Named Entity Recognition

Mohit, Behrang

doi:10.1007/978-3-642-45358-8_7

Cited by 119 publications

(51 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We used word co-occurrence and grammatical dependencies [42] using the Stanford NLP toolkit [25]. Then we detected the specific names within the summaries using rule-based Named-Entity Recognition (NER) [28]. Rule-based NER uses a set of named entity extraction rules for different types of named entity classes with an engine that applies the rules and the lexicons to the text [28].…”

Section: Create Ticket Contentmentioning

confidence: 99%

ESSMArT way to manage customer requests

et al. 2019

View full text Add to dashboard Cite

Quality and market acceptance of software products is strongly influenced by responsiveness to user requests. Once a request is received from a customer, decisions need to be made if the request should be escalated to the development team. Once escalated, the ticket must be formulated as a development task and be assigned to a developer. To make the process more efficient and reduce the time between receiving and escalating the user request, we aim to automate of the complete user request management process. We propose a holistic method called ESSMArT. The methods performs text summarization, predicts ticket escalation, creates the title and content of the ticket used by developers, and assigns the ticket to an available developer. We internally evaluated the method by 4,114 user tickets from Brightsquid and their secure health care communication platform Secure-Mail. We also perform an external evaluation on the usefulness of the approach. We found that supervised learning based on context specific data performs best for extractive summarization. For predicting escalation of tickets, Random Forest trained on a combination of conversation and extractive summarization is best with highest precision (of 0.9) and recall (of 0.55). From external evaluation we found that ESSMArT provides suggestions that are 71% aligned with human ones. Applying the prototype implementation to 315 user requests

show abstract

Section: Create Ticket Contentmentioning

confidence: 99%

ESSMArT way to manage customer requests

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Based on the aforementioned decomposition analysis and the types of fields that are revealed, semi-automatic techniques of information extraction can be further employed to populate the ontology with specific instances [for such techniques, see Mohit (2014)]. …”

Section: Resultsmentioning

confidence: 99%

Toward an Ontopedia for Historical Hebrew Manuscripts

Zhitomirsky‐Geffet

Prebor

2016

Front. Digit. Humanit.

View full text Add to dashboard Cite

Historical handwritten Hebrew manuscripts are one of the most unique and authentic witnesses of Jewish culture and thought that survived through the centuries. In order to enable a systematic research of the knowledge embedded in the manuscripts, there is a need for a formal conceptual data model with a high level of semantic granularity, an ontology. We propose to build a dynamic web-based framework that will allow scholars to create, enrich, and consult an "ontopedia" (ontology-based encyclopedia) of Hebrew manuscripts. The framework is based on an ontology especially designed and implemented for this domain and goals. We view a manuscript as a "living entity" and propose to design a new ontological data model of the narrative for a manuscript, stages/milestones in its biography (creation, copying, and acquisition). A sequence of events and places constitutes a timeline of history against which manuscripts, people, and their relationships can be placed. A large-scale automated reasoning based on the ontology will also enable us to construct a semantically rich social network of people and manuscripts, and to compare the effect of time and place on the manuscripts' qualitative characteristics and quantitative distribution.

show abstract

“…There are two main NER categories: Rule-Based and Statistical NER. Prior work showed that statistical NER is more robust than the rule-based [37,30,34]. In the rule-based NER, the user should come up with different rules to extract the entities while in the statistical NER the user trains a machine learning model on an annotated data with the named entities and their types in order to allow the model to extract and classify the entities.…”

Section: Entity Recognizermentioning

confidence: 99%

MSRBot: Using bots to answer questions from software repositories

Abdellatif¹,

Badran²,

Shihab³

2020

Empir Software Eng

View full text Add to dashboard Cite

Software repositories contain a plethora of useful information that can be used to enhance software projects. Prior work has leveraged repository data to improve many aspects of the software development process, such as, help extract requirement decisions, identify potentially defective code and improve maintenance and evolution. However, in many cases, practitioners are not able to fully benefit from software repositories due to the fact that they need special expertise and dedicated effort to mine their repositories.Therefore, in this paper, we use bots to automate and ease the process of extracting useful information from software repositories. Particularly, we lay out an approach of how bots, layered on top of software repositories, can be used to answer some of the most common software development/maintenance questions facing developers. We perform a preliminary study with 12 participants to validate the effectiveness of the bot. Our findings indicate that using bots achieves very promising results in terms of answer accuracy, speed and usefulness. Our work has the potential to transform the MSR field by significantly lowering the barrier to entry, making the extraction of useful information from software repositories as easy as chatting with a bot.

show abstract

Named Entity Recognition

Cited by 119 publications

References 35 publications

ESSMArT way to manage customer requests

ESSMArT way to manage customer requests

Toward an Ontopedia for Historical Hebrew Manuscripts

MSRBot: Using bots to answer questions from software repositories

Contact Info

Product

Resources

About