This paper introduces a named entity recognition approach in textual corpus. This Named Entity (NE) can be a named: location, person, organization, date, time, etc., characterized by instances. A NE is found in texts accompanied by contexts: words that are left or right of the NE. The work mainly aims at identifying contexts inducing the NE’s nature. As such, The occurrence of the word "Preside...

The recognition and normalization of gene mentions in biomedical literature are crucial steps in biomedical text mining. We present a system for extracting gene names from biomedical literature and normalizing them to gene identifiers in databases. The system consists of four major components: gene name recognition, entity mapping, disambiguation and filtering. The first component is a gene nam...

Named Entity Recognition (NER) is often used to acquire important information from text documents as a part of the Information Extraction (IE) process. However, quality affects accuracy data obtained, especially for acquired involving Optical Character (OCR) process, which never reached 100% accuracy. This research tried examine OCR engine with highest performance IE using NER by comparing thre...

The variety and difference between domains for textual data require customization in the Natural Language Processing component especially Named Entity Recognition where different contain several types of entities. current NER model is deemed not fit to accurately extract entities from Quranic text due its unique content. This paper describes building a rule-based method that exist English trans...

Named Entity Recognition and classification (NERC) is an essential and challenging task in (NLP). Kannada is a highly inflectional and agglutinating language providing one of the richest and most challenging sets of linguistic and statistical features resulting in long and complex word forms, which is large in number. It is primarily a suffixing Language and inflected word starts with a root an...

The KBQA (Knowledge-Based Question Answering) system is an essential part of the smart customer service system. a type QA (Question based on KB (Knowledge Base). It aims to automatically answer natural language questions by retrieving structured data stored in knowledge base. Generally, when receives user’s query, it first needs recognize topic entities such as name, location, organization, etc...

Named Entities (NEs) are often written with no orthographic changes across different languages that share a common alphabet. We show that this can be leveraged so as to improve named entity recognition (NER) by using unsupervised word clusters from secondary languages as features in state-of-the-art discriminative NER systems. We observe significant increases in performance, finding that person...

We described semi-Markov models which relaxes usual Markov assumptions made in hidden Markov models. Semi-Markov models classify segments of adjacent words, rather than single words. We proposed two training strategies, a discriminative training and a generative training for semi-Markov models. Importantly, features for semi-Markov models can measure properties of segments, and transitions with...

Twitter allows users to easily post tweets on any subject or event anytime, generating massive amounts of rich text content diverse topics. Automated methods such as Named Entity Recognition (NER) are required process the tweet data. Processing tweets, however, poses a special challenge they informal posts with incomplete context and often contain acronyms, hashtags, misspellings, abbreviations...

Natural Language Processing (NLP) is a research field where language in consideration processed to understand its syntactic, semantic, and sentimental aspects. The advancement the NLP area has helped solve problems domains such as Neural Machine Translation, Name Entity Recognition, Sentiment Analysis, Chatbots, name few. topic of broadly consists two main parts: representation input text (raw ...