China
Africa
Explore chapters and articles related to this topic
Healthcare NLP Infrastructure for the Greek Language
Published in Satya Ranjan Dash, Shantipriya Parida, Esaú Villatoro Tello, Biswaranjan Acharya, Ondřej Bojar, Natural Language Processing in Healthcare, 2022
Aristides Vagelatos, Elena Mantzari, Mavina Pantazara, Christos Tsalidis, Chryssoula Kalamara
Named Entity Recognition (NER) as well as term recognition are widely used in many NLP applications, such as classification, semantic indexing and searching, virtual assistants, etc. The task of recognition of the syntactic structure of a phrase or a sentence is complex and nondeterministic. Although for multi-word terms the task is simpler because they follow specific morphosyntactic patterns, nevertheless a grammar formalism is necessary to describe these patterns, as well as a resolution algorithm that will apply the grammar to a text (see also section 4.4.2.5). A context-sensitive grammar formalism has been developed, called Kanon (Vagelatos et al. 2011), along with an efficient resolution algorithm that applies the grammar to the text and recognizes syntactic structures. The resolution algorithm applies a surface parsing technique that permits the recognition of parts of a sentence without requiring the syntax recognition of the whole sentence. The following example is a simplified rule in Kanon for candidate terms:[ARULE="TERMS_1_7", VTEXT="TERM", TTEXT=Concatenate("TTEXT"," ")] =>\[LEXY->HasMAttrs ([N])],[LEXY->HasMAttrs ([N,GEN]), GNC_Agreement(NUMBER,[N,GEN])]{2,5}/ ;
Sentiment Analysis of Twitter Data Using Named Entity Recognition
Published in B. K. Mishra, Samarjeet Borah, Hemant Kasturiwale, Computing and Communications Engineering in Real-Time Application Development, 2023
Named entity recognition (NER) is the initial step toward information extraction which is aimed at localization and classification of named entities in text into predefined categories (tokens), such as the names of locations, organizations, persons, events, expressions of times, monetary values, percentages, quantities etc.13 Entity disambiguation (ED) follows NER. This is implemented to map the entity reference from the reference knowledge bases, such as Wikipedia, standard datasets, etc.
Analytics of IoT, SAR, and Social Network Data for Detection of Anomalies in Climate Conditions
Published in Monika Mangla, Ashok Kumar, Vaishali Mehta, Megha Bhushan, Sachi Nandan Mohanty, Real-Life Applications of the Internet of Things, 2022
Named entity recognition (NER) is the initial step towards information extraction which is aimed at localization and classification of named entities in text into predefined categories (tokens), for example, the names of organizations, events, expressions of times, locations, persons, percentages, monetary values, quantities, etc. [24, 25]. After NER, entity disambiguation (ED) is performed for the mapping of an entity reference from knowledge bases such as Wikipedia or other standard datasets.
Entity and relation collaborative extraction approach based on multi-head attention and gated mechanism
Published in Connection Science, 2022
Wei Zhao, Shan Zhao, Shuhui Chen, Tien-Hsiung Weng, WenJie Kang
The information extraction problem can be regarded as a sequence labelling problem, which will generate label space information (label information for short). Sequence labelling aims to give a label to each element in the sequence. In general, in NLP, a sequence refers to a sentence, and an element refers to a word in the sentence. Named Entity Recognition (NER) is a subtask of information extraction, which needs to locate and classify elements. For NER, its label information includes the locations and types of elements. In this paper, the BIO joint tagging method is used to tag each element with “B-X”, “I-X”, or “O”. Where “B-X” indicates the beginning of the element of type X, “I-X” indicates the middle position of the element of type X, and “O”indicates that the element does not have a type. As the entity “Richard Celeste” shown in Figure 1, “Richard” is labelled as “B-Peop” since it is the first element of the entity with the type name. Then “Celeste” is labelled as “I-Peop”. Since “Celeste” is followed by a word labelled “O”, it can be inferred that “Celeste” is the end boundary of this entity.
Visualization of Twitter Sentiments on Kashmir Territorial Conflict
Published in Cybernetics and Systems, 2021
Shubhangi Rastogi, Divya Bansal
Finally, Named Entity Recognition (NER) is to detect and label these nouns with the real-world concepts that they represent considering the context of how a word appears in the sentence and a statistical model to guess which type of noun a word represents. In essence, SA is effective when applied to meaningful entities. In our pilot study, we considered the adjective and noun combinations. Nouns are entities, and adjectives are the qualifiers; giving more weightage to their mixtures would bring out the more precise positive or negative polarity of the sentences. This specific entity combination is selected over others because it is well suited to find the sentiment of a certain topic from a sentence. Finally, the average sentiment score of the complete sentence and entity combination has been calculated and used as the final sentiment score. Figure 3 demonstrates the intermediate outcomes of cleaning the sample using NLP to obtain the cleaned data corpus finally. The combination of adjectives and nouns has been extracted to find the sentiment score and, finally, structured the data using NER dependency parsing.
Identifying traffic conditions from non-traffic related sources
Published in Journal of Intelligent Transportation Systems, 2020
Jorge C. Chamby-Diaz, Rhuam Sena Estevam, Ana L. C. Bazzan
Due to the fact that most of the data collected is in natural language and/or not geolocated, the following two processing phases are necessary.Named Entity Recognition (NER): when it is necessary to find named entities in a text in natural language (as, for example, names of streets and other locations), NER is a common technique, which processes a document and recognizes words or sentences that mention named entities. These are predefined categories, such as names of locations, of people, or organizations. Some tools were already developed to perform this task, e.g., spaCy, NLTK, AllenNLP etc.Geolocation: Names of streets and other locations were recognized by a NER tool, it is necessary to geolocate them using a given system of coordinates.