We present a hybrid text mining method for finding abbreviations and their definitions in free format texts. To deal with the problem, this method employs pattern-based abbreviation rules in addition to text markers and cue words. The pattern-based rules describe how abbreviations are formed from definitions. Rules can be generated automatically and/or manually and can be augmented when the sys...

We study a new content-based method for the evaluation of text summarization systems without human models which is used to produce system rankings. The research is carried out using a new content-based evaluation framework called FRESA to compute a variety of divergences among probability distributions. We apply our comparison framework to various well-established content-based evaluation measu...

Previous work has shown that grammars and similar structure can be induced from unlabeled text (both monolingually and bilingually), and that the performance of an example-based machine translation (EBMT) system can be substantially enhanced by using clustering techniques to determine equivalence classes of individual words which can be used interchangeably, thus converting translation examples...

This paper presents our work on textual inference and situates it within the context of the larger goals of machine reading. The textual inference task is to determine if the meaning of one text can be inferred from the meaning of another and from background knowledge. Our system generates semantic graphs as a representation of the meaning of a text. This paper presents new results for aligning...

We study correlation of rankings of text summarization systems using evaluation methods with and without human models. We apply our comparison framework to various well-established contentbased evaluation measures in text summarization such as coverage, Responsiveness, Pyramids and ROUGE studying their associations in various text summarization tasks including generic and focus-based multi-docu...

Text analysis involves the deconstruction of information within a text. This includes text structure, text pattern, linguistic features, lexical analysis, and syntactic analysis. This research took as its starting point the bottom-up approach of analysing the lexical features, syntactic features, and textual features of patent abstracts for comprehensive coverage of text analysis. Several tools...

Inferring semantic relevance among entities (e.g., entries of Wikipedia) is important and challenging. According to the information resources, the inference can be categorized into learning with either raw text data, or labeled text data (e.g., wiki page), or graph knowledge (e.g, WordNet). Although graph knowledge tends to be more reliable, text data is much less costly and offers a better cov...