Recognition of Chemical Entities using Pattern Matching and Functional Group Classification

2016 ◽  
Vol 12 (4) ◽  
pp. 21-44 ◽  
Author(s):  
R. Hema ◽  
T. V. Geetha
Keyword(s):  
Pattern Matching ◽  
Functional Group ◽  
Named Entity Recognition ◽  
Chemical Compounds ◽  
Chemical Entity ◽  
Entity Recognition ◽  
Matching Method ◽  
Named Entity ◽  
One Way Anova

The two main challenges in chemical entity recognition are: (i) New chemical compounds are constantly being synthesized infinitely. (ii) High ambiguity in chemical representation in which a chemical entity is being described by different nomenclatures. Therefore, the identification and maintenance of chemical terminologies is a tough task. Since most of the existing text mining methods followed the term-based approaches, the problems of polysemy and synonymy came into the picture. So, a Named Entity Recognition (NER) system based on pattern matching in chemical domain is developed to extract the chemical entities from chemical documents. The Tf-idf and PMI association measures are used to filter out the non-chemical terms. The F-score of 92.19% is achieved for chemical NER. This proposed method is compared with the baseline method and other existing approaches. As the final step, the filtered chemical entities are classified into sixteen functional groups. The classification is done using SVM One against All multiclass classification approach and achieved the accuracy of 87%. One-way ANOVA is used to test the quality of pattern matching method with the other existing chemical NER methods.

scholarly journals Tagger: BeCalm API for rapid named entity recognition

2017 ◽  
Author(s):  
Lars Juhl Jensen
Keyword(s):  
Open Access ◽  
Text Mining ◽  
Real Time ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
The Real ◽  
Practical Applications ◽  
Named Entity ◽  
Highly Efficient

AbstractMost BioCreative tasks to date have focused on assessing the quality of text-mining annotations in terms of precision of recall. Interoperability, speed, and stability are, however, other important factors to consider for practical applications of text mining. The new BioCreative/BeCalm TIPS task focuses purely on these. To participate in this task, I implemented a BeCalm API within the real-time tagging server also used by the Reflect and EXTRACT tools. In addition to retrieval of patent abstracts, PubMed abstracts, and Pub-Med Central open-access articles as required in the TIPS task, the BeCalm API implementation facilitates retrieval of documents from other sources specified as custom request parameters. As in earlier tests, the tagger proved to be both highly efficient and stable, being able to consistently process requests of 5000 abstracts in less than half a minute including retrieval of the document text.

scholarly journals A Deep Learning Approach to Integrate Human-Level Understanding in a Chatbot

2021 ◽  
Author(s):  
Afia Fairoose Abedin ◽  
Amirul Islam Al Mamun ◽  
Rownak Jahan Nowrin ◽  
Amitabha Chakrabarty ◽  
Moin Mostakim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Service Management ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Emotion Detection ◽  
Named Entity ◽  
Input Text ◽  
Quality Of Products ◽  
Task Oriented

In recent times, a large number of people have been involved in establishing their own businesses. Unlike humans, chatbots can serve multiple customers at a time, are available 24/7 and reply in less than a fraction of a second. Though chatbots perform well in task-oriented activities, in most cases they fail to understand personalized opinions, statements or even queries which later impact the organization for poor service management. Lack of understanding capabilities in bots disinterest humans to continue conversations with them. Usually, chatbots give absurd responses when they are unable to interpret a user’s text accurately. Extracting the client reviews from conversations by using chatbots, organizations can reduce the major gap of understanding between the users and the chatbot and improve their quality of products and services.Thus, in our research we incorporated all the key elements that are necessary for a chatbot to analyse andunderstand an input text precisely and accurately. We performed sentiment analysis, emotion detection, intent classification and named-entity recognition using deep learning to develop chatbots with humanistic understanding and intelligence. The efficiency of our approach can be demonstrated accordingly by the detailed analysis.

Techniques for Named Entity Recognition

2012 ◽  
pp. 191-217 ◽  
Author(s):  
Girish Keshav Palshikar
Keyword(s):  
Semantic Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entities ◽  
Named Entity ◽  
Domain Specific ◽  
Number Of Factors ◽  
Web Contents ◽  
Biological Domain

While building and using a fully semantic understanding of Web contents is a distant goal, named entities (NEs) provide a small, tractable set of elements carrying a well-defined semantics. Generic named entities are names of persons, locations, organizations, phone numbers, and dates, while domain-specific named entities includes names of for example, proteins, enzymes, organisms, genes, cells, et cetera, in the biological domain. An ability to automatically perform named entity recognition (NER) – i.e., identify occurrences of NE in Web contents – can have multiple benefits, such as improving the expressiveness of queries and also improving the quality of the search results. A number of factors make building highly accurate NER a challenging task. Given the importance of NER in semantic processing of text, this chapter presents a detailed survey of NER techniques for English text.

Techniques for Named Entity Recognition

2013 ◽  
pp. 400-426 ◽  
Author(s):  
Girish Keshav Palshikar
Keyword(s):  
Semantic Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entities ◽  
Named Entity ◽  
Domain Specific ◽  
Number Of Factors ◽  
Web Contents ◽  
Biological Domain

While building and using a fully semantic understanding of Web contents is a distant goal, named entities (NEs) provide a small, tractable set of elements carrying a well-defined semantics. Generic named entities are names of persons, locations, organizations, phone numbers, and dates, while domain-specific named entities includes names of for example, proteins, enzymes, organisms, genes, cells, et cetera, in the biological domain. An ability to automatically perform named entity recognition (NER) – i.e., identify occurrences of NE in Web contents – can have multiple benefits, such as improving the expressiveness of queries and also improving the quality of the search results. A number of factors make building highly accurate NER a challenging task. Given the importance of NER in semantic processing of text, this chapter presents a detailed survey of NER techniques for English text.

scholarly journals Chemical Entity Recognition and Resolution to ChEBI

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Tiago Grego ◽  
Catia Pesquita ◽  
Hugo P. Bastos ◽  
Francisco M. Couto
Keyword(s):  
Machine Learning ◽  
Named Entity Recognition ◽  
Recognition Task ◽  
Entity Resolution ◽  
Chemical Entity ◽  
Biomedical Literature ◽  
Entity Recognition ◽  
Named Entity ◽  
Lexical Similarity ◽  
Recognition Systems

Chemical entities are ubiquitous through the biomedical literature and the development of text-mining systems that can efficiently identify those entities are required. Due to the lack of available corpora and data resources, the community has focused its efforts in the development of gene and protein named entity recognition systems, but with the release of ChEBI and the availability of an annotated corpus, this task can be addressed. We developed a machine-learning-based method for chemical entity recognition and a lexical-similarity-based method for chemical entity resolution and compared them with Whatizit, a popular-dictionary-based method. Our methods outperformed the dictionary-based method in all tasks, yielding an improvement in F-measure of 20% for the entity recognition task, 2–5% for the entity-resolution task, and 15% for combined entity recognition and resolution tasks.

Infoveillance of the Croatian Online Media during the COVID-19 Pandemic: a One-Year Longitudinal NLP Study (Preprint)

2021 ◽  
Author(s):  
Slobodan Beliga ◽  
Sanda Martinčić-Ipšić ◽  
Mihaela Matešić ◽  
Irena Petrijevčanin Vuksanović ◽  
Ana Meštrović
Keyword(s):  
Language Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Online Media ◽  
Quality Of Reporting ◽  
Named Entity ◽  
Public Health Emergencies ◽  
One Year ◽  
Second Wave

BACKGROUND Online media plays an important role in public health emergencies and serves as a communication platform. Infoveillance of online media during the COVID-19 pandemic is an important step toward a better understanding of crisis communication. OBJECTIVE The goal of this study is to perform a longitudinal analysis of the COVID-19 related content based on natural language processing methods. METHODS We collected dataset of news articles published by Croatian online media during the first 13 months of the pandemic. Firstly, we test the correlations between the number of articles and the number of new daily COVID-19 cases. Secondly, we analyze the content by extracting the most frequent terms and apply the Jaccard similarity. Next, we compare the occurrence of the pandemic-related terms during the two waves of the pandemic. Finally, we apply named entity recognition to extract the most frequent entities and track the dynamics of changes during the observed period. RESULTS The results show there is no significant correlation between the number of articles and the number of new daily COVID-19 cases. Furthermore, there are high overlaps in the terminology used in all articles published during the pandemic with a slight shift in the pandemic-related terms between the first and the second wave. Finally, the findings indicate that the most influential entities have lower overlaps for identified persons and higher overlaps for locations and institutions. CONCLUSIONS Our study shows that online media has a prompt response to the pandemic with a large number of COVID-19 related articles. There is a high overlap in the frequently used terms across the first 13 months, which may indicate the lower quality of reporting in certain periods. However, the pandemic-related terminology is well covered.

scholarly journals ACE-ADP: Adversarial Contextual Embeddings Based Named Entity Recognition for Agricultural Diseases and Pests

Agriculture ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 912
Author(s):  
Xuchao Guo ◽  
Xia Hao ◽  
Zhan Tang ◽  
Lei Diao ◽  
Zhao Bai ◽  
...  
Keyword(s):  
Web Application ◽  
Question Answering ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Text Representation ◽  
Named Entity ◽  
Domain Specific ◽  
Adversarial Training ◽  
Ablation Study

Entity recognition tasks, which aim to utilize the deep learning-based models to identify the agricultural diseases and pests-related nouns such as the names of diseases, pests, and drugs from the texts collected on the internet or input by users, are a fundamental component for agricultural knowledge graph construction and question-answering, which will be implemented as a web application and provide the general public with solutions for agricultural diseases and pest control. Nonetheless, there are still challenges: (1) the polysemous problem needs to be further solved, (2) the quality of the text representation needs to be further enhanced, (3) the performance for rare entities needs to be further improved. We proposed an adversarial contextual embeddings-based model named ACE-ADP for named entity recognition in Chinese agricultural diseases and pests domain (CNER-ADP). First, we enhanced the text representation and overcame the polysemy problem by using the fine-tuned BERT model to generate the contextual character-level embedded representation with the specific knowledge. Second, adversarial training was also introduced to enhance the generalization and robustness in terms of identifying the rare entities. The experimental results showed that our model achieved an F1 of 98.31% with 4.23% relative improvement compared to the baseline model (i.e., word2vec-based BiLSTM-CRF) on the self-annotated corpus named Chinese named entity recognition dataset for agricultural diseases and pests (AgCNER). Besides, the ablation study and discussion demonstrated that ACE-ADP could not only effectively extract rare entities but also maintain a powerful ability to predict new entities in new datasets with high accuracy. It could be used as a basis for further research on other domain-specific named entity recognition.

scholarly journals Unsupervised Named Entity Recognition for Hi-Tech Domain

2021 ◽  
Author(s):  
Abinaya Govindan ◽  
Gyan Ranjan ◽  
Amit Verma
Keyword(s):  
Named Entity Recognition ◽  
Entity Recognition ◽  
Manual Annotation ◽  
Data Generation ◽  
Named Entities ◽  
Named Entity ◽  
Free Data ◽  
Minimal Effort ◽  
Unique Context

This paper presents named entity recognition as a multi-answer QA task combined with contextual natural-language-inference based noise reduction. This method allows us to use pre-trained models that have been trained for certain downstream tasks to generate unsupervised data, reducing the need for manual annotation to create named entity tags with tokens. For each entity, we provide a unique context, such as entity types, definitions, questions and a few empirical rules along with the target text to train a named entity model for the domain of our interest. This formulation (a) allows the system to jointly learn NER-specific features from the datasets provided, and (b) can extract multiple NER-specific features, thereby boosting the performance of existing NER models (c) provides business-contextualized definitions to reduce ambiguity among similar entities. We conducted numerous tests to determine the quality of the created data, and we find that this method of data generation allows us to obtain clean, noise-free data with minimal effort and time. This approach has been demonstrated to be successful in extracting named entities, which are then used in subsequent components.

scholarly journals Annotation and initial evaluation of a large annotated German oncological corpus

JAMIA Open ◽  
2021 ◽  
Vol 4 (2) ◽  
Author(s):  
Madeleine Kittner ◽  
Mario Lamping ◽  
Damian T Rieke ◽  
Julian Götze ◽  
Bariya Bajwa ◽  
...  
Keyword(s):  
Information Extraction ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Medical Texts ◽  
Quality Of Information ◽  
Data Set ◽  
Named Entity ◽  
Negation Detection ◽  
Discharge Summaries

Abstract Objective We present the Berlin-Tübingen-Oncology corpus (BRONCO), a large and freely available corpus of shuffled sentences from German oncological discharge summaries annotated with diagnosis, treatments, medications, and further attributes including negation and speculation. The aim of BRONCO is to foster reproducible and openly available research on Information Extraction from German medical texts. Materials and Methods BRONCO consists of 200 manually deidentified discharge summaries of cancer patients. Annotation followed a structured and quality-controlled process involving 2 groups of medical experts to ensure consistency, comprehensiveness, and high quality of annotations. We present results of several state-of-the-art techniques for different IE tasks as baselines for subsequent research. Results The annotated corpus consists of 11 434 sentences and 89 942 tokens, annotated with 11 124 annotations for medical entities and 3118 annotations of related attributes. We publish 75% of the corpus as a set of shuffled sentences, and keep 25% as held-out data set for unbiased evaluation of future IE tools. On this held-out dataset, our baselines reach depending on the specific entity types F1-scores of 0.72–0.90 for named entity recognition, 0.10–0.68 for entity normalization, 0.55 for negation detection, and 0.33 for speculation detection. Discussion Medical corpus annotation is a complex and time-consuming task. This makes sharing of such resources even more important. Conclusion To our knowledge, BRONCO is the first sizable and freely available German medical corpus. Our baseline results show that more research efforts are necessary to lift the quality of information extraction in German medical texts to the level already possible for English.

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