| { | |
| "paper_id": "2020", | |
| "header": { | |
| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T04:33:38.964479Z" | |
| }, | |
| "title": "A Deeper Study on Features for Named Entity Recognition", | |
| "authors": [ | |
| { | |
| "first": "Sobha", | |
| "middle": [], | |
| "last": "Lalitha", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "AU-KBC Research Centre MIT Campus of Anna University Chromepet", | |
| "location": { | |
| "settlement": "Chennai", | |
| "country": "India" | |
| } | |
| }, | |
| "email": "[email protected]" | |
| } | |
| ], | |
| "year": "", | |
| "venue": null, | |
| "identifiers": {}, | |
| "abstract": "This paper deals with the various features used for the identification of named entities. The performance of the machine learning system heavily depends on the feature selection criteria. The intention to trace the essential features required for the development of named entity system across languages motivated us to conduct this study. The linguistic analysis was done to find out the part of speech patterns surrounding the context of named entities and from the observation linguistic oriented features are identified for both Indian and European languages. The Indian languages belongs to Dravidian language family such as Tamil, Telugu, Malayalam, Indo-Aryan language family such as Hindi, Punjabi, Bengali and Marathi, European languages such as English, Spanish, Dutch, German and Hungarian are used in this work. The machine learning technique CRFs was used for the system development. The experiments were conducted using the linguistic features and the results obtained for each languages are comparable with state-of-art systems.", | |
| "pdf_parse": { | |
| "paper_id": "2020", | |
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| "abstract": [ | |
| { | |
| "text": "This paper deals with the various features used for the identification of named entities. The performance of the machine learning system heavily depends on the feature selection criteria. The intention to trace the essential features required for the development of named entity system across languages motivated us to conduct this study. The linguistic analysis was done to find out the part of speech patterns surrounding the context of named entities and from the observation linguistic oriented features are identified for both Indian and European languages. The Indian languages belongs to Dravidian language family such as Tamil, Telugu, Malayalam, Indo-Aryan language family such as Hindi, Punjabi, Bengali and Marathi, European languages such as English, Spanish, Dutch, German and Hungarian are used in this work. The machine learning technique CRFs was used for the system development. The experiments were conducted using the linguistic features and the results obtained for each languages are comparable with state-of-art systems.", | |
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| "section": "Abstract", | |
| "sec_num": null | |
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| { | |
| "text": "Named Entity Recognition (NER) is defined as the process of automatic identification of proper nouns and classifies the identified entities into predefined categories such as person, location, organization, facilities, products, temporal or numeric expressions etc. Even though named entity recognition is a well-established research filed and lot of research works are available for various languages, to the best of our language no work was found on the deeper analysis of features required for named entity system across languages.", | |
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| "section": "Related Work", | |
| "sec_num": "1." | |
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| { | |
| "text": "Initially the term NER was defined in Message Understanding Conference (MUC), when the structured information about company and defense related activities needed to be extracted from the unstructured text. It was noticed that the main information units to be extracted are named entities (Grishman et al. 1996) . The very first research work in NER was done by Lisa F. Rau, who developed the system to recognize company names using hand-crafted rules. In MUC-7, five out of eight systems were generated using rule based method (Chinchor 1998) . Nadeau et al. (2007) has reported fifteen years of research carried out in the field of entity recognition. Guti\u00e9rrez et al. (2015) developed a Spanish NE system using CRF. The dataset was obtained from CONLL 2002 shared task. Ekbal et al. (2008) worked on a Bengali named entity recognition using CRF. Ekbal et al. (2009) contributed NER systems for Hindi & Bengali using CRF framework. Kaur et al. (2012) built an NE system for Punjabi language. Bindu & Sumam Mary (2012) used CRF based approach for identifying named entities in Malayalam text. Khanam et.al. (2016) has worked on the Named Entity Identification for Telugu Language using hybrid approach. Sobha et al. (2007) developed a multilingual named entity system to identify the place names using Finite State Automaton (FSA). Vijayakrishna & Sobha (2008) focused on the Tamil NER for tourism domain which consists of nested tagging of named entities. Malarkodi & Sobha (2012a) built a NE system for Indian languages like Tamil, Telugu, Hindi, Marathi, Punjabi and Bengali using CRF. Malarkodi et al. (2012b) discussed the various challenges, while developing the NE system in Tamil language. Sobha et al. (2013) has participated in ICON NLP tool contest and submitted the test runs for 5 Indian languages and English. Patil et al. (2016) reported a work on NER for Marathi using HMM. Jaspreet et al. (2015) contributed Punjabi NER using 2 machine learning approaches namely HMM and MEMM. Antony et.al. (2014) constructed the NE system for Tamil Biomedical documents using SVM classifier. Lakshmi et.al. (2016) has worked on the Malayalam NER using Fuzzy-SVM and it is based on the semantic features and linguistic grammar rules. Jiljo et.al. (2016) used TnT and Maximum Entropy Markov model for NE identification in Malayalam data. The proposed methodology yields 82.5% accuracy.", | |
| "cite_spans": [ | |
| { | |
| "start": 288, | |
| "end": 310, | |
| "text": "(Grishman et al. 1996)", | |
| "ref_id": "BIBREF6" | |
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| { | |
| "start": 527, | |
| "end": 542, | |
| "text": "(Chinchor 1998)", | |
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| "start": 545, | |
| "end": 565, | |
| "text": "Nadeau et al. (2007)", | |
| "ref_id": "BIBREF17" | |
| }, | |
| { | |
| "start": 653, | |
| "end": 676, | |
| "text": "Guti\u00e9rrez et al. (2015)", | |
| "ref_id": "BIBREF7" | |
| }, | |
| { | |
| "start": 772, | |
| "end": 791, | |
| "text": "Ekbal et al. (2008)", | |
| "ref_id": "BIBREF1" | |
| }, | |
| { | |
| "start": 848, | |
| "end": 867, | |
| "text": "Ekbal et al. (2009)", | |
| "ref_id": "BIBREF2" | |
| }, | |
| { | |
| "start": 933, | |
| "end": 951, | |
| "text": "Kaur et al. (2012)", | |
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| "start": 993, | |
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| "text": "Bindu & Sumam Mary (2012)", | |
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| "text": "Khanam et.al. (2016)", | |
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| "text": "Sobha et al. (2007)", | |
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| "text": "Vijayakrishna & Sobha (2008)", | |
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| "text": "Malarkodi & Sobha (2012a)", | |
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| "text": "Malarkodi et al. (2012b)", | |
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| "text": "Sobha et al. (2013)", | |
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| { | |
| "start": 1824, | |
| "end": 1843, | |
| "text": "Patil et al. (2016)", | |
| "ref_id": "BIBREF18" | |
| }, | |
| { | |
| "start": 1890, | |
| "end": 1912, | |
| "text": "Jaspreet et al. (2015)", | |
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| }, | |
| { | |
| "start": 2094, | |
| "end": 2115, | |
| "text": "Lakshmi et.al. (2016)", | |
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| { | |
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| "end": 2254, | |
| "text": "Jiljo et.al. (2016)", | |
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| "section": "Related Work", | |
| "sec_num": "1." | |
| }, | |
| { | |
| "text": "Boj\u00f3rquez et al. (2015) worked on improving the Spanish NER used in the Text Dialog System (TDS) by using semisupervised technique. Zea et.al. (2016) developed a semisupervised NE system for Spanish language. Athavale et al. (2016) described a Neural Network model for NER based on the Bi Directional RNN-LSTM. In order to identify the mentions of medications, Adverse Drug Event (ADE) and symptoms of the diseases in the clinical notes (Florez et al. 2018) proposed the character-level word representation methods which can be used as an input feature to neural network model called LSTM.", | |
| "cite_spans": [ | |
| { | |
| "start": 132, | |
| "end": 149, | |
| "text": "Zea et.al. (2016)", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 437, | |
| "end": 457, | |
| "text": "(Florez et al. 2018)", | |
| "ref_id": "BIBREF3" | |
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| ], | |
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| "section": "Related Work", | |
| "sec_num": "1." | |
| }, | |
| { | |
| "text": "The various shared tasks conducted for Named Entity Recognition are discussed in this section. In 2002, CONLL shared task about NER was focused on Spanish and Dutch (Tjong et al. 2002) . The CONLL 2003 offered dataset for English and German (Tjong et al. 2003 ). The NERSSEAL shared task of IJCNLP-2008 was organized for 5 Indian Languages namely Hindi, Bengali, Oriya, Telugu and Urdu (Singh, 2008) . In 2013 AU-KBC has organized NER shared task as part of Forum for Information Retrieval for Evaluation (FIRE), to create a benchmark data for Indian Languages. The dataset was released for 4 Indian Languages like Bengali, Hindi, Malayalam, and Tamil and also for English. The various techniques used by the participants are CRF, rule based approach and list based search (Pattabhi & Sobha 2013) . The 2nd edition of NER track for IL has organized as part of FIRE 2014 for English and 3 IL namely Hindi, Malayalam, and Tamil. The main focus of this track is nested entity identification. The participants have used CRF and SVM for system development (Pattabhi et al. 2014 ). ", | |
| "cite_spans": [ | |
| { | |
| "start": 147, | |
| "end": 184, | |
| "text": "Spanish and Dutch (Tjong et al. 2002)", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 222, | |
| "end": 259, | |
| "text": "English and German (Tjong et al. 2003", | |
| "ref_id": null | |
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| { | |
| "start": 386, | |
| "end": 399, | |
| "text": "(Singh, 2008)", | |
| "ref_id": "BIBREF21" | |
| }, | |
| { | |
| "start": 773, | |
| "end": 796, | |
| "text": "(Pattabhi & Sobha 2013)", | |
| "ref_id": "BIBREF19" | |
| }, | |
| { | |
| "start": 1051, | |
| "end": 1072, | |
| "text": "(Pattabhi et al. 2014", | |
| "ref_id": "BIBREF20" | |
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| "section": "Related Work", | |
| "sec_num": "1." | |
| }, | |
| { | |
| "text": "In this section, we discuss the corpus we have used for the study. The corpus for Tamil, Malayalam, and English was collected using an automated crawler program. The details of the POS tagset are explained in this section. The BIS POS tagset was used for Indian Languages. The Tamil POS tagger developed by Sobha et al. (2016) works with an accuracy of 95.16% (Sobha et al., 2016) . The Brills POS tagger (Brill et al., 1992 ) is used for this task. The dataset used for German are preprocessed with Stanford POS tagger (Manning et al., 2014) . The Spanish and Dutch dataset are obtained from the CONLL shared task are already tagged with POS information.", | |
| "cite_spans": [ | |
| { | |
| "start": 307, | |
| "end": 326, | |
| "text": "Sobha et al. (2016)", | |
| "ref_id": "BIBREF23" | |
| }, | |
| { | |
| "start": 360, | |
| "end": 380, | |
| "text": "(Sobha et al., 2016)", | |
| "ref_id": "BIBREF23" | |
| }, | |
| { | |
| "start": 405, | |
| "end": 424, | |
| "text": "(Brill et al., 1992", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 520, | |
| "end": 542, | |
| "text": "(Manning et al., 2014)", | |
| "ref_id": "BIBREF16" | |
| } | |
| ], | |
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| "section": "Corpus Statistics", | |
| "sec_num": "3." | |
| }, | |
| { | |
| "text": "The part of speech patterns frequently occurred in the context of named entities are analyzed for each language and the results are discussed in this section. We analyze the corpus to arrive at the most suitable word level features for identifying the NE which can be used for machine learning purposes. We have taken a window of three words and identified the most frequent grammatical and typographical feature that occurs. The distribution of each feature in each language is given in detail below.", | |
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| "section": "Features used for Named Entity Recognition", | |
| "sec_num": "4." | |
| }, | |
| { | |
| "text": "In Tamil corpus, the named entities occurred at the beginning of the sentence in 3,776 instances and in 2,056 instances named entities occurred at the end of the sentence, punctuations preceded the NE in 6,222 times and 4,596 times punctuations succeed the NE, common nouns preceded the NE in 6,274 times and succeeded the NE in 9,038 times, proper nouns occurred before NE in 2,250 instances and after NE in 2,868 instances. The postpositions occurred before NE in 999 instances, adjectives occurred before NE in 1418 instances and conjunction occurred before NE in 384 times. The verbal participle preceding the named entities in 716 instances and the relative participle verbs preceded the NE in 1,007 times. The finite verbs succeed the NE in 998 instances, postpositions, adverb, and adjectives occurred at 1131, 980 and 878 instances respectively.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Analysis of common Linguistic features", | |
| "sec_num": "4.1" | |
| }, | |
| { | |
| "text": "The Malayalam corpus has the following distribution. The named entities occurred at the beginning of the sentence in 1,062 instances and in 72 instances named entities occurred at the end of the sentence, punctuations preceded the NE in 818 times and 751 times punctuations succeed the NE, common nouns preceded the NE in 1,281 times and succeeded the NE in 1,794 times, proper nouns occurred before NE in 774 instances and after NE in 939 instances. The postpositions occurred before NE in 209 instances, adjectives occurred before NE in 201 instances and conjunction occurred before NE in 85 times. The verbal participle preceding the named entities in 82 instances and the relative participle verbs preceded the NE in 238 times. The finite verbs succeed the NE in 628 instances, postpositions, adverb, and adjectives occurred at 192, 173 and 273 instances respectively.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Analysis of common Linguistic features", | |
| "sec_num": "4.1" | |
| }, | |
| { | |
| "text": "In Telugu corpus, the named entities occurred at the beginning of the sentence in 776 instances and in 17 instances named entities occurred at the end of the sentence, punctuations preceded the NE in 588 times and 610 times punctuations succeed the NE, common nouns preceded the NE in 3722 times and succeeded the NE in 4641 times, proper nouns occurred before NE in 540 instances and after NE in 615 instances. The postpositions occurred before NE in 450 instances, adjectives occurred before NE in 315 instances and conjunction occurred before NE in 153 times. The verbs preceding the named entities in 1541 instances and the relative participle verbs preceded the NE in 78 times. The verbs succeed the NE in 1307 instances, postpositions, adverb and adjectives occurred at 665, 263 and 256 instances respectively.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Analysis of common Linguistic features", | |
| "sec_num": "4.1" | |
| }, | |
| { | |
| "text": "The Hindi corpus has the following distribution. The named entities occurred at the beginning of the sentence in 5,290 instances and in 1,201 instances named entities occurred at the end of the sentence, punctuations preceded the NE in 2281 times and 2070 times punctuations succeed the NE, common nouns preceded the NE in 1862 times and succeeded the NE in 1307 times, proper nouns occurred before NE in 1055 instances and after NE in 753 instances. The postpositions occurred before NE in 3536 instances, adjectives occurred before NE in 611 instances and conjunction occurred before NE in 1844 times. The verbs preceding the named entities in 349 instances and the relative participle verbs preceded the NE in 412 times. The verbs succeed the NE in 876 instances, postpositions, adverb and adjectives occurred at 915, 536 and 436 instances respectively.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Analysis of common Linguistic features", | |
| "sec_num": "4.1" | |
| }, | |
| { | |
| "text": "In Punjabi corpus, the named entities occurred at the beginning of the sentence in 1267 instances and in 831 instances named entities occurred at the end of the sentence, punctuations preceded the NE in 499 times and 475 times punctuations succeed the NE, common nouns preceded the NE in 1,119 times and succeeded the NE in 684 times, proper nouns occurred before NE in 304 instances and after NE in 304 instances. The postpositions occurred before NE in 1363 instances, adjectives occurred before NE in 553 instances and conjunction occurred before NE in 227 times. The verbs preceding the named entities in 99 instances and the relative participle verbs preceded the NE in 176 times. The verbs succeed the NE in 361 instances, postpositions, adverb and adjectives occurred at 3,211, 136 and 158 instances respectively.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Analysis of common Linguistic features", | |
| "sec_num": "4.1" | |
| }, | |
| { | |
| "text": "In Bengali corpus, the NE distribution is as discussed here. The named entities occurred at the beginning of the sentence in 630 instances and in 312 instances named entities occurred at the end of the sentence, punctuations preceded the NE in 288 times and 204 times punctuations succeed the NE, common nouns preceded the NE in 561 times and succeeded the NE in 908 times, proper nouns occurred before NE in 197 instances and after NE in 199 instances. The postpositions occurred before NE in 120 instances, adjectives occurred before NE in 148 instances and conjunction occurred before NE in 239 times. The verbs preceding the named entities in 208 instances and the relative participle verbs preceded the NE in 25 times. The verbs succeed the NE in 290 instances, postpositions, adverb and adjectives occurred at 159, 280 and 238 instances respectively.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Analysis of common Linguistic features", | |
| "sec_num": "4.1" | |
| }, | |
| { | |
| "text": "The Marathi corpus has the following distribution. The named entities occurred at the beginning of the sentence is 967 instances and in 488 instances named entities occurred at the end of the sentence, punctuations preceded the NE in 609 times and 566 times punctuations succeed the NE, common nouns preceded the NE in 1956 times and succeeded the NE in 1879 times, proper nouns occurred before NE in 348 instances and after NE in 219 instances. The postpositions occurred before NE in 14 instances, adjectives occurred before NE in 114 instances and conjunction occurred before NE in 466 times. The verbs preceding the named entities in 475 instances and the relative participle verbs preceded the NE in 38 times. The verbs succeed the NE in 419 instances, postpositions, adverb and adjectives occurred at 212, 253 and 392 instances respectively.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Analysis of common Linguistic features", | |
| "sec_num": "4.1" | |
| }, | |
| { | |
| "text": "In English corpus, the NE distribution is as discussed here. The named entities occurred at the beginning of the sentence in 1,014 instances and in 2,078 instances named entities occurred at the end of the sentence, punctuations preceded the NE in 1549 times and 2078 times punctuations succeed the NE, common nouns preceded the NE in 1239 times and succeeded the NE in 1289 times, proper nouns occurred before NE in 745 instances and after NE in 823 instances. The prepositions occurred before NE in 2794 instances. The determiners preceding the named entities in 1425 instances. The verbal participle preceding the named entities in 156 instances. The finite verbs succeed the NE in 680 instances, prepositions and conjunctions occurred at 1195 and 774 instances respectively.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Analysis of common Linguistic features", | |
| "sec_num": "4.1" | |
| }, | |
| { | |
| "text": "The Spanish corpus has the following distribution. The named entities occurred at the beginning of the sentence in 2046 instances and in 2,131 instances named entities occurred at the end of the sentence, punctuations preceded the NE in 2404 times and 7010 times punctuations succeed the NE, nouns preceded the NE in 1123 times and succeeded the NE in 204 times. The prepositions occurred before NE in 7231 instances. The determiners preceding the named entities in 3993 instances. The verbs succeed the NE in 2060 instances, prepositions and conjunctions occurred at 2116 and 1648 instances respectively.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Analysis of common Linguistic features", | |
| "sec_num": "4.1" | |
| }, | |
| { | |
| "text": "In Dutch corpus, the NE distribution is as discussed here. The named entities occurred at the beginning of the sentence in 5605 instances and in 2787 instances named entities occurred at the end of the sentence, punctuations preceded the NE in 4142 times and 10321 times punctuations succeed the NE, nouns preceded the NE in 2627 times and succeeded the NE in 3174 times. The prepositions occurred before NE in 5146 instances. The determiners preceding the named entities in 4142 instances. The verbs succeed the NE in 4657 instances, prepositions and conjunctions occurred at 2062 and 1411 instances respectively.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Analysis of common Linguistic features", | |
| "sec_num": "4.1" | |
| }, | |
| { | |
| "text": "The German corpus has the following distribution. The named entities occurred at the beginning of the sentence in 2033 instances and in 128 instances named entities occurred at the end of the sentence, punctuations preceded the NE in 966 times and 2535 times punctuations succeed the NE, common nouns preceded the NE in 5012 times and succeeded the NE in 3886 times, proper nouns occurred before NE in 321 instances and after NE in 608 instances. The prepositions occurred before NE in 5869 instances. The determiners preceding the named entities in 7166 instances. The finite verbs succeed the NE in 3140 instances, prepositions and conjunctions occurred at 3075 and 2059 instances respectively.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Analysis of common Linguistic features", | |
| "sec_num": "4.1" | |
| }, | |
| { | |
| "text": "In Hungarian corpus, the NE distribution is as discussed here. The named entities occurred at the beginning of the sentence is 2175 instances and in 26 instances named entities occurred at the end of the sentence, punctuations preceded the NE in 878 times and 1861 times punctuations succeed the NE, nouns preceded the NE in 845 times and succeeded the NE in 2053 times. The postpositions occurred before NE in 148 instances. The determiners preceding the named entities in 1788 instances. The finite verbs succeed the NE in 751 instances, prepositions and conjunctions occurred at 220 and 439 instances respectively.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Analysis of common Linguistic features", | |
| "sec_num": "4.1" | |
| }, | |
| { | |
| "text": "We have analysed the corpus for the various part of speech which is associated with the named entities. In the window of three, the following are the grammatical features that occurred. Also, the Typographical features also arrive through the analysis. From the above analysis, we arrived at the following points The results obtained Indo-Aryan languages using linguistic feature are discussed in this section. The precision and recall achieved for Hindi is 80.12% and 83.1% respectively. Bengali has obtained the f-score of 85%. Punjabi scored the precision of 80.54% and recall of 83.45%. Marathi has achieved the f-measure of 82.57%.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Analysis of common Linguistic features", | |
| "sec_num": "4.1" | |
| }, | |
| { | |
| "text": "The results obtained European languages using linguistic feature are discussed in this section. The precision and recall achieved for English is 84% and 80% respectively. Spanish has obtained the f-score of 85%. Dutch scored the precision of 90.3% and recall of 92.23%. Hungarian has achieved the f-measure of 84.57%. German has obtained the precision of 81%, recall of 72% and f-measure of 76% respectively.", | |
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| "section": "Analysis of common Linguistic features", | |
| "sec_num": "4.1" | |
| }, | |
| { | |
| "text": "The different feature combinations shown in Gayen et al. (2014) has participated in ICON NER shared task and built a named entity system for English and 6 Indian languages using HMM. In comparison with the performance reported by Gayen et al. (2014) , except Bengali we have achieved the highest f-score for all the Indian languages. Abinaya et al. (2014) has participated in FIRE 2014 shared task and developed a NE system for English and 3 Indian Languages. As reported in FIRE 2014 NER task overview paper (Pattabhi et al., 2014) , the results given in table 6 are obtained by Abinaya et al. for maximal entities. They have implemented CRFs for English and SVM for other languages. The present system achieved the better scores than Abinaya et al. The language Independent (LI) NE system has developed for Hindi and Bengali using CRFs by Ekbal et al. (2009) . The results attained by the present work in Bengali and Hindi languages are higher than Ekbal et al. (2009) . But the NE system developed using language specific features by Ekbal et al. (2009) are performing better than the present system. Florian et al. (2003) participated in CONLL 2002 NER shared task and obtained 79.05% for Spanish and 74.99% for Dutch. The present system obtained 85% and 91% f-measure for Spanish and Dutch respectively.", | |
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| "FIGREF0": { | |
| "text": "Language Families Used in this work", | |
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| "TABREF2": { | |
| "content": "<table><tr><td colspan=\"3\">The Hungarian corpus has 400K tokens and 7,068 named</td></tr><tr><td>entities.</td><td/><td/></tr><tr><td colspan=\"3\">Languages Tokens Sentences NEs</td></tr><tr><td>English</td><td>2,56,426 14,002</td><td>25,671</td></tr><tr><td>Dutch</td><td>2,47,820 15,316</td><td>27,390</td></tr><tr><td colspan=\"2\">Hungarian 4,44,661 27,673</td><td>7,068</td></tr><tr><td>German</td><td>5,91,005 31,298</td><td>33,399</td></tr><tr><td>Spanish</td><td>3,17,637 10,238</td><td>23,148</td></tr></table>", | |
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| "text": "Corpus statistics (Indian Languages)The NE corpus used for Spanish and Dutch languages is obtained from CONLL 2003 NER shared task. The Spanish and Dutch corpus contains person, location, organization and miscellaneous NE tags. For German language, the GERMEVAL NER shared task data has been utilized. The German NE corpus has 12 NE tags and mainly has four classes. The number of tokens and named entities in the English dataset are 200K and 25K respectively. The Spanish and Dutch corpus consists of 300K and 200K tokens. The numbers of named entities in Spanish and Dutch dataset are 23,148 and 27,390. The German dataset consists of 500K tokens, 31K sentences and 33,399 NEs.", | |
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| "content": "<table><tr><td colspan=\"4\">: Results for Dravidian Languages</td></tr><tr><td>Languages</td><td>PRE</td><td>REC</td><td>F-M</td></tr><tr><td>Hindi</td><td colspan=\"2\">81.05 83.13</td><td>82.07</td></tr><tr><td>Bengali</td><td colspan=\"2\">82.78 89.31</td><td>85.92</td></tr><tr><td>Punjabi</td><td colspan=\"2\">80.54 83.45</td><td>81.96</td></tr><tr><td>Marathi</td><td colspan=\"2\">78.32 87.32</td><td>82.57</td></tr></table>", | |
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| "content": "<table><tr><td colspan=\"4\">: Results for Indo-Aryan Languages</td></tr><tr><td>Languages</td><td>PRE</td><td>REC</td><td>F-M</td></tr><tr><td>English</td><td colspan=\"3\">84.32 80.35 82.28</td></tr><tr><td>Spanish</td><td colspan=\"3\">86.13 84.37 85.24</td></tr><tr><td>Dutch</td><td>90.3</td><td colspan=\"2\">92.23 91.25</td></tr><tr><td>Hungarian</td><td colspan=\"3\">83.84 85.21 84.51</td></tr><tr><td>German</td><td colspan=\"3\">81.41 72.99 76.97</td></tr></table>", | |
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| "content": "<table><tr><td>: Results for European Languages</td></tr><tr><td>The linguistic feature yielded the precision and recall of</td></tr><tr><td>80.12% and 83.1% for Tamil, 70.63% precision and</td></tr><tr><td>74.82% recall for Malayalam and 69.40% precision score</td></tr><tr><td>and 57.25% recall value for Telugu. The f-score obtained</td></tr><tr><td>by Dravidian languages are 81% for Tamil, 72% for</td></tr><tr><td>Malayalam and 62.74% in Telugu.</td></tr></table>", | |
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| "TABREF8": { | |
| "content": "<table><tr><td>-5</td></tr></table>", | |
| "html": null, | |
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| "text": "", | |
| "num": null | |
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| "TABREF9": { | |
| "content": "<table/>", | |
| "html": null, | |
| "type_str": "table", | |
| "text": "Comparison with existing worksThough the present work is about multilingual named entities, we have compared our work with the existing multilingual NER works.", | |
| "num": null | |
| }, | |
| "TABREF10": { | |
| "content": "<table><tr><td colspan=\"5\">Brill, Eric. (1992). A simple rule-based part of speech</td></tr><tr><td colspan=\"5\">tagger. In Proceedings of the third conference on Applied</td></tr><tr><td>natural</td><td>language</td><td>processing.</td><td>Association</td><td>for</td></tr><tr><td colspan=\"3\">Computational Linguistics.</td><td/><td/></tr><tr><td>Boj\u00f3rquez,</td><td/><td/><td/><td/></tr></table>", | |
| "html": null, | |
| "type_str": "table", | |
| "text": "Abinaya, N. Neethu, J. Barathi, H.B.G. Anand, M.K. & Soman, K.P. (2014). AMRITA CEN@ FIRE-2014: Named Entity Recognition for Indian Languages using Rich Features. In Proceedings of the Forum for Information Retrieval Evaluation, pp. 103-111 Antony, J.B. & Mahalakshmi, G.S. (2014). Named entity recognition for Tamil biomedical documents. In Proceedings of the 2014 International Conference on Circuits, Power and Computing Technologies, ICCPCT-2014, pp. 1571-1577. Athavale, V. Bharadwaj, S., Pamecha, M., Prabhu, A. & Shrivastava, M. (2016). Towards deep learning in hindi ner: An approach to tackle the labelled data scarcity. arXiv preprint arXiv:1610.09756. Salvador, S. & V\u0131ctor, M.G. (2015). Semi-Supervised Approach to Named Entity Recognition in Spanish Applied to a Real-World Conversational System. Pattern Recognition: 7th Mexican Conference, MCPR 2015, Mexico City, Mexico, vol. 9116, pp. 24-27.", | |
| "num": null | |
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