Entities In Text Research Articles

Transformation of Pathology Reports Into the Common Data Model With Oncology Module: Use Case for Colon Cancer.

BackgroundCommon data models (CDMs) help standardize electronic health record data and facilitate outcome analysis for observational and longitudinal research. An analysis of pathology reports is required to establish fundamental information infrastructure for data-driven colon cancer research. The Observational Medical Outcomes Partnership (OMOP) CDM is used in distributed research networks for clinical data; however, it requires conversion of free text–based pathology reports into the CDM’s format. There are few use cases of representing cancer data in CDM.ObjectiveIn this study, we aimed to construct a CDM database of colon cancer–related pathology with natural language processing (NLP) for a research platform that can utilize both clinical and omics data. The essential text entities from the pathology reports are extracted, standardized, and converted to the OMOP CDM format in order to utilize the pathology data in cancer research.MethodsWe extracted clinical text entities, mapped them to the standard concepts in the Observational Health Data Sciences and Informatics vocabularies, and built databases and defined relations for the CDM tables. Major clinical entities were extracted through NLP on pathology reports of surgical specimens, immunohistochemical studies, and molecular studies of colon cancer patients at a tertiary general hospital in South Korea. Items were extracted from each report using regular expressions in Python. Unstructured data, such as text that does not have a pattern, were handled with expert advice by adding regular expression rules. Our own dictionary was used for normalization and standardization to deal with biomarker and gene names and other ungrammatical expressions. The extracted clinical and genetic information was mapped to the Logical Observation Identifiers Names and Codes databases and the Systematized Nomenclature of Medicine (SNOMED) standard terminologies recommended by the OMOP CDM. The database-table relationships were newly defined through SNOMED standard terminology concepts. The standardized data were inserted into the CDM tables. For evaluation, 100 reports were randomly selected and independently annotated by a medical informatics expert and a nurse.ResultsWe examined and standardized 1848 immunohistochemical study reports, 3890 molecular study reports, and 12,352 pathology reports of surgical specimens (from 2017 to 2018). The constructed and updated database contained the following extracted colorectal entities: (1) NOTE_NLP, (2) MEASUREMENT, (3) CONDITION_OCCURRENCE, (4) SPECIMEN, and (5) FACT_RELATIONSHIP of specimen with condition and measurement.ConclusionsThis study aimed to prepare CDM data for a research platform to take advantage of all omics clinical and patient data at Seoul National University Bundang Hospital for colon cancer pathology. A more sophisticated preparation of the pathology data is needed for further research on cancer genomics, and various types of text narratives are the next target for additional research on the use of data in the CDM.

Context-sensitive gender inference of named entities in text

The gender information of named entities is an important prerequisite for many text analysis tasks such as gender bias detection and targeted advertising. Despite its valuable use cases, gender tagging of named entities has traditionally been database-reliant. The lack of open-source benchmarks is a major impediment to exploring the effectiveness of machine learning-based methods for this task. Towards this goal, the article serves two main purposes. Firstly, we create four open-source datasets from well-known NER corpora and make them publicly available. Secondly, we propose a novel supervised learning approach based on the transformer network to identify the gender of named entities. We evaluate the proposed approach on four gender identification datasets. The proposed method outperforms two commercial database-reliant approaches and five deep sequence models, including BERT.

Richpedia: A Large-Scale, Comprehensive Multi-Modal Knowledge Graph

Large-scale knowledge graphs such as Wikidata and DBpedia have become a powerful asset for semantic search and question answering. However, most of the knowledge graph construction works focus on organizing and discovering textual knowledge in a structured representation, while paying little attention to the proliferation of visual resources on the Web. To consolidate this recent trend, in this paper, we present Richpedia, aiming to provide a comprehensive multi-modal knowledge graph by distributing sufficient and diverse images to textual entities in Wikidata. We also set Resource Description Framework links (visual semantic relations) between image entities based on the hyperlinks and descriptions in Wikipedia. The Richpedia resource is accessible on the Web via a faceted query endpoint, which provides a pathway for knowledge graph and computer vision tasks, such as link prediction and visual relation detection.

Knowledge-driven joint posterior revision of named entity classification and linking

In this work we address the problem of extracting quality entity knowledge from natural language text, an important task for the automatic construction of knowledge graphs from unstructured content.More in details, we investigate the benefit of performing a joint posterior revision, driven by ontological background knowledge, of the annotations resulting from natural language processing (NLP) entity analyses such as named entity recognition and classification (NERC) and entity linking (EL). The revision is performed via a probabilistic model, called jpark, that given the candidate annotations independently identified by NERC and EL tools on the same textual entity mention, reconsiders the best annotation choice performed by the tools in light of the coherence of the candidate annotations with the ontological knowledge. The model can be explicitly instructed to handle the information that an entity can potentially be NIL (i.e., lacking a corresponding referent in the target linking knowledge base), exploiting it for predicting the best NERC and EL annotation combination.We present a comprehensive evaluation of jpark along various dimensions, comparing its performances with and without exploiting NIL information, as well as the usage of three different background knowledge resources (YAGO, DBpedia, and Wikidata) to build the model. The evaluation, conducted using different tools (the popular Stanford NER and DBpedia Spotlight, as well as the more recent Flair NER and End-to-End Neural EL) with three reference datasets (AIDA, MEANTIME, and TAC-KBP), empirically confirms the capability of the model to improve the quality of the annotations of the given tools, and thus their performances on the tasks they are designed for.

The Role of Knowledge in Determining Identity of Long-Tail Entities

Identifying entities in text is an important step of semantic analysis. Some entity mentions comprise a name or description, but many include no information that identifies them in the system’s knowledge resources, which means that their identity cannot be established through traditional disambiguation. Consequently, such NIL (not in lexicon) entities have received little attention in entity linking systems and tasks so far. However, given the non-redundancy of knowledge on NIL entities, their lack of frequency priors, their potentially extreme ambiguity, and their numerousness, they constitute an important class of long-tail entities and pose a great challenge for state-of-the-art systems. In this paper, we describe a method for imputing identifying knowledge to NILs from generalized characteristics. We enrich the locally extracted information with profile models that rely on background knowledge in Wikidata. We describe and implement two profiling machines using state-of-the-art neural models. We evaluate their intrinsic behavior and their impact on the task of determining the identity of NIL entities.

Roconnaissance des Entités Nommées En Langues Peu Dotées En Utilisant La Représentation Distributionnelle Multiligue

Named Entity Recognition (NER) is a fundamental task in many NLP applications that seek to identify and classify expressions such as people, location, and organization names. Many NER systems have been developed, but the annotated data needed for good performances are not available for low-resource languages, such as Cameroonian languages. In this paper we exploit the low frequency of named entities in text to define a new suitable cross-lingual distributional representation for named entity recognition. We build the first Ewondo (a Bantu low-resource language of Cameroon) named entities recognizer by projecting named entity tags from English using our word representation. In terms of Recall, Precision and F-score, the obtained results show the effectiveness of the proposed distributional representation of words La reconnaissance des entités nommées (REN) est une tâche fondamentale du TALN dont le but est d'identifier les expressions telles que les noms de personnes, de lieux et d'organisations dans un texte. Il existe de nos jours plusieurs systèmes de REN, cependant les données nécessaires pour les utiliser dans le traitement des langues peu dotées telles que les langues camerounaises ne sont pas disponibles. Nous exploitons le fait que les entités nommées apparaissent rarement dans les textes pour définir une nouvelle représentation distributionnelle interlingue des mots, qui soit adaptée à la REN. En utilisant notre représentation, nous projectons les entités nommées de l'anglais vers l'ewondo (une langue bantou du Cameroun); nous obtenons donc le tout premier modèle de reconnaissance des entités nommées en langue ewondo. Les résultats en terme de précision, rappel et f-mesure montrent l'efficacité de notre représentation

Deep neural network-based recognition of entities in Chinese online medical inquiry texts

It is quite challenging to correctly identify entities such as disease names, symptoms, and drugs from Chinese online medical inquiry texts. On the one hand, traditional natural language related methods cannot be directly applied to the field of online medical inquiry. Although supervised or unsupervised learning algorithms provide an entity recognition strategy for medical inquiries on online platforms, these methods either rely extensively on specific knowledge sources or artificially-designed features, or have a strong self-adaptivity, can barely obtain fairly good entity recognition outcomes, and consequently have weak generalization. On the other hand, Chinese online medical inquiry data is characterized by a large volume and excessively rich unstructured data, medical entities are indeed distributed in a sparse way, and the Chinese characters and words are quite complicated. It is difficult to establish a robust model for the recognition of entities in Chinese online medical inquiry texts. Therefore, establishing a new deep neural network (OMINer-CE) is the attempt of this paper. First, in order to form a proper feature strategy, the basic features of other tasks are introduced, while extended features, such as continuous bag of word cluster (CBOWC) feature, are constructed. Second, the feature vectors of Chinese character and word fusion are introduced to reserve all the Chinese character information of original sequences while introducing Chinese word-based semantic information. Third, a context encoding layer and a label decoding layer are introduced; On the basis of the recurrent neural network model BiLSTM, a convolutional neural network (CNN) is added to learn more key local features of Chinese word context, and the attention mechanism is used to obtain the long-distance dependency of the Chinese words to form a OMINer model. Finally, the basic and extended feature vectors are integrated into the OMINer model, and the grammatical and semantic information contained in the labeled texts is obtained from different perspectives. Therefore, it considers the feature strategies of Chinese online medical platforms, and realizes a strong self-adaptivity by utilizing the deep neural network. It is showed by the experiments that preferably good performances can be realized by combining the CE basic and extended feature vectors in the BiLSTM of the OMINer model, suggesting that the OMINer-CE model improves the performance of recognizing entities in Chinese online medical inquiry texts.

Medieval Spanish (12th-15th centuries) named entity recognition and attribute annotation system based on contextual information.

The recognition of named entities in Spanish medieval texts presents great complexity, involving specific challenges: First, the complex morphosyntactic characteristics in proper‐noun use in medieval texts. Second, the lack of strict orthographic standards. Finally, diachronic and geographical variations in Spanish from the 12th to 15th century. In this period, named entities usually appear as complex text structure. For example, it was frequent to add nicknames and information about the persons role in society and geographic origin. To tackle this complexity, named entity recognition and classification system has been implemented. The system uses contextual cues based on semantics to detect entities and assign a type. Given the occurrence of entities with attached attributes, entity contexts are also parsed to determine entity‐type‐specific dependencies for these attributes. Moreover, it uses a variant generator to handle the diachronic evolution of Spanish medieval terms from a phonetic and morphosyntactic viewpoint. The tool iteratively enriches its proper lexica, dictionaries, and gazetteers. The system was evaluated on a corpus of over 3,000 manually annotated entities of different types and periods, obtaining F1 scores between 0.74 and 0.87. Attribute annotation was evaluated for a person and role name attributes with an overall F1 of 0.75.

Object-Aware Multimodal Named Entity Recognition in Social Media Posts With Adversarial Learning

Named Entity Recognition (NER) in social media posts is challenging since texts are usually short and contexts are lacking. Most recent works show that visual information can boost the NER performance since images can provide complementary contextual information for texts. However, the image-level features ignore the mapping relations between fine-grained visual objects and textual entities, which results in error detection in entities with different types. To better exploit visual and textual information in NER, we propose an adversarial gated bilinear attention neural network (AGBAN). The model jointly extracts entity-related features from both visual objects and texts, and leverages an adversarial training to map two different representations into a shared representation. As a result, domain information contained in an image can be transferred and applied for extracting named entities in the text associated with the image. Experimental results on Tweets dataset demonstrate that our model outperforms the state-of-the-art methods. Moreover, we systematically evaluate the effectiveness of the proposed gated bilinear attention network in capturing the interactions of mutimodal features visual objects and textual words. Our results indicate that the adversarial training can effectively exploit commonalities across heterogeneous data sources, which leads to improved performance in NER when compared to models purely exploiting text data or combining the image-level visual features.

MeSH-Based Semantic Indexing Approach to Enhance Biomedical Information Retrieval

Abstract Owing to the tremendous size of electronic biomedical documents, users encounter difficulties in seeking useful biomedical information. An efficient and smart access to the relevant biomedical information has become a fundamental need. In this research paper, we set forward a novel biomedical MeSH-based semantic indexing approach to enhance biomedical information retrieval. The proposed semantic indexing approach attempts to strengthen the content representation of both documents and queries by incorporating unambiguous MeSH concepts as well as the adequate senses of ambiguous MeSH concepts. For this purpose, our proposed approach relies on a disambiguation method to identify the adequate senses of ambiguous MeSH concepts and introduces four representation enrichment strategies so as to identify the best appropriate representatives of the adequate sense in the textual entities representation. To prove its effectiveness, the proposed semantic indexing approach was evaluated by intensive experiments. These experiments were carried out on OHSUMED test collection. The results reveal that our proposal outperforms the state-of-the-art approaches and allow us to highlight the most effective strategy.

The Epistemology of the “Great Invisibles”

Abstract The central collective myth of surrealism, Les grands transparents, was designed by André Breton in 1947 as a means for imagining a desirable society through effecting a vitalizing sense of the unknown and a “decentering of man”. As a contribution to the recent re-examination of surrealism in view of theoretical developments in the field of Western esotericism, this article argues that Breton utilizes his mythic narrative to articulate a transformative knowledge, a surreality, that in certain ways correspond to the concepts of gnosis and clairvoyance in esoteric discourse. To substantiate this, similar mythic narratives about great imperceptible entities in texts of Anthroposophy (Rudolf Steiner) and Rosicrucianism (Lectorium Rosicrucianum) are examined. A comparativist model for describing popular approaches (or mythemes) to ineffable experience is applied. An underlying “gnostic” approach of considering such experiences as incomplete and as being co-created is discerned, highlighting each actor’s endeavours to validate imaginative perception.

Enriching Context Information for Entity Linking with Web Data

Entity linking (EL) is the task of determining the identity of textual entity mentions given a predefined knowledge base (KB). Plenty of existing efforts have been made on this task using either “local” information (contextual information of the mention in the text), or “global” information (relations among candidate entities). However, either local or global information might be insufficient especially when the given text is short. To get richer local and global information for entity linking, we propose to enrich the context information for mentions by getting extra contexts from the web through web search engines (WSE). Based on the intuition above, two novel attempts are made. The first one adds web-searched results into an embedding-based method to expand the mention’s local information, where we try two different methods to help generate high-quality web contexts: one is to apply the attention mechanism and the other is to use the abstract extraction method. The second one uses the web contexts to extend the global information, i.e., finding and utilizing more extra relevant mentions from the web contexts with a graph-based model. Finally, we combine the two models we propose to use both extended local and global information from the extra web contexts. Our empirical study based on six real-world datasets shows that using extra web contexts to extend the local and the global information could effectively improve the F1 score of entity linking.

Extending TextAE for annotation of non-contiguous entities.

Named entity recognition tools are used to identify mentions of biomedical entities in free text and are essential components of high-quality information retrieval and extraction systems. Without good entity recognition, methods will mislabel searched text and will miss important information or identify spurious text that will frustrate users. Most tools do not capture non-contiguous entities which are separate spans of text that together refer to an entity, e.g., the entity “type 1 diabetes” in the phrase “type 1 and type 2 diabetes.” This type is commonly found in biomedical texts, especially in lists, where multiple biomedical entities are named in shortened form to avoid repeating words. Most text annotation systems, that enable users to view and edit entity annotations, do not support non-contiguous entities. Therefore, experts cannot even visualize non-contiguous entities, let alone annotate them to build valuable datasets for machine learning methods. To combat this problem and as part of the BLAH6 hackathon, we extended the TextAE platform to allow visualization and annotation of non-contiguous entities. This enables users to add new subspans to existing entities by selecting additional text. We integrate this new functionality with TextAE’s existing editing functionality to allow easy changes to entity annotation and editing of relation annotations involving non-contiguous entities, with importing and exporting to the PubAnnotation format. Finally, we roughly quantify the problem across the entire accessible biomedical literature to highlight that there are a substantial number of non-contiguous entities that appear in lists that would be missed by most text mining systems.

Chinese clinical named entity recognition based on stacked neural network

SummaryThe precise named entity recognition (NER) is a key component in Chinese clinical natural language processing. Although clinical NER systems have attracted widespread attention and been studied for decades, the latest NER research usually relies on a shallow text representation with one‐layer neural encoding, which fails to capture deep features and limits its performance improvement. To capture more features and encode the clinical text efficiently, we propose a deep stacked neural network for Chinese clinical NER. The neural network stacks two bidirectional long‐short term memory and gated recurrent unit layers to encode the text twice, followed by a conditional random fields (CRF) layer to recognize named entities in Chinese clinical text. Extensive empirical results on three real‐world datasets demonstrate that the proposed method significantly outperforms six state‐of‐the‐art NER methods. Especially compared with the conventional CRF model, our method has at least 3.75% F1‐score improvement on these public datasets.

Recognizing Indonesian Acronym and Expansion Pairs with Supervised Learning and MapReduce

During the previous decades, intelligent identification of acronym and expansion pairs from a large corpus has garnered considerable research attention, particularly in the fields of text mining, entity extraction, and information retrieval. Herein, we present an improved approach to recognize the accurate acronym and expansion pairs from a large Indonesian corpus. Generally, an acronym can be either a combination of uppercase letters or a sequence of speech sounds (syllables). Our proposed approach can be computationally divided into four steps: (1) acronym candidate identification; (2) acronym and expansion pair collection; (3) feature generation; and (4) acronym and expansion pair recognition using supervised learning techniques. Further, we introduce eight numerical features and evaluate their effectiveness in representing the acronym and expansion pairs based on the precision, recall, and F-measure. Furthermore, we compare the k-nearest neighbors (K-NN), support vector machine (SVM), and bidirectional encoder representations from transformers (BERT) algorithms in terms of accurate acronym and expansion pair classification. The experimental results indicate that the SVM polynomial model that considers eight features exhibits the highest accuracy (97.93%), surpassing those of the SVM polynomial model that considers five features (90.45%), the K-NN algorithm with k = 3 that considers eight features (96.82%), the K-NN algorithm with k = 3 that considers five features (95.66%), BERT-Base model (81.64%), and BERT-Base Multilingual Cased model (88.10%). Moreover, we analyze the performance of the Hadoop technology using various numbers of data nodes to identify the acronym and expansion pairs and obtain their feature vectors. The results reveal that the Hadoop cluster containing a large number of data nodes is faster than that with fewer data nodes when processing from ten million to one hundred million pairs of acronyms and expansions.

LATTE: Latent Type Modeling for Biomedical Entity Linking

Entity linking is the task of linking mentions of named entities in natural language text, to entities in a curated knowledge-base. This is of significant importance in the biomedical domain, where it could be used to semantically annotate a large volume of clinical records and biomedical literature, to standardized concepts described in an ontology such as Unified Medical Language System (UMLS). We observe that with precise type information, entity disambiguation becomes a straightforward task. However, fine-grained type information is usually not available in biomedical domain. Thus, we propose LATTE, a LATent Type Entity Linking model, that improves entity linking by modeling the latent fine-grained type information about mentions and entities. Unlike previous methods that perform entity linking directly between the mentions and the entities, LATTE jointly does entity disambiguation, and latent fine-grained type learning, without direct supervision. We evaluate our model on two biomedical datasets: MedMentions, a large scale public dataset annotated with UMLS concepts, and a de-identified corpus of dictated doctor's notes that has been annotated with ICD concepts. Extensive experimental evaluation shows our model achieves significant performance improvements over several state-of-the-art techniques.

The role of knowledge in determining identity of long-tail entities

Identifying entities in text is an important step of semantic analysis. Some entity mentions comprise a name or description, but many include no information that identifies them in the system’s knowledge resources, which means that their identity cannot be established through traditional disambiguation. Consequently, such NIL (not in lexicon) entities have received little attention in entity linking systems and tasks so far. However, given the non-redundancy of knowledge on NIL entities, their lack of frequency priors, their potentially extreme ambiguity, and their numerousness, they constitute an important class of long-tail entities and pose a great challenge for state-of-the-art systems. In this paper, we describe a method for imputing identifying knowledge to NILs from generalized characteristics. We enrich the locally extracted information with profile models that rely on background knowledge in Wikidata. We describe and implement two profiling machines using state-of-the-art neural models. We evaluate their intrinsic behavior and their impact on the task of determining the identity of NIL entities.

Visibility Tagging of nouns in Text-to-Scene Conversion

The recognition and extraction of visual text information is the basis of the realization of Text-to-Scene Conversion, while nouns are the main expression form of entity, event, orientation, action and other information in text, but not all nouns are suitable for visualization, so how to recognize the visibility of nouns is the key problem of Text-to-Scene Conversion. Based on the definition and classification of noun visualization, this paper establishes a keyword visibility dictionary. Furthermore, in view of the static limitations of the dictionary, a dynamic expansion method based on semantic similarity is studied, and an optimization method for the visibility annotation of nouns is proposed in combination with unsupervised clustering. The experimental results show that the proposed method is effective and feasible for nominal visibility annotation of common texts, and the accuracy of the optimized method is 16% higher than that of the simple semantic similarity method.

Poetik der Kritik – Ästhetik des Deutens

Abstract Some of the mainly unchartered territories in literary criticism are the implications of Susan Sontag’s frontal attack on traditional hermeneutical practices in Against Interpretation (1969). This contribution to investigations into the modes of interpretation attempts to draw constructive consequences from this provocation and investigate the notion of a ›poetics of criticism‹ emanating into what can be called the ›aesthetics of interpretation‹. In so doing, it explores the Romantic backdrop of this discourse through examining Friedrich Schlegel’s plea for a ›poetization‹ of critique and his demand to turn critical approaches into aesthetic, if not artistic, acts. Then, these reflections examine notions of perception or Anschauung as a cornerstone of comprehension; discuss poetic renderings of thought with Nietzsche, who epitomizes the fusion of reflection and aesthetic production; single out one of Gottfried Benn’s early poems (»Kreislauf«) as an object for putting aesthetic interpretation into practice given the specific character of this Expressionistic text; and, finally, assess elements of theories of recognition in terms of aesthetic practice with specific reference to a paragraph in early Adorno, which highlights cognitive transformation processes as matters of aesthetic experience. Thus, this essay illustrates the interrelationship between critical theory and practice as an aesthetic act, which takes into account the significance of Sontag’s challenge, exemplifying the necessity of finding a language register that can claim to strive towards adequacy in relation to the (artistic) object of criticism without compromising analytical rigour. The argument developed in this contribution towards an aesthetics of interpretation begins with a critical appreciation of various forms and modes of criticism in literature and other aspects of artistic expression. It centres on the significance of the dialogue as an explorative means of critical discourse, ranging from Friedrich Schlegel to Hugo von Hofmannsthal and indeed Hans Magnus Enzensberger. With the (fictive) dialogue as an instrument of aesthetic judgement, ›experience‹ entered the stage of literary criticism negotiating ambivalences and considering alternative points of view often generated from the texts under consideration. In terms of the ambivalences mentioned above, this investigation into the nature of criticism considers the notion of criticism as a form of art and an extrapolation of aesthetic reason as propagated already by Henry Kames, once even quoted by Hegel in connection with the establishing of a rationale for the critical appreciation of artistic products. It discusses the interplay of distance from, and empathy with, objects of aesthetic criticism asking to what extent the act of interpretation (Wolfgang Iser) can acquire a creative momentum of its own without distorting its true mission, namely to assess the characteristics and aesthetic qualities of specific (poetic) texts or other artistic objects. Following the closer examination of several of Nietzsche’s poems and Roland Barthes’s insistence on the segmentation of the linguistic material that constitutes a textual entity worthy of criticism, the article examines one of Gottfried Benn’s early poems (»Kreislauf«, 1912) in respect of its textual and structural dynamics, awkward sensuality as a form of negative eroticism. On the basis of a detailed linguistic, and indeed poetic, examination it shows where, when, and how literary criticism can meaningfully identify structural features as denominators for aesthetic experience. The final section is devoted to instrumentalize Adorno’s point that concepts can turn with some inevitability into images enabling the theory of cognition to acquire some credibility as a potentially fertile basis for aesthetic practice – both in literary criticism and poetic production. With a concluding reference to Paul Celan’s remark that language acquires a Being of its own and that something of existential significance occurs in the poem, this article illustrates that interpretation depends on a successful interplay of cognitive and sensual processes, which leaves criticism somewhere between aesthetic analysis and contextualization as well as between taking linguistic images metaphorically or indeed literarily. Finally, it suggests regarding aesthetic criticism as a way to assess both the actual creative process and its results as if they were involved in a ›dialogue‹ of their own. Therefore, interpretation can be seen as a process that generates its very own dynamics and procedures (i. e. ›poetics‹), either in relation to its object or in form of a juxtaposition. If the latter, the likelihood is stronger that ›interpretation‹ acquires more distinctiveness. Ultimately, however, the (quasi-performative) quality of interpretation depends on its stylistic features, the adequacy of language used, and conceptual stringency without disregarding its essential function, namely to enable a dialogue between the work of art and its recipient and the recipients amongst themselves.

Named-Entity Recognition in Sports Field Based on a Character-Level Graph Convolutional Network

Traditional methods for identifying naming ignore the correlation between named entities and lose hierarchical structural information between the named entities in a given text. Although traditional named-entity methods are effective for conventional datasets that have simple structures, they are not as effective for sports texts. This paper proposes a Chinese sports text named-entity recognition method based on a character graph convolutional neural network (Char GCN) with a self-attention mechanism model. In this method, each Chinese character in the sports text is regarded as a node. The edge between the nodes is constructed using a similar character position and the character feature of the named-entity in the sports text. The internal structural information of the entity is extracted using a character map convolutional neural network. The hierarchical semantic information of the sports text is captured by the self-attention model to enhance the relationship between the named entities and capture the relevance and dependency between the characters. The conditional random fields classification function can accurately identify the named entities in the Chinese sports text. The results conducted on four datasets demonstrate that the proposed method improves the F-Score values significantly to 92.51%, 91.91%, 93.98%, and 95.01%, respectively, in comparison to the traditional naming methods.