NLU

The next major feature of the age of conversational services is chatbots in the new era of technology. A Chatbot
framework is a software program that uses natural language to communi- cate with users. Chatbots is a virtual entity that can
effectively explore the use of digital textual competencies with any hu- man being. Recently, their growth as a medium of
conversa- tion between people and computers has taken a great step for- ward. The aim of the chatbot framework for machine
learn- ing and artificial intelligence is to simulate a human conver-sation, maybe through text or speech. Natural Language Processing understands one or more human languages through chatbot software. To simulate informal chat communication, the
chatbot structure combines a language model and compu- tational algorithms covering enormous natural language pro- cessing
techniques. This paper discusses other applications that may be useful for chatbots, such as a computer conversa- tion system,
virtual agent, dialogue system, retrieval of infor- mation, industry, telecommunications, banking, health, cus- tomer call centers,
and e-commerce. It also offers an overview of cloud-based chatbots technology along with the program- ming of chatbots and
programming problems in the chatbot’s present and future periods.

Enormous amount of data are being processed and exchanged in our daily life, and database, which is used to organize data has been an active research topic for a long time. Database plays a major role in many computer systems and there is always a demand from technical and nontechnical people to ease the process of accessing data on database. Using Natural Language to directly interact with a database is a nice and user friendly solution. In order to achieve this type of communication between the computer (In particular, database) and human we have to make the computer understand what the human asks, and then, be able to respond with the right answer that was expected to be extracted from the database. In this paper we present an intelligent system for converting Natural Language queries into equivalent database Structured Query Language (SQL). Our system also allows processing complex Natural Language queries. We call this Intelligent Agent based Natural Language Interface to Database (INLIDB). The query results from the INLIDB is presented in an attractive succinctly viewable format. We have obtained encouraging results from INLIDB.

Enormous amount of data are being processed and exchanged in our daily life, and database, which is used to organize data has been an active research topic for a long time. Database plays a major role in many computer systems and there is always a demand from technical and nontechnical people to ease the process of accessing data on database. Using Natural Language to directly interact with a database is a nice and user friendly solution. In order to achieve this type of communication between the computer (In particular, database) and human we have to make the computer understand what the human asks, and then, be able to respond with the right answer that was expected to be extracted from the database. In this paper we present an intelligent system for converting Natural Language queries into equivalent database Structured Query Language (SQL). Our system also allows processing complex Natural Language queries. We call this Intelligent Agent based Natural Language Interface to Datab...

With One Voice: Disambiguating Sung and Spoken Voices Through a Composer’s Experience.

Mary Midgley has pointed out how our drive towards specialisation has led us to value the differences between things over their connections. The voice is largely dislodged from educational development and has been divided by isolationist practices in and within music and speech. Technology has allowed microscopic details of the voice to become public property whilst the athletic, full-voiced singer moves ever closer to Lucie Manén’s  “…one timbre, according to their own psychological make-up.” I will discuss my experience of these issues in my work with singers of all styles, actors and the synthetic voice. . In relation to the title of this paper, I propose the disambiguation of spoken and sung words requires re-synthesis.

As part of FNP Workshop Series, “Title Detection” is one of the two shared tasks proposed on Financial Document Structure Extraction. The objective of the task was to classify a given text block, that had been extracted from financial prospectuses in pdf format, as a title. Our DNN-based approach scored a weighted F1 of 97.16% on the test data.

NLTK (3.2.5), which incorporated features like Arabic stemmers, NIST evaluation, MOSES tokenizer, Stanford segmenter, treebank detokenizer, verbnet, and vader, etc. NLTK was created in 2001 as a part of Computational Linguistic Department at the University of Pennsylvania. Since then it has been tested and developed. The important packages of this system are 1) corpus builder, 2) tokenizer, 3) collocation, 4) tagging, 5) parsing, 6) metrics, and 7) probability distribution system. Toolbox NLTK was built to meet four primary requirements: 1) Simplicity: An substantive framework for building blocks; 2) Consistency: Consistent interface; 3) Extensibility: Which can be easily scaled; and 4) Modularity: All modules are independent of each other.