Design of Belarusian and Russian natural language interfaces for online help systems

O b j e c t i v e s. The main goal of the work is a research of the natural language user interfaces and the developmentof a prototype of such an interface. The prototype is a bilingual Russian and Belarusian question-and-answer dialogue system. The research of the natural language interfaces was conducted in terms of the use of natural language for interaction between a user and a computer system. The main problems here are the ambiguity of natural language and the difficulties in the design of natural language interfaces that meet user expectations.M e t ho d s. The main principles of modelling the natural language user interfaces are considered. As an intelligent system, it consists of a database, knowledge machine and a user interface. Speech recognition and speech synthesis components make natural language interfaces more convenient from the point of view of usability.R e s u l t s. The description of the prototype of a natural language interface for a question-and-answer intelligent system is presented. The model of the prototype includes speech-to-text and text-to-speech Belarusian and Russian subsystems, generation of responses in the form of the natural language and formal text.An additional component is natural Belarusian and Russian voice input. Some of the data, required for human voice recognition, are stored as knowledge in the knowledge base or created on the basis of existing knowledge. Another important component is Belarusian and Russian voice output. This component is the top required for making the natural language interface more user-friendly.Co n c l u s i o n. The article presents the research of natural language user interfaces, the result of which provides the development and description of the prototype of the natural language interface for the intelligent question- and-answer system.

ANEC: Artificial Named Entity Classifier based on BI-LSTM for an AI-based Business Analyst

Business users across enterprises today rely on reports and dashboards created by IT organizations to understand the dynamics of their business better and get insights into the data. In many cases, these users are underserved and do not possess the technical skillset to query the data source to get the information they need. There is a need for users to access information in the most natural way possible. AI-based Business Analysts are going to change the future of business analytics and business intelligence by providing a natural language interface between the user and data. This natural language interface can understand ambiguous questions from users, the intent and convert the same into a database query. One of the important elements of an AI-based business analyst is to interpret a natural language question. It also requires identification of key business entities within the question and relationship between them to generate insights. The Artificial Named Entity Classifier (ANEC) helps us take a huge step forward in that direction by not only identifying but also classifying entities with the help of the sequence recognising prowess of BiLSTMs.

Empowering Natural Language Interfaces to Databases with Aggregations

A Natural Language Interface to Database (NLIDB) refers to a database interface that translates a question asked in natural language into a structured query. Aggregation questions express aggregation functions, such as count, sum, average, minimum and maximum, and optionally a group by clause and a having clause. NLIDBs deliver good results for standard questions but usually do not deal with aggregation questions. The main contribution of this article is a generic module, called GLAMORISE (GeneraL Aggregation MOdule using a RelatIonal databaSE), that extends NLIDBs to cope with aggregation questions. GLAMORISE covers aggregations with ambiguities, timescale differences, aggregations in multiple attributes, the use of superlative adjectives, basic recognition of measurement units, and aggregations in attributes with compound names.

Combining Logic and Natural Language Processing to Support Investment Management

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

Hash Tables for a Digital Lexicon

This paper deals with the construction of digital lexicons within the scope of Natural Language Processing. Data Structures called Hash Tables have demonstrated to generate good results for Natural Language Interface for Databases and have data dispersion, response speed and programming simplicity as main features. The storage of the desired information is done by associating a key through the hashing functions that is responsible for distributing the information in this table. The objective of this paper is to present the tool called Visual TaHs that uses a sparse table to a real lexicon (Lexicon of Herbs), improving performance results of several implemented hash functions. Such structure has achieved satisfactory results in terms of speed and storage when compared to conventional databases and can work in various media, such as desktop, Web and mobile.

TREE-BASED SEMANTIC ANALYSIS METHOD FOR NATURAL LANGUAGE PHRASE TO FORMAL QUERY CONVERSION

Context. This work is devoted to the problem of natural language interface construction for ontological graph databases. The focus here is on the methods for the conversion of natural language phrases into formal queries in SPARQL and CYPHER query languages. Objective. The goals of the work are the creation of a semantic analysis method for the input natural language phrases semantic type determination and obtaining meaningful entities from them for query template variables initialization, construction of flexible query templates for the types, development of program implementation of the proposed technique. Method. A tree-based method was developed for semantic determination of a user’s phrase type and obtaining a set of terms from it to put them into certain places of the most suiting formal query template. The proposed technique solves the tasks of the phrase type determination (and this is the criterion of the formal query template selection) and obtaining meaningful terms, which are to initialize variables of the chosen template. In the current work only interrogative and incentive user’s phrases are considered i.e. ones that clearly propose the system to answer or to do something. It is assumed that the considered dialog or reference system uses a graph ontological database, which directly impacts the formal query patterns – the resulting queries are destined to be in SPARQL or Cypher query languages. The semantic analysis examples considered in this work are aimed primarily at inflective languages, especially, Ukrainian and Russian, but the basic principles could be suitable to most of the other languages. Results. The developed method of natural language phrase to a formal query in SPARQL and CYPHER conversion has been implemented in software for Ukrainian and Norwegian languages using narrow subjected ontologies and tested against formal performance criteria. Conclusions. The proposed method allows the dialog system fast and with minimum number of steps to select the most suitable query template and extract informative entities from a natural language phrase given the huge phrase variability in inflective languages. Carried out experiments have shown high precision and reliability of the constructed system and its potential for practical usage and further development.