Named Entity Extraction Research Articles

Statistical Arabic Name Entity Recognition Approaches: A Survey

With the increase of Arabic textual information via internet websites and services, a tools for processing Arabic text information are needed to extract knowledge from them. Name Entity recognition aims to extract name entities such as: person names, locations and organizations from a given text. Name Entity recognition approaches were classified into two main approaches: rule-based approach and statistical approach. Although the literature on Name Entity recognition is quit extensive, few researches to extract Name Entities in the Arabic language could be found. This paper provides a comprehensive survey about statistical approaches of Arabic Name Entity extraction.

Boosted Web Named Entity Recognition via Tri-Training

Named entity extraction is a fundamental task for many natural language processing applications on the web. Existing studies rely on annotated training data, which is quite expensive to obtain large datasets, limiting the effectiveness of recognition. In this research, we propose a semisupervised learning approach for web named entity recognition (NER) model construction via automatic labeling and tri-training. The former utilizes structured resources containing known named entities for automatic labeling, while the latter makes use of unlabeled examples to improve the extraction performance. Since this automatically labeled training data may contain noise, a self-testing procedure is used as a follow-up to remove low-confidence annotation and prepare higher-quality training data. Furthermore, we modify tri-training for sequence labeling and derive a proper initialization for large dataset training to improve entity recognition. Finally, we apply this semisupervised learning framework for person name recognition, business organization name recognition, and location name extraction. In the task of Chinese NER, an F-measure of 0.911, 0.849, and 0.845 can be achieved, for person, business organization, and location NER, respectively. The same framework is also applied for English and Japanese business organization name recognition and obtains models with performance of a 0.832 and 0.803 F-measure.

Issues and Challenges in Marathi Named Entity Recognition

Information Extraction (IE) is a sub discipline of Artificial Intelligence. IE identifies information in unstructured information source that adheres to predefined semantics i.e. people, location etc. Recognition of named entities (NEs) from computer readable natural language text is significant task of IE and natural language processing (NLP). Named entity (NE) extraction is important step for processing unstructured content. Unstructured data is computationally opaque. Computers require computationally transparent data for processing. IE adds meaning to raw data so that it can be easily processed by computers. There are various different approaches that are applied for extraction of entities from text. This paper elaborates need of NE recognition for Marathi and discusses issues and challenges involved in NE recognition tasks for Marathi language. It also explores various methods and techniques that are useful for creation of learning resources and lexicons that are important for extraction of NEs from natural language unstructured text.

Optimized Multi Class SVM Classifier for Named Entity Extraction for Workflow Scheduling in Cloud

Optimized Multi Class SVM Classifier for Named Entity Extraction for Workflow Scheduling in Cloud

Semantic Enrichment for Recommendation of Primary Studies in a Systematic Literature Review

A Systematic Literature Review (SLR) identifies, evaluates, and synthesizes the literature available for a given topic. This generally requires a significant human workload and has subjectivity bias that could affect the results of such a review. Automated document classification can be a valuable tool for recommending the selection of studies. In this article, we propose an automated pre-selection approach based on text mining and semantic enrichment techniques. Each document is firstly processed by a named entity extractor. The DBpedia URIs coming from the entity linking process are used as external sources of information. Our system collects the bag of words of those sources and it adds them to the initial document. A Multinomial Naive Bayes classifier discriminates whether the enriched document belongs to the positive example set or not. We used an existing manually performed SLR as benchmark data set. We trained our system with different configurations of relevant documents and we tested the goodness of our approach with an empirical assessment. Results show a reduction of the manual workload of 18% that a human researcher has to spend, while holding a remarkable 95% of recall, important condition for the nature itself of SLRs. We measure the effect of the enrichment process to the precision of the classifier and we observed a gain up to 5%.

TwitterNEED: A hybrid approach for named entity extraction and disambiguation for tweet

AbstractTwitter is a rich source of continuously and instantly updated information. Shortness and informality of tweets are challenges for Natural Language Processing tasks. In this paper, we present TwitterNEED, a hybrid approach for Named Entity Extraction and Named Entity Disambiguation for tweets. We believe that disambiguation can help to improve the extraction process. This mimics the way humans understand language and reduces error propagation in the whole system. Our extraction approach aims for high extraction recall first, after which a Support Vector Machine attempts to filter out false positives among the extracted candidates using features derived from the disambiguation phase in addition to other word shape and Knowledge Base features. For Named Entity Disambiguation, we obtain a list of entity candidates from the YAGO Knowledge Base in addition to top-ranked pages from the Google search engine for each extracted mention. We use a Support Vector Machine to rank the candidate pages according to a set of URL and context similarity features. For evaluation, five data sets are used to evaluate the extraction approach, and three of them to evaluate both the disambiguation approach and the combined extraction and disambiguation approach. Experiments show better results compared to our competitors DBpedia Spotlight, Stanford Named Entity Recognition, and the AIDA disambiguation system.

Exploiting Linked Data for Open and Configurable Named Entity Extraction

Named Entity Extraction (NEE) is the process of identifying entities in texts and, very commonly, linking them to related (Web) resources. This task is useful in several applications, e.g. for question answering, annotating documents, post-processing of search results, etc. However, existing NEE tools lack an open or easy configuration although this is very important for building domain-specific applications. For example, supporting a new category of entities, or specifying how to link the detected entities with online resources, is either impossible or very laborious. In this paper, we show how we can exploit semantic information (Linked Data) at real-time for configuring (handily) a NEE system and we propose a generic model for configuring such services. To explicitly define the semantics of the proposed model, we introduce an RDF/S vocabulary, called “Open NEE Configuration Model”, which allows a NEE service to describe (and publish as Linked Data) its entity mining capabilities, but also to be dynamically configured. To allow relating the output of a NEE process with an applied configuration, we propose an extension of the Open Annotation Data Model which also enables an application to run advanced queries over the annotated data. As a proof of concept, we present X-Link, a fully-configurable NEE framework that realizes this approach. Contrary to the existing tools, X-Link allows the user to easily define the categories of entities that are interesting for the application at hand by exploiting one or more semantic Knowledge Bases. The user is also able to update a category and specify how to semantically link and enrich the identified entities. This enhanced configurability allows X-Link to be easily configured for different contexts for building domain-specific applications. To test the approach, we conducted a task-based evaluation with users that demonstrates its usability, and a case study that demonstrates its feasibility.

NER-FL: A Novel Named Entity Recognizer of Farsi Languageusing the Web-Based Natural Language Processors and Semantic Annotations

Named Entity Recognition is a main task in the NLP area that has yielded multiple web-based natural language processors gaining popularity in the Semantic Web community for extracting knowledge from web data. These processors are generally located as pipelines, using dedicated APIs and various taxonomy for extracting, classifying and disambiguating named entities. In this paper, we address the problem of NER on Farsi language by proposing NER-FL, a novel semantic framework which unifies three popular named entity extractors available on the web, and the NER-FL ontology which provides a rich set of axioms aligning the taxonomies of these web natural language processors automatically on the LOD-cloud.

Tibetan-Chinese Named Entity Extraction Based on Comparable Corpus

Tibetan-Chinese named entity extraction is the foundation of cross language information processing, and provides a basis for machine translation and cross language information retrieval research. In this paper, we use the multi-language links of Wikipedia to obtain Tibetan-Chinese comparable corpus, and combine sentence length, word matching and entity boundary words together to get parallel sentence. Then we extract Tibetan-Chinese named entity from the comparable corpus in three ways: (1) Extracting Natural labeling information. (2) Acquiring the links of Tibetan entries and Chinese entries. (3) Using sequence intersection method, which includes the sentence representation, Chinese named entity recognition and corresponding Tibetan sentences intersection. Finally, the results show the extraction method based on comparable corpus is effective.

Name Entity Recognition by New Framework Using Machine Learning Algorithm

The amount of textual information available electronically has made it difficult for many users to find and access the right information within acceptable time. Research communities in the natural language processing (NLP) field are developing tools and techniques to alleviate these problems and help users in exploiting these vast resources. These techniques include Information Retrieval (IR) and Information Extraction (IE). The work described in this thesis concerns IE and more specifically, named entity extraction in English. The English language is of significant interest to the NLP community mainly due to its political and economic significance, but also due to its interesting characteristics. Text usually contains all kinds of names such as person names, company names, city and country names, sports teams, chemicals and lots of other names from specific domains. These names are called Named Entities (NE) and Named Entity Recognition (NER), one of the main tasks of IE systems, seeks to locate and classify automatically these names into predefined categories. NER systems are developed for different applications and can be beneficial to other information management technologies as it can be built over an IR system or can be used as the base module of a Data Mining application. In this thesis we propose an efficient and effective framework for extracting Arabic NEs from text using a rule based approach. Our approach makes use of English contextual and morphological information to extract named entities. The context is represented by means of words that are used as clues for each named entity type. Morphological information is used to detect the part of speech of each word given to the morphological analyzer. Subsequently we developed and implemented our rules in order to recognize each position of the named entity. Finally, our system implementation, evaluation metrics and experimental results are presented. We Present our Methodology by this Paper. Which use Hybrid approach of NlP and Machine Learning. This paper is a Review paper and Introduce Our Methodlogy. Key Terms: Natural Language Processing, Machine Translation, Name Entity Recognition, Different Languages.

Razpoznavanje imenskih entitet v slovenskem besedilu

Članek predstavlja algoritem in implementacijo programa za razpoznavanje imen v slovenskem jeziku s pomočjo strojnega učenja. Nadzorovani pristop na osnovi pogojnih naključnih polj je naučen na označenem korpusu ssj500k. V korpusu, ki je prosto dostopen pod licenco Creative Commons CC-BY-NC-SA, so pri besednih pojavnicah poleg oblikoskladenjskih oznak in lem označena tudi imena organizacij, osebna, zemljepisna ter stvarna imena. Članek predstavlja vpliv na natančnost razpoznavanja ob uporabi oblikoskladenjskih oznak, leksikonov in konjunkcij sosednjih lastnosti. Ena od ugotovitev raziskave je, da so oblikoskladenjske oznake pri razpoznavanju entitet koristne. V kombinaciji z vsemi ostalimi lastnostmi doseže sistem na testni množici 74% natančnost in 72% priklic, pri čemer so najbolje razpoznana osebna imena, sledijo jim zemljepisna ter organizacijska in nazadnje stvarna imena. Novo spoznanje članka je tudi to, da lahko z delitvijo razreda vseh stvarnih imen na organizacije in preostala stvarna imena dosežemo boljše rezultate prepoznavanja tudi pri drugih razredih. Preizkusi na neodvisno označenih korpusi kažejo dobro posplošenost modela za osebna in zemljepisna imena. Programska oprema, narejena v raziskavi, je prosto dostopna pod licenco Apache 2.0 na naslovu http://ailab.ijs.si/~tadej/slner.zip, razvojne različice pa so na voljo na naslovuhttps://github.com/tadejs/slner.

Biomedical named entity extraction: some issues of corpus compatibilities

BackgroundNamed Entity (NE) extraction is one of the most fundamental and important tasks in biomedical information extraction. It involves identification of certain entities from text and their classification into some predefined categories. In the biomedical community, there is yet no general consensus regarding named entity (NE) annotation; thus, it is very difficult to compare the existing systems due to corpus incompatibilities. Due to this problem we can not also exploit the advantages of using different corpora together. In our present work we address the issues of corpus compatibilities, and use a single objective optimization (SOO) based classifier ensemble technique that uses the search capability of genetic algorithm (GA) for NE extraction in biomedicine. We hypothesize that the reliability of predictions of each classifier differs among the various output classes. We use Conditional Random Field (CRF) and Support Vector Machine (SVM) frameworks to build a number of models depending upon the various representations of the set of features and/or feature templates. It is to be noted that we tried to extract the features without using any deep domain knowledge and/or resources.ResultsIn order to assess the challenges of corpus compatibilities, we experiment with the different benchmark datasets and their various combinations. Comparison results with the existing approaches prove the efficacy of the used technique. GA based ensemble achieves around 2% performance improvements over the individual classifiers. Degradation in performance on the integrated corpus clearly shows the difficulties of the task.ConclusionsIn summary, our used ensemble based approach attains the state-of-the-art performance levels for entity extraction in three different kinds of biomedical datasets. The possible reasons behind the better performance in our used approach are the (i). use of variety and rich features as described in Subsection “Features for named entity extraction”; (ii) use of GA based classifier ensemble technique to combine the outputs of multiple classifiers.

A Rule Based Answer Extraction System with Stemming & Anaphora Resolution

Natural Language Processing (NLP) is an area of computer Science and Sub area of Artificial Intelligence (AI).We are developing a rule-based system that can read a large collection of text (say for e.g. story) and find the sentence in the text that best answers the given question. The system uses set of handcrafted rules augmented with some NLP techniques like stemming, named entity extraction etc. that look for Lexical and semantic clues in the question and the text (i.e. story). Each rule awards a certain number of points to each sentence. After all of the rules have been applied, the sentence that obtains the highest score is returned as the answer.

An end-to-end system to identify temporal relation in discharge summaries: 2012 i2b2 challenge

To create an end-to-end system to identify temporal relation in discharge summaries for the 2012 i2b2 challenge. The challenge includes event extraction, timex extraction, and temporal relation identification. An end-to-end temporal relation system was developed. It includes three subsystems: an event extraction system (conditional random fields (CRF) name entity extraction and their corresponding attribute classifiers), a temporal extraction system (CRF name entity extraction, their corresponding attribute classifiers, and context-free grammar based normalization system), and a temporal relation system (10 multi-support vector machine (SVM) classifiers and a Markov logic networks inference system) using labeled sequential pattern mining, syntactic structures based on parse trees, and results from a coordination classifier. Micro-averaged precision (P), recall (R), averaged P&R (P&R), and F measure (F) were used to evaluate results. For event extraction, the system achieved 0.9415 (P), 0.8930 (R), 0.9166 (P&R), and 0.9166 (F). The accuracies of their type, polarity, and modality were 0.8574, 0.8585, and 0.8560, respectively. For timex extraction, the system achieved 0.8818, 0.9489, 0.9141, and 0.9141, respectively. The accuracies of their type, value, and modifier were 0.8929, 0.7170, and 0.8907, respectively. For temporal relation, the system achieved 0.6589, 0.7129, 0.6767, and 0.6849, respectively. For end-to-end temporal relation, it achieved 0.5904, 0.5944, 0.5921, and 0.5924, respectively. With the F measure used for evaluation, we were ranked first out of 14 competing teams (event extraction), first out of 14 teams (timex extraction), third out of 12 teams (temporal relation), and second out of seven teams (end-to-end temporal relation). The system achieved encouraging results, demonstrating the feasibility of the tasks defined by the i2b2 organizers. The experiment result demonstrates that both global and local information is useful in the 2012 challenge.

ROSeAnn

Named entity extractors can be used to enrich both text and Web documents with semantic annotations. While originally focused on a few standard entity types, the ecosystem of annotators is becoming increasingly diverse, with recognition capabilities ranging from generic to specialised entity types. Both the overlap and the diversity in annotator vocabularies motivate the need for managing and integrating semantic annotations: allowing users to see the results of multiple annotations and to merge them into a unified solution. We demonstrate ROSEANN, a system for the management of semantic annotations. ROSEANN provides users with a unified view over the opinion of multiple independent annotators both on text and Web documents. It allows users to understand and reconcile conflicts between annotations via ontology-aware aggregation. ROSEANN incorporates both supervised aggregation, appropriate when representative training data is available, and an unsupervised method based on the notion of weighted-repair.

Analysis of Textual Data Based on Inductive Learning Techniques

This paper introduces knowledge discovery methods based on inductive learning techniques from textual data. The author argues three methods extracting features of the textual data. First one activates a key concept dictionary, second one does a key phrase pattern dictionary, and third one does a named entity extractor. These features are used in order to generate rules representing relationships between the features and text classes. The rules are described in the format of a fuzzy decision tree. Also, these features are used in order to acquire a classification model based on SVM (Support Vector Machine). The model can classify new textual data into the text classes with high classification accuracy. Lastly, this paper introduces two application tasks based on these methods and verifies the effect of the methods.

Arabic Semantic Web Applications – A Survey

Arabic Language is the mother tongue for 23 countries and more than 350 million persons. It is the language of the Holy Quran; therefore, many non-Arabic Islamic countries, like Pakistan, teach Arabic as a second language. Nevertheless, it is observable that the Arabic content on the Web is less than what should be. The evolution of the Semantic Web (SW) added a new dimension to this problem. This paper is an attempt to figure out the problem, its causes, and to open avenues to think about the solutions. The survey presented in this paper concerned with the SW applications regarding the Arabic Language in the domains of Ontology construction and utilization, Arabic WordNet (AWN) exploiting and enrichment, Arabic Named Entities Extraction, Holy Quran and Islamic Knowledge semantic representation, and Arabic Semantic Search Engines. In fact, the study revealed serious deficiencies in dealing semantically with the Arabic Language. That is mainly owing to the rarity of tools that can support the Arabic script. Furthermore, the Arabic resources, if available, are not free. Moreover, there are many technical problems in the semantic dealing with the Arabic context. Therefore, most of the developed applications are not sufficiently proficient. However, due to the significance of the Arabic Language, it is inevitable to overcome these deficiencies in order to put the Arabic Language in the category of the machine-semantically- interpretable languages, rather than just the textually processable ones. This way, we can exploit the power of the Semantic Web features in extracting the essence of the knowledge residing in the Arabic web documents and going beyond dealing with its rigid texts.

ANEEC: A Quasi-Automatic System for Massive Named Entity Extraction and Categorization

Named entity recognition seeks to locate atomic elements in texts and classify them into predefined categories. It is essentially useful for many applications, including microblog analysis and query suggestion. In recent years, with the explosion of Web 2.0, people have found it a promising way to extract large-scale, high-quality entities from structured web content. However, existing studies seldom provide an integrated system for simultaneously extracting and categorizing both the head and tail entities, and the identification of ambiguous entities is still a challenging task. In light of these, we propose a system named quasi-Automatic Named Entity Extraction and Categorization (ANEEC) for massive named-entity management. Specifically, ANEEC first identifies representative websites by using a small seed-set of entities and the query logs of a search engine, and then extracts high-quality entities from the parallel structures in the webpages. ANEEC then employs the extracted entities and their corresponding atom-level groups to establish an entity taxonomy as well as a hierarchical classifier ensemble. Two problems, i.e. definition abnormality and granularity unfitness, have also been addressed to further improve the quality of the taxonomy. An application case using 932 seed entities and the query logs of the search engine Bing demonstrates that ANEEC can effectively identify over 870 000 named entities in 32 bottom-level categories, and the resulting taxonomy has an excellent classification performance with F 1 =85.17%, provided that the entity features are properly preprocessed and weighted. In particular, ANEEC shows the potential for tail entity recognition and ambiguous entity detection.

The Study on Enlarging Specific Extractor for Technology-Related Named Entity Extraction from Text Collections of Applied Mechanics Field

This paper presents additional linguistic factors that should be considered to more effectively extract terms from the machinery industry documents by augmenting the general extraction patterns. We expand on the general term extraction patterns with patterns that are tailored for machinery industry documents to improve precision and recall. We establish a theoretical basis for developing a system to support information research in the machinery industry. Using this system, we expect to increase the efficiency of new business planning process in the machine industry.

A new multiword expression metric and its applications

Multiword Expressions (MWEs) appear frequently and ungrammatically in natural languages. Identifying MWEs in free texts is a very challenging problem. This paper proposes a knowledge-free, unsupervised, and language-independent Multiword Expression Distance (MED). The new metric is derived from an accepted physical principle, measures the distance from an n-gram to its semantics, and outperforms other state-of-the-art methods on MWEs in two applications: question answering and named entity extraction.