov Model

Cao³

et al. 2020

IEEE Access

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It is different from the previous supervised learning algorithm based on personal travel questionnaire, the aim of this study is to develop an unsupervised learning methodology to estimate the docked bike-sharing users' trip purposes using IC card data, which trip purposes were unknown from the dataset. The present study is able to extract the trip-chains, which is used to understand the complete individual trip process. A rigorous method is then proposed to interpret the purpose of each leg of the tripchain using a continuous hidden Markov model (CHMM). This method effectively combines the Gaussian mixture model and the hidden Markov model, and realizes the inference based on trip-chains. It is intended to enhance the understanding of docked bike-sharing users' transfer intention, which is different from most trip motivation recognition methods. The Gaussian mixture layer uses the feature space constructed by the spatial and temporal information on trip-chains from the IC card data, as well as the land-use characteristics of the docked bike-sharing docking stations to complete the transfer of the trip-chains to the trip modes. The hidden Markov structure can realize the process from the trip modes to the trip purposes. The IC card data of docked bike-sharing usage in Nanjing, China is used to interpret the specific steps of the proposed model. A questionnaire survey is conducted to obtain the real trip purposes, which is compared with the estimated results from the model to verify the effectiveness of the model.. The results show that the accuracies of single trip recognition and chain trip recognition are 0.770 and 0.756, respectively. Compared with the baseline algorithm, the model also shows good performance. Therefore, the proposed approach can be used to discover and interpret the trip purpose using the IC card data. INDEX TERMS Continuous hidden Markov model, IC card, docked bike-sharing, trip-chain, trip purpose

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Section: B Model Validation Resultsmentioning

confidence: 99%

Trip Purpose Identification of Docked Bike-Sharing From IC Card Data Using a Continuous Hidden Markov Model

Cao³

et al. 2020

IEEE Access

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“…We can use this task to identify named entities (Kasai et al, 2019;Arora et al, 2019;Jain et al, 2019) and for understanding other cultures (Katan and Taibi, 2004).…”

Section: Wer Ist Bill Gates?mentioning

confidence: 99%

Adapting Entities across Languages and Cultures

Peskov¹,

Hangya²,

Boyd-Graber³

et al. 2021

Findings of the Association for Computational Linguistics: EMNLP 2021

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How would you explain Bill Gates to a German? He is associated with founding a company in the United States, so perhaps the German founder Carl Benz could stand in for Gates in those contexts. This type of translation is called adaptation in the translation community (Vinay and Darbelnet, 1995). Until now, this task has not been done computationally. Automatic adaptation could be used in natural language processing for machine translation and indirectly for generating new question answering datasets and education. We propose two automatic methods and compare them to human results for this novel NLP task. First, a structured knowledge base adapts named entities using their shared properties. Second, vector arithmetic and orthogonal embedding mappings identify better candidates, but at the expense of interpretable features. We evaluate our methods through a new dataset 1 of human adaptations.

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“…In addition to research on improving the performance of the NER model, other experimental setups have been proposed for this task. These include domain adaptation, where a model trained on data from a source domain is used to tag data from a different target domain (Guo et al, 2009;Greenberg et al, 2018;Wang et al, 2020), temporal drift, where a model is tested on data from future time intervals (Derczynski et al, 2016;Rijhwani and Preotiuc-Pietro, 2020), cross-lingual modelling where models trained in one language are adapted to other languages (Tsai et al, 2016;Ni et al, 2017;Xie et al, 2018), identifying nested entities Lu and Roth, 2015) or high-precision NER models (Arora et al, 2019).…”

Section: Related Workmentioning

confidence: 99%

Identifying Named Entities as they are Typed

Arora

Tsai

Preoţiuc-Pietro³

2021

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

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Identifying named entities in written text is an essential component of the text processing pipeline used in applications such as text editors to gain a better understanding of the semantics of the text. However, the typical experimental setup for evaluating Named Entity Recognition (NER) systems is not directly applicable to systems that process text in real time as the text is being typed. Evaluation is performed on a sentence level assuming the end-user is willing to wait until the entire sentence is typed for entities to be identified and further linked to identifiers or coreferenced. We introduce a novel experimental setup for NER systems for applications where decisions about named entity boundaries need to be performed in an online fashion. We study how state-of-the-art methods perform under this setup in multiple languages and propose adaptations to these models to suit this new experimental setup. Experimental results show that the best systems that are evaluated on each token after its typed, reach performance within 1-5 F 1 points of systems that are evaluated at the end of the sentence. These show that entity recognition can be performed in this setup and open up the development of other NLP tools in a similar setup.

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A Semi-Markov Structured Support Vector Machine Model for High-Precision Named Entity Recognition

Cited by 17 publications

References 11 publications

Trip Purpose Identification of Docked Bike-Sharing From IC Card Data Using a Continuous Hidden Markov Model

Trip Purpose Identification of Docked Bike-Sharing From IC Card Data Using a Continuous Hidden Markov Model

Adapting Entities across Languages and Cultures

Identifying Named Entities as they are Typed

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