Liana

1975 ◽  
Vol 30 ◽  
pp. 1-24
Author(s):  
I. Batoni ◽  
R. Henning ◽  
H. Lehmann ◽  
B. Schirmer ◽  
M. Zoeppritz
Keyword(s):  
Natural Language ◽  
Question Answering ◽  
Semantic Analysis ◽  
Tree Structure ◽  
Semantic Interpretation ◽  
Syntactic Analysis ◽  
Question Answering System ◽  
Continuous Text ◽  
Language Input

Abstract LIANA is a question answering system in PL/1. The program takes German natural language input and, by morphological, syntactic and semantic analysis, creates a representation of the text, which is stored and can be accessed for retrieval purposes. All individuals (objects) mentioned in the sentence are found and stored. In continuous text, therefore, information about individuals can be piled up successively. LIANA uses the programming concept of the Boston Syntax Analyzer. Therefore, the output of syntactic analysis is a tree structure, simulated through pointers which connect the nodes in the tree. Each node is associated with a feature table which is operated on by the semantic interpretation. Node and feature handling is facilitated by a set of macros for adding, erasing, and checking features and copying, deleting, and inserting nodes.

Performance of natural language classifiers in a question-answering system

2017 ◽  
Vol 61 (4/5) ◽  
pp. 14:1-14:10 ◽  
Author(s):  
R. Bakis ◽  
D. P. Connors ◽  
P. Dube ◽  
P. Kapanipathi ◽  
A. Kumar ◽  
...  
Keyword(s):  
Natural Language ◽  
Question Answering ◽  
Question Answering System

BUILD KNOWLEDGE GRAPH FROM HETEROGENEOUS DOCUMENTS

2021 ◽  
Vol 47 (05) ◽  
Author(s):  
NGUYỄN CHÍ HIẾU
Keyword(s):  
Information Retrieval ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Semantic Analysis ◽  
Knowledge Graph ◽  
Question Answering Systems ◽  
Knowledge Graphs

Knowledge Graphs are applied in many fields such as search engines, semantic analysis, and question answering in recent years. However, there are many obstacles for building knowledge graphs as methodologies, data and tools. This paper introduces a novel methodology to build knowledge graph from heterogeneous documents.  We use the methodologies of Natural Language Processing and deep learning to build this graph. The knowledge graph can use in Question answering systems and Information retrieval especially in Computing domain

Parsing

2012 ◽  
Author(s):  
John Carroll
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Real World ◽  
Level Of Detail ◽  
Semantic Interpretation ◽  
Syntactic Analysis ◽  
Real World Applications ◽  
Grammar Formalisms ◽  
Speech Recognizer ◽  
Context Free

This article introduces the concepts and techniques for natural language (NL) parsing, which signifies, using a grammar to assign a syntactic analysis to a string of words, a lattice of word hypotheses output by a speech recognizer or similar. The level of detail required depends on the language processing task being performed and the particular approach to the task that is being pursued. This article further describes approaches that produce ‘shallow’ analyses. It also outlines approaches to parsing that analyse the input in terms of labelled dependencies between words. Producing hierarchical phrase structure requires grammars that have at least context-free (CF) power. CF algorithms that are widely used in parsing of NL are described in this article. To support detailed semantic interpretation more powerful grammar formalisms are required, but these are usually parsed using extensions of CF parsing algorithms. Furthermore, this article describes unification-based parsing. Finally, it discusses three important issues that have to be tackled in real-world applications of parsing: evaluation of parser accuracy, parser efficiency, and measurement of grammar/parser coverage.

A Persian Medical Question Answering System

2020 ◽  
Vol 29 (06) ◽  
pp. 2050019
Author(s):  
Hadi Veisi ◽  
Hamed Fakour Shandi
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Document Retrieval ◽  
Main Concept ◽  
Question Answering System ◽  
Detection Algorithms ◽  
Persian Language ◽  
Part Of Speech ◽  
Answer Extraction

A question answering system is a type of information retrieval that takes a question from a user in natural language as the input and returns the best answer to it as the output. In this paper, a medical question answering system in the Persian language is designed and implemented. During this research, a dataset of diseases and drugs is collected and structured. The proposed system includes three main modules: question processing, document retrieval, and answer extraction. For the question processing module, a sequential architecture is designed which retrieves the main concept of a question by using different components. In these components, rule-based methods, natural language processing, and dictionary-based techniques are used. In the document retrieval module, the documents are indexed and searched using the Lucene library. The retrieved documents are ranked using similarity detection algorithms and the highest-ranked document is selected to be used by the answer extraction module. This module is responsible for extracting the most relevant section of the text in the retrieved document. During this research, different customized language processing tools such as part of speech tagger and lemmatizer are also developed for Persian. Evaluation results show that this system performs well for answering different questions about diseases and drugs. The accuracy of the system for 500 sample questions is 83.6%.

scholarly journals Basic challenges in natural language processing systems

2020 ◽  
pp. 41-45
Author(s):  
O. Hyryn
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Information Search ◽  
Question Answering ◽  
Syntactic Analysis ◽  
Anaphora Resolution ◽  
Grammatical Structure ◽  
English Sentence ◽  
Improved Model

The article proceeds from the intended use of parsing for the purposes of automatic information search, question answering, logical conclusions, authorship verification, text authenticity verification, grammar check, natural language synthesis and other related tasks, such as ungrammatical speech analysis, morphological class definition, anaphora resolution etc. The study covers natural language processing challenges, namely of an English sentence. The article describes formal and linguistic problems, which might arise during the process and which are connected with graphic, semantic, and syntactic ambiguity. The article provides the description of how the problems had been solved before the automatic syntactic analysis was applied and the way, such analysis methods could be helpful in developing new analysis algorithms today. The analysis focuses on the issues, blocking the basis for the natural language processing — parsing — the process of sentence analysis according to their structure, content and meaning, which aims to examine the grammatical structure of the sentence, the division of sentences into constituent components and defining links between them. The analysis identifies a number of linguistic issues that will contribute to the development of an improved model of automatic syntactic analysis: lexical and grammatical synonymy and homonymy, hypo- and hyperonymy, lexical and semantic fields, anaphora resolution, ellipsis, inversion etc. The scope of natural language processing reveals obvious directions for the improvement of parsing models. The improvement will consequently expand the scope and improve the results in areas that already employ automatic parsing. Indispensable achievements in vocabulary and morphology processing shall not be neglected while improving automatic syntactic analysis mechanisms for natural languages.

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