21 lines
34 KiB
Python
21 lines
34 KiB
Python
text = "1 Introduction The publication rate in the medical and biomedical sciences is growing at an exponential rate (Bornmann and Mutz, 2014). The information overload problem is widespread across academia, but is particularly apparent in the biomedical sciences, where individual papers may contain specific discoveries relating to a dizzying variety of genes, drugs, and proteins. In order to cope with the sheer volume of new scientific knowledge, there have been many attempts to automate the process of extracting entities, relations, protein interactions and other structured knowledge from scientific papers (Wei et al., 2016; Ammar et al., 2018; Poon et al., 2014). Although there exists a wealth of tools for processing biomedical text, many focus primarily on entity linking, negation detection and abbreviation detection. MetaMap and MetaMapLite (Aronson, 2001; Demner-Fushman et al., 2017), the two most widely used and supported tools for biomedical text processing, consider additional features, such as negation detection and acronym resolution. However, tools which cover more classical natural language processing (NLP) tasks such as the GENIA tagger (Tsuruoka et al., 2005; Tsuruoka and Tsujii, 2005) and phrase structure parsers such as those presented in (McClosky and Charniak, 2008) typically do not make use of new research innovations such as word representations or neural networks. In this paper, we introduce scispaCy, a specialized NLP library for processing biomedical texts which builds on the robust spaCy library,1 and document its performance relative to state of the art models for part of speech (POS) tagging, dependency parsing, named entity recognition (NER) and sentence segmentation. Specifically, we: • Release a reformatted version of the GENIA 1.0 (Kim et al., 2003) corpus converted into Universal Dependencies v1.0 and aligned 1spacy.io ar X iv :1 90 2. 07 66 9v 2 [ cs .C L ] 2 1 Fe b 20 19 with the original text from the PubMed abstracts. • Benchmark 9 named entity recognition models for more specific entity extraction applications demonstrating competitive performance when compared to strong baselines. • Release and evaluate two fast and convenient pipelines for biomedical text, which include tokenization, part of speech tagging, dependency parsing and named entity recognition. 2 Overview of (sci)spaCy In this section, we briefly describe the models used in the spaCy library and describe how we build on them in scispaCy. spaCy. The spaCy library (Honnibal and Montani, 2017)2 provides a variety of practical tools for text processing in multiple languages. Their models have emerged as the defacto standard for practical NLP due to their speed, robustness and close to state of the art performance. As the spaCy models are popular and the spaCy API is widely known to many potential users, we choose to build upon the spaCy library for creating a biomedical text processing pipeline. scispaCy. Our goal is to develop scispaCy as a robust, efficient and performant NLP library to satisfy the primary text processing needs in the biomedical domain. In this release of scispaCy, we retrain spaCy3 models for POS tagging, dependency parsing, and NER using datasets relevant to biomedical text, and enhance the tokenization module with additional rules. scispaCy contains two core released packages: en core sci sm and en core sci md. Models in the en core sci md package have a larger vocabulary and include word vectors, while those in en core sci sm have a smaller vocabulary and do not include word vectors, as shown in Table 1. Processing Speed. To emphasize the efficiency and practical utility of the end-to-end pipeline provided by scispaCy packages, we perform a speed comparison with several other publicly available processing pipelines for biomedical text using 10k randomly selected PubMed abstracts. We report 2Source code at https://github.com/ explosion/spaCy 3scispaCy models are based on spaCy version 2.0.18 results with and without segmenting the abstracts into sentences since some of the libraries (e.g., GENIA tagger) are designed to operate on sentences. As shown in Table 2, both models released in scispaCy demonstrate competitive speed to pipelines written in C++ and Java, languages designed for production settings. Whilst scispaCy is not as fast as pipelines designed for purely production use-cases (e.g., NLP4J), it has the benefit of straightforward integration with the large ecosystem of Python libraries for machine learning and text processing. Although the comparison in Table 2 is not an apples to apples comparison with other frameworks (different tasks, implementation languages etc), it is useful to understand scispaCy’s runtime in the context of other pipeline components. Running scispaCy models in addition to standard Entity Linking software such as MetaMap would result in only a marginal increase in overall runtime. In the following section, we describe the POS taggers and dependency parsers in scispaCy. 3 POS Tagging and Dependency Parsing The joint POS tagging and dependency parsing model in spaCy is an arc-eager transition-based parser trained with a dynamic oracle, similar to (Goldberg and Nivre, 2012). Features are CNN representations of token features and shared across all pipeline models (Kiperwasser and Goldberg, 2016; Zhang and Weiss, 2016). Next, we describe the data we used to train it in scispaCy. 3.1 Datasets GENIA 1.0 Dependencies. To train the dependency parser and part of speech tagger in both released models, we convert the treebank of (McClosky and Charniak, 2008),4 which is based on the GENIA 1.0 corpus (Kim et al., 2003), to Universal Dependencies v1.0 using the Stanford Dependency Converter (Schuster and Manning, 2016). As this dataset has POS tags annotated, we use it to train the POS tagger jointly with the dependency parser in both released models. As we believe the Universal Dependencies converted from the original GENIA 1.0 corpus are generally useful, we have released them as a separate contribution of this paper.5 In this data release, we also align the converted dependency parses to their original text spans in the raw, untokenized abstracts from the original release,6 and include the PubMed metadata for the abstracts which was discarded in the GENIA corpus released by McClosky and Charniak (2008). We hope that this raw format can emerge as a resource for practical evaluation in the biomedical domain of core NLP tasks such as tokenization, sentence segmentation and joint models of syntax. Finally, we also retrieve from PubMed the original metadata associated with each abstract. This includes relevant named entities linked to their Medical Subject Headings (MeSH terms) as well as chemicals and drugs linked to a variety of ontologies, as well as author metadata, publication dates, citation statistics and journal metadata. We hope that the community can find interesting problems for which such natural supervision can be used. 4https://nlp.stanford.edu/˜mcclosky/ biomedical.html 5Available at https://github.com/allenai/ genia-dependency-trees 6Available at http://www.geniaproject.org/ OntoNotes 5.0. To increase the robustness of the dependency parser and POS tagger to generic text, we make use of the OntoNotes 5.0 corpus7 when training the dependency parser and part of speech tagger (Weischedel et al., 2011; Hovy et al., 2006). The OntoNotes corpus consists of multiple genres of text, annotated with syntactic and semantic information, but we only use POS and dependency parsing annotations in this work. 3.2 Experiments We compare our models to the recent survey study of dependency parsing and POS tagging for biomedical data (Nguyen and Verspoor, 2018) in Tables 3 and 4. POS tagging results show that both models released in scispaCy are competitive with state of the art systems, and can be considered of equivalent practical value. In the case of dependency parsing, we find that the Biaffine parser of (Dozat and Manning, 2016) outperforms the scispaCy models by a margin of 2-3%. However, as demonstrated in Table 2, the scispaCy models are 7Instructions for download at http://cemantix. org/data/ontonotes.html approximately 9x faster due to the speed optimizations in spaCy. Robustness to Web Data. A core principle of the scispaCy models is that they are useful on a wide variety of types of text with a biomedical focus, such as clinical notes, academic papers, clinical trials reports and medical records. In order to make our models robust across a wider range of domains more generally, we experiment with incorporating training data from the OntoNotes 5.0 corpus when training the dependency parser and POS tagger. Figure 2 demonstrates the effectiveness of adding increasing percentages of web data, showing substantially improved performance on OntoNotes, at no reduction in performance on biomedical text. Note that mixing in web text during training has been applied to previous systems - the GENIA Tagger (Tsuruoka et al., 2005) also employs this technique. 4 Named Entity Recognition The NER model in spaCy is a transition-based system based on the chunking model from (Lample et al., 2016). Tokens are represented as hashed, embedded representations of the prefix, suffix, shape and lemmatized features of individual words. Next, we describe the data we used to train NER models in scispaCy. 4.1 Datasets The main NER model in both released packages in scispaCy is trained on the mention spans in the MedMentions dataset (Murty et al., 2018). Since the MedMentions dataset was originally designed for entity linking, this model recognizes a wide variety of entity types, as well as non-standard syntactic phrases such as verbs and modifiers, but the model does not predict the entity type. In order to provide for users with more specific requirements around entity types, we release four additional packages en ner {bc5cdr|craft |jnlpba|bionlp13cg} md with finer-grained NER models trained on BC5CDR (for chemicals and diseases; Li et al., 2016), CRAFT (for cell types, chemicals, proteins, genes; Bada et al., 2011), JNLPBA (for cell lines, cell types, DNAs, RNAs, proteins; Collier and Kim, 2004) and BioNLP13CG (for cancer genetics; Pyysalo et al., 2015), respectively. 4.2 Experiments As NER is a key task for other biomedical text processing tasks, we conduct a through evaluation of the suitability of scispaCy to provide baseline performance across a wide variety of datasets. In particular, we retrain the spaCy NER model on each of the four datasets mentioned earlier (BC5CDR, CRAFT, JNLPBA, BioNLP13CG) as well as five more datasets in Crichton et al. (2017): AnatEM, BC2GM, BC4CHEMD, Linnaeus, NCBI-Disease. These datasets cover a wide variety of entity types required by different biomedical domains, including cancer genetics, disease-drug interactions, pathway analysis and trial population extraction. Additionally, they vary considerably in size and number of entities. For example, BC4CHEMD (Krallinger et al., 2015) has 84,310 annotations while Linnaeus (Gerner et al., 2009) only has 4,263. BioNLP13CG (Pyysalo et al., 2015) annotates 16 entity types while five of the datasets only annotate a single entity type.8 Table 5 provides a through comparison of the scispaCy NER models compared to a variety of models. In particular, we compare the models to strong baselines which do not consider the use of 1) multi-task learning across multiple datasets and 2) semi-supervised learning via large pretrained language models. Overall, we find that the scispaCy models are competitive baselines for 5 of the 9 datasets. Additionally, in Table 6 we evaluate the recall of the pipeline mention detector available in both 8For a detailed discussion of the datasets and their creation, we refer the reader to https://github.com/ cambridgeltl/MTL-Bioinformatics-2016/ blob/master/Additional%20file%201.pdf scispaCy models (trained on the MedMentions dataset) against all 9 specialised NER datasets. Overall, we observe a modest drop in average recall when compared directly to the MedMentions results in Table 7, but considering the diverse domains of the 9 specialised NER datasets, achieving this level of recall across datasets is already nontrivial. 5 Sentence Segmentation and Citation Handling Accurate sentence segmentation is required for many practical applications of natural language processing. Biomedical data presents many difficulties for standard sentence segmentation algorithms: abbreviated names and noun compounds containing punctuation are more common, whilst the wide range of citation styles can easily be misidentified as sentence boundaries. We evaluate sentence segmentation using both sentence and full-abstract accuracy when segmenting PubMed abstracts from the raw, untokenized GENIA development set (the Sent/Abstract columns in Table 8). Additionally, we examine the ability of the segmentation learned by our model to generalise to the body text of PubMed articles. Body text is typically more complex than abstract text, but in particular, it contains citations, which are considerably less frequent in abstract text. In order to examine the effectiveness of our models in this scenario, we design the following synthetic experiment. Given sentences from (Anonymous, 2019)9 which were originally designed for citation intent prediction, we run these sentences individually through our models. As we know that these sentences should be single sentences, we can simply count the frequency with which our models segment the individual sentences containing citations into multiple sentences (the Citation column in Table 8). As demonstrated by Table 8, training the dependency parser on in-domain data (both the scispaCy models) completely obviates the need for rule-based sentence segmentation. This is a positive result - rule based sentence segmentation is a brittle, time consuming process, which we have replaced with a domain specific version of an existing pipeline component. Both scispaCy models are released with the custom tokeniser, but without a custom sentence segmenter by default. 6 Related Work Apache cTakes (Savova et al., 2010) was designed specifically for clinical notes rather than the broader biomedical domain. MetaMap and MetaMapLite (Aronson, 2001; Demner-Fushman et al., 2017) from the National Library of 9Paper currently under review. Medicine focus specifically on entity linking using the Unified Medical Language System (UMLS) (Bodenreider, 2004) as a knowledge base. (Buyko et al.) adapt Apache OpenNLP using the GENIA corpus, but their system is not openly available and is less suitable for modern, Python-based workflows. The GENIA Tagger (Tsuruoka et al., 2005) provides the closest comparison to scispaCy due to it’s multi-stage pipeline, integrated research contributions and production quality runtime. We improve on the GENIA Tagger by adding a full dependency parser rather than just noun chunking, as well as improved results for NER without compromising significantly on speed. In more fundamental NLP research, the GENIA corpus (Kim et al., 2003) has been widely used to evaluate transfer learning and domain adaptation. (McClosky et al., 2006) demonstrate the effectiveness of self-training and parse re-ranking for domain adaptation. (Rimell and Clark, 2008) adapt a CCG parser using only POS and lexical categories, while (Joshi et al., 2018) extend a neural phrase structure parser trained on web text to the biomedical domain with a small number of partially annotated examples. These papers focus mainly of the problem of domain adaptation itself, rather than the objective of obtaining a robust, high-performance parser using existing resources. NLP techniques, and in particular, distant supervision have been employed to assist the curation of large, structured biomedical resources. (Poon et al., 2015) extract 1.5 million cancer path- way interactions from PubMed abstracts, leading to the development of Literome (Poon et al., 2014), a search engine for genic pathway interactions and genotype-phenotype interactions. A fundamental aspect of (Valenzuela-Escarcega et al., 2018; Poon et al., 2014) is the use of hand-written rules and triggers for events based on dependency tree paths; the connection to the application of scispaCy is quite apparent. 7 Conclusion In this paper we presented several robust model pipelines for a variety of natural language processing tasks focused on biomedical text. The scispaCy models are fast, easy to use, scalable, and achieve close to state of the art performance. We hope that the release of these models enables new applications in biomedical information extraction whilst making it easy to leverage high quality syntactic annotation for downstream tasks. Additionally, we released a reformatted GENIA 1.0 corpus augmented with automatically produced Universal Dependency annotations and recovered and aligned original abstract metadata. 1 Introduction The publication rate in the medical and biomedical sciences is growing at an exponential rate (Bornmann and Mutz, 2014). The information overload problem is widespread across academia, but is particularly apparent in the biomedical sciences, where individual papers may contain specific discoveries relating to a dizzying variety of genes, drugs, and proteins. In order to cope with the sheer volume of new scientific knowledge, there have been many attempts to automate the process of extracting entities, relations, protein interactions and other structured knowledge from scientific papers (Wei et al., 2016; Ammar et al., 2018; Poon et al., 2014). Although there exists a wealth of tools for processing biomedical text, many focus primarily on entity linking, negation detection and abbreviation detection. MetaMap and MetaMapLite (Aronson, 2001; Demner-Fushman et al., 2017), the two most widely used and supported tools for biomedical text processing, consider additional features, such as negation detection and acronym resolution. However, tools which cover more classical natural language processing (NLP) tasks such as the GENIA tagger (Tsuruoka et al., 2005; Tsuruoka and Tsujii, 2005) and phrase structure parsers such as those presented in (McClosky and Charniak, 2008) typically do not make use of new research innovations such as word representations or neural networks. In this paper, we introduce scispaCy, a specialized NLP library for processing biomedical texts which builds on the robust spaCy library,1 and document its performance relative to state of the art models for part of speech (POS) tagging, dependency parsing, named entity recognition (NER) and sentence segmentation. Specifically, we: • Release a reformatted version of the GENIA 1.0 (Kim et al., 2003) corpus converted into Universal Dependencies v1.0 and aligned 1spacy.io ar X iv :1 90 2. 07 66 9v 2 [ cs .C L ] 2 1 Fe b 20 19 with the original text from the PubMed abstracts. • Benchmark 9 named entity recognition models for more specific entity extraction applications demonstrating competitive performance when compared to strong baselines. • Release and evaluate two fast and convenient pipelines for biomedical text, which include tokenization, part of speech tagging, dependency parsing and named entity recognition. 2 Overview of (sci)spaCy In this section, we briefly describe the models used in the spaCy library and describe how we build on them in scispaCy. spaCy. The spaCy library (Honnibal and Montani, 2017)2 provides a variety of practical tools for text processing in multiple languages. Their models have emerged as the defacto standard for practical NLP due to their speed, robustness and close to state of the art performance. As the spaCy models are popular and the spaCy API is widely known to many potential users, we choose to build upon the spaCy library for creating a biomedical text processing pipeline. scispaCy. Our goal is to develop scispaCy as a robust, efficient and performant NLP library to satisfy the primary text processing needs in the biomedical domain. In this release of scispaCy, we retrain spaCy3 models for POS tagging, dependency parsing, and NER using datasets relevant to biomedical text, and enhance the tokenization module with additional rules. scispaCy contains two core released packages: en core sci sm and en core sci md. Models in the en core sci md package have a larger vocabulary and include word vectors, while those in en core sci sm have a smaller vocabulary and do not include word vectors, as shown in Table 1. Processing Speed. To emphasize the efficiency and practical utility of the end-to-end pipeline provided by scispaCy packages, we perform a speed comparison with several other publicly available processing pipelines for biomedical text using 10k randomly selected PubMed abstracts. We report 2Source code at https://github.com/ explosion/spaCy 3scispaCy models are based on spaCy version 2.0.18 results with and without segmenting the abstracts into sentences since some of the libraries (e.g., GENIA tagger) are designed to operate on sentences. As shown in Table 2, both models released in scispaCy demonstrate competitive speed to pipelines written in C++ and Java, languages designed for production settings. Whilst scispaCy is not as fast as pipelines designed for purely production use-cases (e.g., NLP4J), it has the benefit of straightforward integration with the large ecosystem of Python libraries for machine learning and text processing. Although the comparison in Table 2 is not an apples to apples comparison with other frameworks (different tasks, implementation languages etc), it is useful to understand scispaCy’s runtime in the context of other pipeline components. Running scispaCy models in addition to standard Entity Linking software such as MetaMap would result in only a marginal increase in overall runtime. In the following section, we describe the POS taggers and dependency parsers in scispaCy. 3 POS Tagging and Dependency Parsing The joint POS tagging and dependency parsing model in spaCy is an arc-eager transition-based parser trained with a dynamic oracle, similar to (Goldberg and Nivre, 2012). Features are CNN representations of token features and shared across all pipeline models (Kiperwasser and Goldberg, 2016; Zhang and Weiss, 2016). Next, we describe the data we used to train it in scispaCy. 3.1 Datasets GENIA 1.0 Dependencies. To train the dependency parser and part of speech tagger in both released models, we convert the treebank of (McClosky and Charniak, 2008),4 which is based on the GENIA 1.0 corpus (Kim et al., 2003), to Universal Dependencies v1.0 using the Stanford Dependency Converter (Schuster and Manning, 2016). As this dataset has POS tags annotated, we use it to train the POS tagger jointly with the dependency parser in both released models. As we believe the Universal Dependencies converted from the original GENIA 1.0 corpus are generally useful, we have released them as a separate contribution of this paper.5 In this data release, we also align the converted dependency parses to their original text spans in the raw, untokenized abstracts from the original release,6 and include the PubMed metadata for the abstracts which was discarded in the GENIA corpus released by McClosky and Charniak (2008). We hope that this raw format can emerge as a resource for practical evaluation in the biomedical domain of core NLP tasks such as tokenization, sentence segmentation and joint models of syntax. Finally, we also retrieve from PubMed the original metadata associated with each abstract. This includes relevant named entities linked to their Medical Subject Headings (MeSH terms) as well as chemicals and drugs linked to a variety of ontologies, as well as author metadata, publication dates, citation statistics and journal metadata. We hope that the community can find interesting problems for which such natural supervision can be used. 4https://nlp.stanford.edu/˜mcclosky/ biomedical.html 5Available at https://github.com/allenai/ genia-dependency-trees 6Available at http://www.geniaproject.org/ OntoNotes 5.0. To increase the robustness of the dependency parser and POS tagger to generic text, we make use of the OntoNotes 5.0 corpus7 when training the dependency parser and part of speech tagger (Weischedel et al., 2011; Hovy et al., 2006). The OntoNotes corpus consists of multiple genres of text, annotated with syntactic and semantic information, but we only use POS and dependency parsing annotations in this work. 3.2 Experiments We compare our models to the recent survey study of dependency parsing and POS tagging for biomedical data (Nguyen and Verspoor, 2018) in Tables 3 and 4. POS tagging results show that both models released in scispaCy are competitive with state of the art systems, and can be considered of equivalent practical value. In the case of dependency parsing, we find that the Biaffine parser of (Dozat and Manning, 2016) outperforms the scispaCy models by a margin of 2-3%. However, as demonstrated in Table 2, the scispaCy models are 7Instructions for download at http://cemantix. org/data/ontonotes.html approximately 9x faster due to the speed optimizations in spaCy. Robustness to Web Data. A core principle of the scispaCy models is that they are useful on a wide variety of types of text with a biomedical focus, such as clinical notes, academic papers, clinical trials reports and medical records. In order to make our models robust across a wider range of domains more generally, we experiment with incorporating training data from the OntoNotes 5.0 corpus when training the dependency parser and POS tagger. Figure 2 demonstrates the effectiveness of adding increasing percentages of web data, showing substantially improved performance on OntoNotes, at no reduction in performance on biomedical text. Note that mixing in web text during training has been applied to previous systems - the GENIA Tagger (Tsuruoka et al., 2005) also employs this technique. 4 Named Entity Recognition The NER model in spaCy is a transition-based system based on the chunking model from (Lample et al., 2016). Tokens are represented as hashed, embedded representations of the prefix, suffix, shape and lemmatized features of individual words. Next, we describe the data we used to train NER models in scispaCy. 4.1 Datasets The main NER model in both released packages in scispaCy is trained on the mention spans in the MedMentions dataset (Murty et al., 2018). Since the MedMentions dataset was originally designed for entity linking, this model recognizes a wide variety of entity types, as well as non-standard syntactic phrases such as verbs and modifiers, but the model does not predict the entity type. In order to provide for users with more specific requirements around entity types, we release four additional packages en ner {bc5cdr|craft |jnlpba|bionlp13cg} md with finer-grained NER models trained on BC5CDR (for chemicals and diseases; Li et al., 2016), CRAFT (for cell types, chemicals, proteins, genes; Bada et al., 2011), JNLPBA (for cell lines, cell types, DNAs, RNAs, proteins; Collier and Kim, 2004) and BioNLP13CG (for cancer genetics; Pyysalo et al., 2015), respectively. 4.2 Experiments As NER is a key task for other biomedical text processing tasks, we conduct a through evaluation of the suitability of scispaCy to provide baseline performance across a wide variety of datasets. In particular, we retrain the spaCy NER model on each of the four datasets mentioned earlier (BC5CDR, CRAFT, JNLPBA, BioNLP13CG) as well as five more datasets in Crichton et al. (2017): AnatEM, BC2GM, BC4CHEMD, Linnaeus, NCBI-Disease. These datasets cover a wide variety of entity types required by different biomedical domains, including cancer genetics, disease-drug interactions, pathway analysis and trial population extraction. Additionally, they vary considerably in size and number of entities. For example, BC4CHEMD (Krallinger et al., 2015) has 84,310 annotations while Linnaeus (Gerner et al., 2009) only has 4,263. BioNLP13CG (Pyysalo et al., 2015) annotates 16 entity types while five of the datasets only annotate a single entity type.8 Table 5 provides a through comparison of the scispaCy NER models compared to a variety of models. In particular, we compare the models to strong baselines which do not consider the use of 1) multi-task learning across multiple datasets and 2) semi-supervised learning via large pretrained language models. Overall, we find that the scispaCy models are competitive baselines for 5 of the 9 datasets. Additionally, in Table 6 we evaluate the recall of the pipeline mention detector available in both 8For a detailed discussion of the datasets and their creation, we refer the reader to https://github.com/ cambridgeltl/MTL-Bioinformatics-2016/ blob/master/Additional%20file%201.pdf scispaCy models (trained on the MedMentions dataset) against all 9 specialised NER datasets. Overall, we observe a modest drop in average recall when compared directly to the MedMentions results in Table 7, but considering the diverse domains of the 9 specialised NER datasets, achieving this level of recall across datasets is already nontrivial. 5 Sentence Segmentation and Citation Handling Accurate sentence segmentation is required for many practical applications of natural language processing. Biomedical data presents many difficulties for standard sentence segmentation algorithms: abbreviated names and noun compounds containing punctuation are more common, whilst the wide range of citation styles can easily be misidentified as sentence boundaries. We evaluate sentence segmentation using both sentence and full-abstract accuracy when segmenting PubMed abstracts from the raw, untokenized GENIA development set (the Sent/Abstract columns in Table 8). Additionally, we examine the ability of the segmentation learned by our model to generalise to the body text of PubMed articles. Body text is typically more complex than abstract text, but in particular, it contains citations, which are considerably less frequent in abstract text. In order to examine the effectiveness of our models in this scenario, we design the following synthetic experiment. Given sentences from (Anonymous, 2019)9 which were originally designed for citation intent prediction, we run these sentences individually through our models. As we know that these sentences should be single sentences, we can simply count the frequency with which our models segment the individual sentences containing citations into multiple sentences (the Citation column in Table 8). As demonstrated by Table 8, training the dependency parser on in-domain data (both the scispaCy models) completely obviates the need for rule-based sentence segmentation. This is a positive result - rule based sentence segmentation is a brittle, time consuming process, which we have replaced with a domain specific version of an existing pipeline component. Both scispaCy models are released with the custom tokeniser, but without a custom sentence segmenter by default. 6 Related Work Apache cTakes (Savova et al., 2010) was designed specifically for clinical notes rather than the broader biomedical domain. MetaMap and MetaMapLite (Aronson, 2001; Demner-Fushman et al., 2017) from the National Library of 9Paper currently under review. Medicine focus specifically on entity linking using the Unified Medical Language System (UMLS) (Bodenreider, 2004) as a knowledge base. (Buyko et al.) adapt Apache OpenNLP using the GENIA corpus, but their system is not openly available and is less suitable for modern, Python-based workflows. The GENIA Tagger (Tsuruoka et al., 2005) provides the closest comparison to scispaCy due to it’s multi-stage pipeline, integrated research contributions and production quality runtime. We improve on the GENIA Tagger by adding a full dependency parser rather than just noun chunking, as well as improved results for NER without compromising significantly on speed. In more fundamental NLP research, the GENIA corpus (Kim et al., 2003) has been widely used to evaluate transfer learning and domain adaptation. (McClosky et al., 2006) demonstrate the effectiveness of self-training and parse re-ranking for domain adaptation. (Rimell and Clark, 2008) adapt a CCG parser using only POS and lexical categories, while (Joshi et al., 2018) extend a neural phrase structure parser trained on web text to the biomedical domain with a small number of partially annotated examples. These papers focus mainly of the problem of domain adaptation itself, rather than the objective of obtaining a robust, high-performance parser using existing resources. NLP techniques, and in particular, distant supervision have been employed to assist the curation of large, structured biomedical resources. (Poon et al., 2015) extract 1.5 million cancer path- way interactions from PubMed abstracts, leading to the development of Literome (Poon et al., 2014), a search engine for genic pathway interactions and genotype-phenotype interactions. A fundamental aspect of (Valenzuela-Escarcega et al., 2018; Poon et al., 2014) is the use of hand-written rules and triggers for events based on dependency tree paths; the connection to the application of scispaCy is quite apparent. 7 Conclusion In this paper we presented several robust model pipelines for a variety of natural language processing tasks focused on biomedical text. The scispaCy models are fast, easy to use, scalable, and achieve close to state of the art performance. We hope that the release of these models enables new applications in biomedical information extraction whilst making it easy to leverage high quality syntactic annotation for downstream tasks. Additionally, we released a reformatted GENIA 1.0 corpus augmented with automatically produced Universal Dependency annotations and recovered and aligned original abstract metadata."
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import pysbd
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import time
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import cProfile
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from tqdm import tqdm
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segmenter = pysbd.Segmenter(language='en', clean=False)
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n_trials = 10
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times = []
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for i in tqdm(range(n_trials)):
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start = time.time()
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# segments = cProfile.run('segmenter.segment(text)')
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segments = segmenter.segment(text)
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end = time.time()
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times.append(end-start)
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print("Total seconds {}".format(sum(times)))
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print("Num trials {}".format(n_trials))
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print("Average second {}".format(sum(times)/n_trials)) |