BlogForever D2.4: Weblog spider prototype and associated methodology

The purpose of this document is to present the evaluation of different solutions for capturing blogs, established methodology and to describe the developed blog spider prototype

Automating Content Extraction of HTML Documents

Web pages often contain clutter (such as unnecessary images and extraneous links) around the body of an article that distracts a user from actual content. Extraction of 'useful and relevant' content from web pages has many applications, including cell phone and PDA browsing, speech rendering for the visually impaired, and text summarization. Most approaches to making content more readable involve changing font size or removing HTML and data components such as images, which takes away from a webpage's inherent look and feel. Unlike 'Content Reformatting', which aims to reproduce the entire webpage in a more convenient form, our solution directly addresses 'Content Extraction'. We have developed a framework that employs an easily extensible set of techniques. It incorporates advantages of previous work on content extraction. Our key insight is to work with DOM trees, a W3C specified interface that allows programs to dynamically access document structure, rather than with raw HTML markup. We have implemented our approach in a publicly available Web proxy to extract content from HTML web pages. This proxy can be used both centrally, administered for groups of users, as well as by individuals for personal browsers. We have also, after receiving feedback from users about the proxy, created a revised version with improved performance and accessibility in mind

BlogForever D2.6: Data Extraction Methodology

This report outlines an inquiry into the area of web data extraction, conducted within the context of blog preservation. The report reviews theoretical advances and practical developments for implementing data extraction. The inquiry is extended through an experiment that demonstrates the effectiveness and feasibility of implementing some of the suggested approaches. More specifically, the report discusses an approach based on unsupervised machine learning that employs the RSS feeds and HTML representations of blogs. It outlines the possibilities of extracting semantics available in blogs and demonstrates the benefits of exploiting available standards such as microformats and microdata. The report proceeds to propose a methodology for extracting and processing blog data to further inform the design and development of the BlogForever platform

Expert and Corpus-Based Evaluation of a 3-Space Model of Conceptual Blending

This paper presents the 3-space model of conceptual blending that estimates the figurative similarity between Input spaces 1 and 2 using both their analogical similarity and the interconnecting Generic Space. We describe how our Dr Inventor model is being evaluated as a model of lexically based figurative similarity. We describe distinct but related evaluation tasks focused on 1) identifying novel and quality analogies between computer graphics publications 2) evaluation of machine generated translations of text documents 3) evaluation of documents in a plagiarism corpus. Our results show that Dr Inventor is capable of generating novel comparisons between publications but also appears to be a useful tool for evaluating machine translation systems and for detecting and assessing the level of plagiarism between documents. We also outline another more recent evaluation, using a corpus of patent applications

Design and implementation of a filter engine for semantic web documents

This report describes our project that addresses the challenge of changes in the semantic web. Some studies have already been done for the so-called adaptive semantic web, such as applying inferring rules. In this study, we apply the technology of Event Notification System (ENS). Treating changes as events, we developed a notification system for such events

What's Cookin'? Interpreting Cooking Videos using Text, Speech and Vision

We present a novel method for aligning a sequence of instructions to a video of someone carrying out a task. In particular, we focus on the cooking domain, where the instructions correspond to the recipe. Our technique relies on an HMM to align the recipe steps to the (automatically generated) speech transcript. We then refine this alignment using a state-of-the-art visual food detector, based on a deep convolutional neural network. We show that our technique outperforms simpler techniques based on keyword spotting. It also enables interesting applications, such as automatically illustrating recipes with keyframes, and searching within a video for events of interest.Comment: To appear in NAACL 201

Automatic inference of causal reasoning chains from student essays

While there has been an increasing focus on higher-level thinking skills arising from the Common Core Standards, many high-school and middle-school students struggle to combine and integrate information from multiple sources when writing essays. Writing is an important learning skill, and there is increasing evidence that writing about a topic develops a deeper understanding in the student. However, grading essays is time consuming for teachers, resulting in an increasing focus on shallower forms of assessment that are easier to automate, such as multiple-choice tests. Existing essay grading software has attempted to ease this burden but relies on shallow lexico-syntactic features and is unable to understand the structure or validity of a student’s arguments or explanations. Without the ability to understand a student’s reasoning processes, it is impossible to write automated formative assessment systems to assist students with improving their thinking skills through essay writing. In order to understand the arguments put forth in an explanatory essay in the science domain, we need a method of representing the causal structure of a piece of explanatory text. Psychologists use a representation called a causal model to represent a student\u27s understanding of an explanatory text. This consists of a number of core concepts, and a set of causal relations linking them into one or more causal chains, forming a causal model. In this thesis I present a novel system for automatically constructing causal models from student scientific essays using Natural Language Processing (NLP) techniques. The problem was decomposed into 4 sub-problems - assigning essay concepts to words, detecting causal-relations between these concepts, resolving coreferences within each essay, and using the structure of the whole essay to reconstruct a causal model. Solutions to each of these sub-problems build upon the predictions from the solutions to earlier problems, forming a sequential pipeline of models. Designing a system in this way allows later models to correct for false positive predictions from downstream models. However, this also has the disadvantage that errors made in earlier models can propagate through the system, negatively impacting the upstream models, and limiting their accuracy. Producing robust solutions for the initial 2 sub problems, detecting concepts, and parsing causal relations between them, was critical in building a robust system. A number of sequence labeling models were trained to classify the concepts associated with each word, with the most effective approach being a bidirectional recurrent neural network (RNN), a deep learning model commonly applied to word labeling problems. This is because the RNN used pre-trained word embeddings to better generalize to rarer words, and was able to use information from both ends of each sentence to infer a word\u27s concept. The concepts predicted by this model were then used to develop causal relation parsing models for detecting causal connections between these concepts. A shift-reduce dependency parsing model was trained using the SEARN algorithm and out-performed a number of other approaches by better utilizing the structure of the problem and directly optimizing the error metric used. Two pre-trained coreference resolution systems were used to resolve coreferences within the essays. However a word tagging model trained to predict anaphors combined with a heuristic for determining the antecedent out-performed these two systems. Finally, a model was developed for parsing a causal model from an entire essay, utilizing the solutions to the three previous problems. A beam search algorithm was used to produce multiple parses for each sentence, which in turn were combined to generate multiple candidate causal models for each student essay. A reranking algorithm was then used to select the optimal causal model from all of the generated candidates. An important contribution of this work is that it represents a system for parsing a complete causal model of a scientific essay from a student\u27s written answer. Existing systems have been developed to parse individual causal relations, but no existing system attempts to parse a sequence of linked causal relations forming a causal model from an explanatory scientific essay. It is hoped that this work can lead to the development of more robust essay grading software and formative assessment tools, and can be extended to build solutions for extracting causality from text in other domains. In addition, I also present 2 novel approaches for optimizing the micro-F1 score within the design of two of the algorithms studied: the dependency parser and the reranking algorithm. The dependency parser uses a custom cost function to estimate the impact of parsing mistakes on the overall micro-F1 score, while the reranking algorithm allows the micro-F1 score to be optimized by tuning the beam search parameter to balance recall and precision