<p>On the Internet an enormous amount of information is available that is represented in an unstructured form. The purpose with a text mining tool is to collect this information and present it in a more structured form. In this report text mining is used to create an algorithm that searches abstracts available from PubMed and finds specific relationships between genes that can be used to create a network. The algorithm can also be used to find information about a specific gene. The network created by Mendoza et al. (1999) was verified in all the connections but one using the algorithm. This connection contained implicit information. The results suggest that the algorithm is better at extracting information about specific genes than finding connections between genes. One advantage with the algorithm is that it can also find connections between genes and proteins and genes and other chemical substances.</p>

Thesis (M.S. in Software Engineering)--Naval Postgraduate School, June 2004. / Thesis advisor(s): Valdis Berzins. Includes bibliographical references (p. 179). Also available online.

Thesis (Ph. D.)--University of Washington, 2002. / Vita. Includes bibliographical references (p. 101-108).

Thesis (Ph. D.)--University of Washington, 2002. / Vita. Includes bibliographical references (leaves 181-193).

Det här arbetet redogör för hur jag går tillväga för att skapa ett koncept med informationstexter om styrketräning. Texterna informerar om hur vanliga friviktsövningar ska utföras på ett korrekt sätt och placeras direkt i gymlokalen. Som fall används gymmet Parsa Power i Uppsala. Uppdragsgivarna där är Omid Parsa, grundare, och Ulrica Lindkvist, delägare. För att ta fram konceptet undersöktes bland annat vad som fångar målgruppens uppmärksamhet i en miljö med många distraktioner och vilket tilltal som bör användas. Arbetet baseras på vad uppdragsgivarna efterfrågar, målgruppens behov och olika forskningsresultat och teorier kring uppmärksamhet, retorik och layoutprinciper. Använda metoder är bland annat en fokusgrupp och en enkätundersökning. / This article give an account for my process when creating a concept of information texts about weight training. The information describes how common exercises should be performed properly and are placed directly in the gym. As a case I use the gym Parsa Power in Uppsala. The clients there are Omid Parsa, founder, and Ulrica Lindkvist, co-owner. To create the concept this study examines what catches the attention of the target group in the particular environment with many distractors and how to appeal the target group, with words. The work is based on what the clients want, what the target group needs and various research findings and theories of attention, rhetoric and layout principles. Used methods include a focus group and a survey.

This report explores various aspects of sentiment mining. The two research goals for the report were: (1) to determine useful methods in increasing recall of negative sentences and (2) to determine the best method for applying sentence level classification to the document level. The methods in this report were applied to the Movie Reviews corpus at both the document and sentence level. The basic approach was to first identify polar and neutral sentences within the text and then classify the polar sentences as either positive or negative. The Maximum Entropy classifier was used as the baseline system in which the application of further methods was explored. Part-of-speech tagging was used for its effectiveness to determine if its inclusion increased recall of negative sentences. It was also used to aid in the handling of negations within sentences at the sentence level. Smoothing was investigated and various metrics to describe the sentiment composition were explored to address goal (2). Negative recall was shown to increase with the adjustment of the classification threshold and was also seen to increase through the methods used to address goal (2). Overall, classifying at the sentence level using bigrams and a cutoff value of one was observed to result in the highest evaluation scores. / text

The purpose of this qualitative research was to better understand the multilingual and multimodal composition resources appropriated by students during a study of Latino children’s picturebooks within a predominantly Latino, third grade classroom. A conceptual framework guided by socio-cultural perspectives, a social semiotic theory of communication, and Composition 2.0 studies was employed to investigate the ways in which students remixed multilingual and multimodal composition resources and manifested identities in texts. This research was guided by both design-based and case study methods and drew upon constant-comparative, discourse, and visual discourse analytic methods to examine the data. Analysis was also located in the literature on identity and texts so as to better understand the socio-cultural histories and identities attached to the children's picturebooks. Data collection was focused on both the multilingual and multimodal resources students appropriated to compose and the ways students orchestrated those resources during the classroom picturebook study. Analysis was structured by two interrelated strands. The first strand explores more broadly the composition resources in use during the classroom picturebook study, and the second analyzes explicitly the ways two focal students remixed composition resources within their picturebook productions and sedimented identities in texts. Three findings generated from the two related strands of analysis provided insights into the potential of a picturebook study as a viable multilingual and multimodal composition curriculum. First, in the context of the teacher and researcher co-designed curriculum and instruction, students appropriated literary, illustrated, material, and picturebook form resources from Latino children’s picturebooks in diverse ways. Second, in the act of picturebook making, students invoked other socio-cultural texts as mentors and remixed composition resources from diverse sources to craft their own picturebooks. Finally, students manifested aspects of their identities within the material worlds and languages reflected within their picturebooks. Together, these findings situate picturebook study and picturebook making as creative and intellectual acts for students. Moreover, this study features Latino children’s picturebooks as culturally responsive mentor texts. Several pedagogical implications related to composition instruction for young writers and diverse population are also discussed. / text

The subject matter of this thesis is the problem of learning to discover grammatical structure from raw text alone, without access to explicit instruction or annotation -- in particular, by a computer or computational process -- in other words, unsupervised parser induction, or simply, unsupervised parsing. This work presents a method for raw text unsupervised parsing that is simple, but nevertheless achieves state-of-the-art results on treebank-based direct evaluation. The approach to unsupervised parsing presented in this dissertation adopts a different way to constrain learned models than has been deployed in previous work. Specifically, I focus on a sub-task of full unsupervised partial parsing called unsupervised partial parsing. In essence, the strategy is to learn to segment a string of tokens into a set of non-overlapping constituents or chunks which may be one or more tokens in length. This strategy has a number of advantages: it is fast and scalable, based on well-understood and extensible natural language processing techniques, and it produces predictions about human language structure which are useful for human language technologies. The models developed for unsupervised partial parsing recover base noun phrases and local constituent structure with high accuracy compared to strong baselines. Finally, these models may be applied in a cascaded fashion for the prediction of full constituent trees: first segmenting a string of tokens into local phrases, then re-segmenting to predict higher-level constituent structure. This simple strategy leads to an unsupervised parsing model which produces state-of-the-art results for constituent parsing of English, German and Chinese. This thesis presents, evaluates and explores these models and strategies. / text

Machine reading is the artificial-intelligence task of automatically reading a corpus of texts and, from the contents, building a knowledge base that supports automated reasoning and question answering. Success at this task could fundamentally solve the knowledge acquisition bottleneck – the widely recognized problem that knowledge-based AI systems are difficult and expensive to build because of the difficulty of acquiring knowledge from authoritative sources and building useful knowledge bases. One challenge inherent in machine reading is knowledge integration – the task of correctly and coherently combining knowledge snippets extracted from texts. This dissertation shows that knowledge integration can be automated and that it can significantly improve the performance of machine reading. We specifically focus on two contributions of knowledge integration. The first contribution is for improving the coherence of learned knowledge bases to better support automated reasoning and question answering. Knowledge integration achieves this benefit by aligning knowledge snippets that contain overlapping content. The alignment is difficult because the snippets can use significantly different surface forms. In one common type of variation, two snippets might contain overlapping content that is expressed at different levels of granularity or detail. Our matcher can “see past” this difference to align knowledge snippets drawn from a single document, from multiple documents, or from a document and a background knowledge base. The second contribution is for improving text interpretation. Our approach is to delay ambiguity resolution to enable a machine-reading system to maintain multiple candidate interpretations. This is useful because typically, as the system reads through texts, evidence accumulates to help the knowledge integration system resolve ambiguities correctly. To avoid a combinatorial explosion in the number of candidate interpretations, we propose the packed representation to compactly encode all the candidates. Also, we present an algorithm that prunes interpretations from the packed representation as evidence accumulates. We evaluate our work by building and testing two prototype machine reading systems and measuring the quality of the knowledge bases they construct. The evaluation shows that our knowledge integration algorithms improve the cohesiveness of the knowledge bases, indicating their improved ability to support automated reasoning and question answering. The evaluation also shows that our approach to postponing ambiguity resolution improves the system’s accuracy at text interpretation. / text

Στόχος της παρούσας πτυχιακής εργασίας είναι να εξηγήσει το ρόλο που έχει σε ένα γραπτό κείμενο και στην κατανόησή του ο συγγραφέας, υπό ποιους όρους το κατανοεί ο αναγνώστης και κυρίως τη λειτουργία του ίδιου του γραπτού κειμένου στη διπλή σχέση του τόσο με τον συγγραφέα όσο και με τον αναγνώστη. Αφετηρία για τη μελέτη αυτή αποτελεί η σύγχρονη συζήτηση στη φιλολογία και τη θεωρία της λογοτεχνίας, σχετικά με την πρόθεση του συγγραφέα. Δύο κείμενα του Πλάτωνα, ο «Φαίδρος» και η «7η Επιστολή», θα βοηθήσουν να παρουσιαστεί πιο αναλυτικά το ζήτημα της κατανόησης. / The aim of this dissertation is to explain: a) the role of the author in a written text and in its understanding, b) under which circumstances the reader understand the written text and c) the function of the written text. Main point of this study is the contemporary discussion between philology and theory of literature, as far as the intention of the author is concerned. The analysis of two books of Plato, “Phaedrus” and “Seventh Letter”, will contribute to the presentation of the matter of understanding more extensively.