Exploiting Deep Semantics and Compositionality of Natural Language for Human-Robot-Interaction

We develop a natural language interface for human robot interaction that implements reasoning about deep semantics in natural language. To realize the required deep analysis, we employ methods from cognitive linguistics, namely the modular and compositional framework of Embodied Construction Grammar (ECG) [Feldman, 2009]. Using ECG, robots are able to solve fine-grained reference resolution problems and other issues related to deep semantics and compositionality of natural language. This also includes verbal interaction with humans to clarify commands and queries that are too ambiguous to be executed safely. We implement our NLU framework as a ROS package and present proof-of-concept scenarios with different robots, as well as a survey on the state of the art

A Quasi-Fregean Solution to ‘The Concept Horse’ Paradox

In this paper I offer a conceptually tighter, quasi-Fregean solution to the concept horse paradox based on the idea that the unterfallen relation is asymmetrical. The solution is conceptually tighter in the sense that it retains the Fregean principle of separating sharply between concepts and objects, it retains Frege’s conclusion that the sentence ‘the concept horse is not a concept’ is true, but does not violate our intuitions on the matter. The solution is only ‘quasi’- Fregean in the sense that it rejects Frege’s claims about the ontological import of natural language and his analysis thereof

Optimal Activation of French for Specific Purposes for Human Development in Nigeria

It would not be far from the truth to say that communication through the use of the natural language plays a paramount role in the quest for development, be it human, social, political, technological and any other form of development. A paramount role because knowledge, which is the life wire of any development effort, is acquired through information. Information comes through communication powered by language. Looking at Nigeria as a country, English which is the official language and language of instruction in schools seems to have become inadequate for a sustainable human development which must take into account new trends in the globalized world. It is based on this background that this paper aims at exploring the concept of French for Specific Purposes (FSP), a paradigm of French studies, which has not been optimally activated in Nigeria as against what obtains in countries such as USA, Britain, Japan etc. The paper begins by defining the concept of French for Specific Purposes and goes further to examine the importance of French in Nigeria. The paper also makes a critical analysis of developmental benefits that are derivable from the optimal activation of this concept in Nigeria. To conclude, the paper recommends various practical and pragmatic approaches, which include the introduction of FSP certificate and diploma programmes in Nigerian universities, as steps towards the optimal activation of the concept in the countr

Language Without Words: A Pointillist Model for Natural Language Processing

This paper explores two separate questions: Can we perform natural language processing tasks without a lexicon?; and, Should we? Existing natural language processing techniques are either based on words as units or use units such as grams only for basic classification tasks. How close can a machine come to reasoning about the meanings of words and phrases in a corpus without using any lexicon, based only on grams? Our own motivation for posing this question is based on our efforts to find popular trends in words and phrases from online Chinese social media. This form of written Chinese uses so many neologisms, creative character placements, and combinations of writing systems that it has been dubbed the "Martian Language." Readers must often use visual queues, audible queues from reading out loud, and their knowledge and understanding of current events to understand a post. For analysis of popular trends, the specific problem is that it is difficult to build a lexicon when the invention of new ways to refer to a word or concept is easy and common. For natural language processing in general, we argue in this paper that new uses of language in social media will challenge machines' abilities to operate with words as the basic unit of understanding, not only in Chinese but potentially in other languages.Comment: 5 pages, 2 figure

25 years development of knowledge graph theory: the results and the challenge

The project on knowledge graph theory was begun in 1982. At the initial stage, the goal was to use graphs to represent knowledge in the form of an expert system. By the end of the 80's expert systems in medical and social science were developed successfully using knowledge graph theory. In the following stage, the goal of the project was broadened to represent natural language by knowledge graphs. Since then, this theory can be considered as one of the methods to deal with natural language processing. At the present time knowledge graph representation has been proven to be a method that is language independent. The theory can be applied to represent almost any characteristic feature in various languages.\ud The objective of the paper is to summarize the results of 25 years of development of knowledge graph theory and to point out some challenges to be dealt with in the next stage of the development of the theory. The paper will give some highlight on the difference between this theory and other theories like that of conceptual graphs which has been developed and presented by Sowa in 1984 and other theories like that of formal concept analysis by Wille or semantic networks