Textual analytics based on representations of documents as bags of words have been reasonably successful. However, analysis that requires deeper insight into language, into author properties, or into the contexts in which documents were created requires a richer representation. Systemic nets are one such representation. They have not been extensively used because they required human effort to construct. We show that systemic nets can be algorithmically inferred from corpora, that the resulting nets are plausible, and that they can provide practical benefits for knowledge discovery problems. This opens up a new class of practical analysis techniques for textual analytics.
Kappagoda @cite_0 shows that word-function tags can be added to words using conditional random fields, in the same kind of general way that parsers add part-of-speech tags to words. These word-function tags provide hints of the systemic-functional role that words carry. This is limited because there is no hierarchy. Nevertheless, he is able to show that the process of labelling can be partially automated and that the resulting tags aid in understanding documents.
@cite_0
248887798
This paper describes the neural dependency parser submitted by Stanford to the CoNLL 2017 Shared Task on parsing Universal Dependencies. Our system uses relatively simple LSTM networks to produce part of speech tags and labeled dependency parses from segmented and tokenized sequences of words. In order to address the rare word problem that abounds in languages with complex morphology, we include a character-based word representation that uses an LSTM to produce embeddings from sequences of characters. Our system was ranked first according to all five relevant metrics for the system: UPOS tagging (93.09 ), XPOS tagging (82.27 ), unlabeled attachment score (81.30 ), labeled attachment score (76.30 ), and content word labeled attachment score (72.57 ). Application of natural language understanding to requirements gathering remains a field that has only limited explorations so far. This paper presents an approach to extract the object oriented elements of the required system. This approach starts with assigning the parts of speech tags to each word in the given input document. Further, to resolve the ambiguity posed by the pronouns, the pronoun resolutions are performed before normalizing the text. Finally the elements of the object-oriented system namely the classes, the attributes, methods and relationships between the classes, sequence of actions, the use-cases and actors are identified by mapping the 'parts of speech- tagged' words onto the Object Oriented Modeling Language elements using mapping rules which is the key to a successful implementation of user requirements. Most current dependency parsers presuppose that input words have been morphologically disambiguated using a part-of-speech tagger before parsing begins. We present a transition-based system for joint part-of-speech tagging and labeled dependency parsing with non-projective trees. Experimental evaluation on Chinese, Czech, English and German shows consistent improvements in both tagging and parsing accuracy when compared to a pipeline system, which lead to improved state-of-the-art results for all languages. In this paper, we will discuss a method for assigning part of speech tags to words in an unannotated text corpus whose structure is completely unknown, with a little bit of help from an informant. Starting from scratch, automated and semiautomated methods are employed to build a part of speech tagger for the text. There are three steps to building the tagger: uncovering a set of part of speech tags, building a lexicon which indicates for each word its most likely tag, and learning rules to both correct mistakes in the learned lexicon and discover where contextual information can repair tagging mistakes. The long term goal of this work is to create a system which would enable somebody to take a large text in a language he does not know, and with only a few hours of help from a speaker of the language, accurately annotate the text with part of speech information.