Research toward the development of a lexical knowledge base for natural language processing

The article describes a classification system with natural language processing. Many systems use neural networks, but it needs massive amounts of data for training, which is not always available. Authors propose to use ontologies in such systems. As example of such approach it is shown the classification system, which helps to form a list of the best candidates during the recruitment process. An overview of the methods for ontologies constructing and language analyzers appropriate for classification systems are presented. The system in the form of a knowledge base is constracted. Described system supports Ukrainian and English languages. The possible ways of system expansion is regarded.

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Coreference resolution Ontology-based in sentiment analysis

Science and Technology Development Journal ◽

10.32508/stdj.v20ik9.1673 ◽

2019 ◽

Vol 20 (K9) ◽

pp. 23-30

Author(s):

Le Thi Thuy ◽

Phan Thi Tuoi ◽

Quan Thanh Tho

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Knowledge Base ◽

Sentiment Analysis ◽

Language Processing ◽

Evaluation Methods ◽

English Text ◽

Coreference Resolution ◽

Research Topics ◽

Reference Resolution

Entity co-reference resolution and sentiment analysis are independent problems and popular research topics in the community of natural language processing. However, the combination of those two problems has not been getting much attention. Thus, this paper susgests to apply knowledge base to solve co- reference between object and aspect with sentiment. In addition, the paper also proposes the model of Ontology-based co-reference resolution in sentiment analysis for English text. Finally, we also discuss evaluation methods applied for our model and the results obtained.

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Development of an Intelligent NLP-Based Audit Plan Knowledge Discovery System

Journal of Emerging Technologies in Accounting ◽

10.2308/jeta-52665 ◽

2019 ◽

Vol 17 (1) ◽

pp. 89-97

Author(s):

Qiao Li ◽

Junming Liu

Keyword(s):

Natural Language Processing ◽

Decision Support ◽

Natural Language ◽

Knowledge Base ◽

Knowledge Discovery ◽

Language Processing ◽

Discovery System

ABSTRACT Auditors' discussions in audit plan brainstorming sessions provide valuable knowledge on how audit engagement teams evaluate information, identify and assess risks, and make audit decisions. Collected expertise and experience from experienced auditors can be used as decision support for future audit plan engagements. With the help of Natural Language Processing (NLP) techniques, this paper proposes an intelligent NLP-based audit plan knowledge discovery system (APKDS) that can collect and extract important contents from audit brainstorming discussions and transfer the extracted contents into an audit knowledge base for future use.

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A Composite Natural Language Processing and Information Retrieval Approach to Question Answering Using a Structured Knowledge Base

International Journal of Semantic Computing ◽

10.1142/s1793351x17400141 ◽

2017 ◽

Vol 11 (03) ◽

pp. 345-371

Author(s):

Avani Chandurkar ◽

Ajay Bansal

Keyword(s):

Information Retrieval ◽

Natural Language Processing ◽

Natural Language ◽

Knowledge Base ◽

Language Processing ◽

Question Answering ◽

Automated System ◽

Free Form ◽

Question Answering System ◽

Novel Approach

With the inception of the World Wide Web, the amount of data present on the Internet is tremendous. This makes the task of navigating through this enormous amount of data quite difficult for the user. As users struggle to navigate through this wealth of information, the need for the development of an automated system that can extract the required information becomes urgent. This paper presents a Question Answering system to ease the process of information retrieval. Question Answering systems have been around for quite some time and are a sub-field of information retrieval and natural language processing. The task of any Question Answering system is to seek an answer to a free form factual question. The difficulty of pinpointing and verifying the precise answer makes question answering more challenging than simple information retrieval done by search engines. The research objective of this paper is to develop a novel approach to Question Answering based on a composition of conventional approaches of Information Retrieval (IR) and Natural Language processing (NLP). The focus is on using a structured and annotated knowledge base instead of an unstructured one. The knowledge base used here is DBpedia and the final system is evaluated on the Text REtrieval Conference (TREC) 2004 questions dataset.

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Lexical Knowledge Acquisition

10.1093/oxfordhb/9780199276349.013.0021 ◽

2012 ◽

Author(s):

Yuji Matsumoto

Keyword(s):

Natural Language Processing ◽

Mutual Information ◽

Natural Language ◽

Language Processing ◽

Lexical Knowledge ◽

Lexical Representations ◽

Domain Specific ◽

Text Corpora ◽

Corpus Data ◽

Machine Readable

This article deals with the acquisition of lexical knowledge, instrumental in complementing the ambiguous process of NLP (natural language processing). Imprecise in nature, lexical representations are mostly simple and superficial. The thesaurus would be an apt example. Two primary tools for acquiring lexical knowledge are ‘corpora’ and ‘machine-readable dictionary’ (MRD). The former are mostly domain specific, monolingual, while the definitions in MRD are generally described by a ‘genus term’ followed by a set of differentiae. Auxiliary technical nuances of the acquisition process, find mention as well, such as ‘lexical collocation’ and ‘association’, referring to the deliberate co-occurrence of words that form a new meaning altogether and loses it whenever a synonym replaces either of the words. The first seminal work on collocation extraction from large text corpora, was compiled around the early 1990s, using inter-word mutual information to locate collocation. Abundant corpus data would be obtainable from the Linguistic Data Consortium (LDC).

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Intelligent Support for Building Knowledge Bases for Natural Language Processing

Intelligent Assistant Systems ◽

10.4018/978-1-59140-878-9.ch005 ◽

2007 ◽

pp. 86-113 ◽

Cited By ~ 1

Author(s):

Son B. Pham ◽

Achim Hoffmann

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Knowledge Base ◽

Language Processing ◽

Current Knowledge ◽

Knowledge Bases ◽

Intelligent Support ◽

Current Knowledge Base ◽

Complex Knowledge ◽

Comparable Quality

In this chapter we discuss ways of assisting experts to develop complex knowledge bases for a variety of natural language processing tasks. The proposed techniques are embedded into an existing knowledge acquisition framework, KAFTIE, specifically designed for building knowledge bases for natural language processing. Our intelligent agent, the rule suggestion module within KAFTIE, assists the expert by suggesting new rules in order to address incorrect behavior of the current knowledge base. The suggested rules are based on previously entered rules which were “hand-crafted” by the expert. Initial experiments with the new rule suggestion module are very encouraging as they resulted in a more compact knowledge base of comparable quality to a fully hand-crafted knowledge base. At the same time the development time for the more compact knowledge base was considerably reduced.

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