scholarly journals Combining Logic and Natural Language Processing to Support Investment Management

2021 ◽  
Author(s):  
Marjolein Deryck ◽  
Nuno Comenda ◽  
Bart Coppens ◽  
Joost Vennekens
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Financial Assets ◽  
Logical Inference ◽  
Multiple Forms ◽  
Automatic Translation ◽  
Natural Language Interface ◽  
The Creation ◽  
Reasoning Engine ◽  
Selection Of

This paper presents an application that we developed to assist users with the creation of an investment profile for the selection of financial assets. It consists of a natural language interface, an automatic translation to a declarative FO(.) knowledge base, and the IDP reasoning engine with multiple forms of logical inference. The application speeds up the investment profile creation process, and reduces the considerable inherent operational risk linked to the creation of investment profiles

scholarly journals A Novel Solution for the Aggregation Problem in Natural Language Interface to Databases (NLIDB)

2020 ◽  
Author(s):  
Alexandre Ferreira Novello ◽  
Marco Antonio Casanova
Keyword(s):  
Natural Language ◽  
Basic Unit ◽  
Aggregation Functions ◽  
Natural Language Interface ◽  
Aggregation Problem ◽  
The Creation ◽  
Multiple Attributes ◽  
Generic Module

Natural Language Interface to Databases (NLIDB) systems usually do not deal with aggregations, which can be of two types: aggregation functions (such as count, sum, average, minimum, and maximum) and grouping functions (GROUP BY). This paper addresses the creation of a generic module, to be used in NLIDB systems, that allows such systems to perform queries with aggregations, on the condition that the query results the NLIDB returns are or can be transformed into tables. The paper covers aggregations with specificities, such as ambiguities, timescale differences, aggregations in multiple attributes, the use of superlative adjectives, basic unit measure recognition, and aggregations in attributes with compound names.

scholarly journals The Representation of the Temporal Properties of Linguistic Utterances through a Mathematical Model

2020 ◽  
Vol 42 (2) ◽  
pp. 22-49
Author(s):  
Ismael Teomiro ◽  
María Beatriz Pérez Cabello de Alba
Keyword(s):  
Mathematical Model ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Search Engines ◽  
Dimensional Space ◽  
Mathematical Objects ◽  
Automatic Translation ◽  
Temporal Properties ◽  
Translation Systems

In this article we use a mathematical model to encode the temporal properties of linguistic utterances across languages by means of mathematical objects—points, lines, segments, vectors and versors—and the relations established among them in a four-dimensional space. Such temporal properties are encoded through threedifferent systems: tense—past, present and future—which locates the utterance on a temporal line, aspect—perfectivity and progressivity—which sets the viewpoint of the speaker, and Aktionsart, which refers to the structural temporal properties of the utterance such as telicity—whether the event has an endpoint or not—dynamicity—whether a change is conveyed or not—and duration. This model aims to be language independent in order to allow for the codification of the temporal properties of utterances in any language, thus rendering it appropriate to be used as an interlingua in Natural Language Processing (NLP) applications. This wouldsignificantly improve the comprehension of natural language in search engines and automatic translation systems, to name two examples. Hence, our ultimate goal is for this model to achieve computational adequacy.

scholarly journals APLIKASI AGEN CERDAS UNTUK PEMBELAJARAN PEMPROGRAMAN BERORIENTASI OBJEK BERBASIS NATURAL LANGUAGE PROCESSING (NLP)

2018 ◽  
Author(s):  
Khairil Anam ◽  
SEHMAN
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
High Performance ◽  
Object Oriented Programming ◽  
Learning System ◽  
Computer Laboratory ◽  
Final Project ◽  
Laboratory Learning ◽  
The Creation

The existence of a touch of technology on laboratory learning becomes another alternative as a supporter of laboratory learning. Different practitioner's wishes and intensity of relatively short laboratory practice which resulted in dissatisfaction in the implementation of a practicum. Thus, an intelligent learning alternative is needed. This intelligent learning aims to provide high-quality and high-performance training skills that can assist the practitioner in solving problems related to practicum materials. The intelligent learning system is a learning system that handles some student instruction without any intervention from a teacher.Alternative learning system that can support the creation of Intelligent Learning System is by Natural Language Processing (NLP) method. This final project provides an explanation of the creation and implementation of intelligent learning systems in the Object Oriented Programming Computer Laboratory. This system consists of several stages: parsing, similarity, stemming, Knowledge Base which is designed in an interactive form between praktikan and agent based dialoge based application. The success rate of this system in answering questions from praktikan session II is 88.75%.

Understanding Romanian Texts by Using Gamification Methods

2019 ◽  
Vol 9 (1) ◽  
pp. 52-57
Author(s):  
Ștefania-Eliza Berghia ◽  
Bogdan Pahomi ◽  
Daniel Volovici
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Choice Theory ◽  
Social Choice Theory ◽  
Parts Of Speech ◽  
Automatic Translation ◽  
E Learning ◽  
Syntactic Function

AbstractIn recent years, there has been increasing interest in the field of natural language processing. Determining which syntactic function is right for a specific word is an important task in this field, being useful for a variety of applications like understanding texts, automatic translation and question-answering applications and even in e-learning systems. In the Romanian language, this is an even harder task because of the complexity of the grammar. The present paper falls within the field of “Natural Language Processing”, but it also blends with other concepts such as “Gamification”, “Social Choice Theory” and “Wisdom of the Crowd”. There are two main purposes for developing the application in this paper:a) For students to have at their disposal some support through which they can deepen their knowledge about the syntactic functions of the parts of speech, a knowledge that they have accumulated during the teaching hours at schoolb) For collecting data about how the students make their choices, how do they know which grammar role is correct for a specific word, these data being primordial for replicating the learning process

Ontology Formation and Comparison for Syllabus Structure Using NLP

2021 ◽  
Author(s):  
Masoom Raza ◽  
Aditee Patil ◽  
Mangesh Bedekar ◽  
Rashmi Phalnikar ◽  
Bhavana Tiple
Keyword(s):  
Natural Language Processing ◽  
Software Engineering ◽  
Natural Language ◽  
Comparative Study ◽  
Language Processing ◽  
Shared Knowledge ◽  
The Subject ◽  
The Creation ◽  
The Right ◽  
Collection And Management

Ontologies are largely responsible for the creation of a framework or taxonomy for a particular domain which represents the shared knowledge, concepts and how these concepts are related with each other. This paper shows the usage of ontology for the comparison of a syllabus structure of universities. This is done with the extraction of the syllabus, creation of ontology for the representing syllabus, then parsing the ontology and applying Natural language processing to remove unwanted information. After getting the appropriate ontologies, a comparative study is made on them. Restrictions are made over the extracted syllabus to the subject “Software Engineering” for convenience. This depicts the collection and management of ontology knowledge and processing it in the right manner to get the desired insights.

scholarly journals Clinical Natural Language Processing in 2015: Leveraging the Variety of Texts of Clinical Interest

2016 ◽  
Vol 25 (01) ◽  
pp. 234-239 ◽  
Author(s):  
P. Zweigenbaum ◽  
A. Névéol ◽  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Clinical Information ◽  
Bibliographic Databases ◽  
Sources Of Information ◽  
Multiple Sources ◽  
Clinical Interest ◽  
Clinical Natural Language Processing ◽  
Selection Of

Summary Objective: To summarize recent research and present a selection of the best papers published in 2015 in the field of clinical Natural Language Processing (NLP). Method: A systematic review of the literature was performed by the two section editors of the IMIA Yearbook NLP section by searching bibliographic databases with a focus on NLP efforts applied to clinical texts or aimed at a clinical outcome. Section editors first selected a shortlist of candidate best papers that were then peer-reviewed by independent external reviewers. Results: The clinical NLP best paper selection shows that clinical NLP is making use of a variety of texts of clinical interest to contribute to the analysis of clinical information and the building of a body of clinical knowledge. The full review process highlighted five papers analyzing patient-authored texts or seeking to connect and aggregate multiple sources of information. They provide a contribution to the development of methods, resources, applications, and sometimes a combination of these aspects. Conclusions: The field of clinical NLP continues to thrive through the contributions of both NLP researchers and healthcare professionals interested in applying NLP techniques to impact clinical practice. Foundational progress in the field makes it possible to leverage a larger variety of texts of clinical interest for healthcare purposes.

scholarly journals XenC: An Open-Source Tool for Data Selection in Natural Language Processing

2013 ◽  
Vol 100 (1) ◽  
pp. 73-82 ◽  
Author(s):  
Anthony Rousseau
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Open Source ◽  
Language Processing ◽  
Statistical Machine Translation ◽  
Data Selection ◽  
Specific Domain ◽  
Open Source Tool ◽  
The Difference ◽  
Selection Of

Abstract In this paper we describe XenC, an open-source tool for data selection aimed at Natural Language Processing (NLP) in general and Statistical Machine Translation (SMT) or Automatic Speech Recognition (ASR) in particular. Usually, when building a SMT or ASR system, the considered task is related to a specific domain of application, like news articles or scientific talks for instance. The goal of XenC is to allow selection of relevant data regarding the considered task, which will be used to build the statistical models for such a system. It is done by computing the difference between cross-entropy scores of sentences from a large out-of-domain corpus and sentences from a corpus considered as in-domain for the task. Written in C++, this tool can operate on monolingual or bilingual data and is language-independent. XenC, now part of the LIUM toolchain for SMT, is actively developed since December 2011 and used in many MT projects.

Natural language interface to video database

2001 ◽  
Vol 7 (1) ◽  
pp. 1-27 ◽  
Author(s):  
T. R. GAYATRI ◽  
S. RAMAN
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Input Word ◽  
Video Database ◽  
Video Objects ◽  
Natural Language Interface ◽  
Different Types ◽  
Natural Language Query ◽  
Slot Filling ◽  
Three Phases

In this paper, we discuss a natural language interface to a database of structured textual descriptions in the form of annotations of video objects. The interface maps the natural language query input on to the annotation structures. The language processing is done in three phases of expectations and implications from the input word, disambiguation of noun implications and slot-filling of prepositional expectations, and finally, disambiguation of verbal expectations. The system has been tested with different types of user inputs, including ill-formed sentences, and studied for erroneous inputs and for different types of portability issues.

scholarly journals NLP TOKEN MATCHING ON DATABASE USING BINARY SEARCH

2012 ◽  
Vol 3 (1) ◽  
pp. 140-143
Author(s):  
Ekta Aggarwal ◽  
Shreeja Nair
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Time Complexity ◽  
Binary Search ◽  
Translation Process ◽  
Query Translation ◽  
Natural Language Text ◽  
Natural Language Interface ◽  
Reduced Time ◽  
Language Text

Natural Language Processing (NLP) is an area of research and application that explores how computers can be used to understand and manipulate natural language text or speech to do useful things. The paper deals with the concept of database where by the data resources data can be fetched and accessed accordingly with reduced time complexity. The retrieval techniques are pointed out based on the ideas of binary search. A natural language interface refers to words in its own dictionary as well as to the words in the standard dictionary, in order to interpret a query. The main contribution of this investigation is addressing the problem of improving the accuracy of the query translation process by using the information provided by the database schema.  

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