Communicating messages on social media usually conveys much implicit linguistic knowledge, which makes it difficult to process texts for further analysis. One of the major problems, the linguistic co-referencing task, has mostly been addressed for formal and full-sized text in which a relatively clear discourse structure can be discovered, using Natural-Language Processing techniques. However, texts in social media are short, informal and lack a lot of underlying linguistic information to make decisions so traditional methods can not be applied. Furthermore, this may significantly impact the performance of several tasks on social media applications such as opinion mining, network analysis, sentiment analysis, text categorization.In order to deal with these issues, this research address the task of linguistic co-referencing using an evolutionary computation approach. It combines discourse coreference analysis techniques, domain-based heuristics (i.e., syntactic, semantic and world knowledge), graph representation methods, and evolutionary computation algorithms to resolving implicit co-referencing within informal opinion texts.Experiments were conducted to assess the ability of the model to find implicit referents on informal messages, showing the promise of our approach when compared to related methods.

Geo-referencing is a key task for geographical information retrieval because it allows unstructured or textual documents i.e., Web pages to be associated with geographical locations, which are then used by geo-search engines to index documents and search information by spatial criteria. This work proposes a strategy to extract geo-references from textual documents that combine natural language-processing techniques and co-reference solving heuristics, which in turn can be used to expand a geographical gazetteer. Implicit geographical entities i.e., those entities referred to by pronouns are recognized and incorporated into the gazetteer that is updated and used for geo-referencing tasks. Experiments show the promise of the approach to geo-referencing Web pages when dealing with implicit and/or indirect geo-references.

This paper proposes a new approach for mining novel patterns from textual databases which considers both the mining process itself, the evaluation of this knowledge, and the human assessment. This is achieved by integrating Information Extraction technology and Genetic Algorithms to produce high-level explanatory novel hypotheses. Experimental results using the model are discussed and the assessment by human experts are highlighted.

This paper considers a nite-state parsing algorithm for implementing natural language interfaces. The algorithm has some practical improvements compared to other recognizing algorithms. Resolution of syntax ambiguity, programming facilities and post execution of semantic actions are discussed too. Our approach was implemented in order to design the GILENA software tool which automatically allows to generate natural language interfaces. In addition, our designed algorithm, its implementation and its results are discussed.

This work considers a parsing algorithm for implementing ATN-based natural language interfaces. That algorithm has many practical improvements compared to other parsing algorithms. Resolution of syntax ambiguity , programming facilities and post execution of semantic actions are some of the features included too. The algorithm was implemented in order to design a software tool to automatically allow medium size natural language interfaces. In addition, some designed algorithms , the system using these algorithms and their results are described.

This paper proposes a new approach for mining novel patterns from textual databases which considers both the mining process itself, the evaluation of this knowledge, and the human assessment. This is achieved by integrating Information Extraction technology and Genetic Algorithms to produce high-level explanatory novel hypotheses. Experimental results using the model are discussed and the assessment by human experts are highlighted.

An evolutionary approach that combines information extraction technology and genetic algorithms can produce a new, integrated model for text mining. Text mining discovers unseen patterns in textual databases. We've brought together the benefits of GAs for data mining and IE technology to propose a new approach for high-level knowledge discovery. Unlike previous KDT approaches, our model doesn't rely on external resources or conceptual descriptions. Instead, it performs the discovery using only information from the original corpus of text documents and from training data computed from them. The GA that produces the hypotheses is strongly guided by semantic constraints, which means that several specifically defined metrics evaluate the quality and plausibility.

We present a novel evolutionary model for knowledge discovery from texts (KDTs), which deals with issues concerning shallow text representation and processing for mining purposes in an integrated way. Its aims is to look for novel and interesting explanatory knowledge across text documents. The approach uses natural language technology and genetic algorithms to produce explanatory novel hypotheses. The proposed approach is interdisciplinary, involving concepts not only from evolutionary algorithms but also from many kinds of text mining methods. Accordingly, new kinds of genetic operations suitable for text mining are proposed. The principles behind the representation and a new proposal for using multiobjective evaluation at the semantic level are described. Some promising results and their assessment by human experts are also discussed which indicate the plausibility of the model for effective KDT.

A loss queueing system GI/G/m/0 is considered. Let a(x) be a p.d.f. of interarrival intervals. Assume that this function behaves like cλ α x α-1 for small x . Further let B(x) be a d.f. of service time; (1/μ) be the mean service time. Conditions are derived for the light-traffic insensitivity of the loss probability to the form of B(x) as (λ/ μ →) 0. In particular, the condition α = 1 is necessary. Estimates for the loss probability are obtained.