Abstract not found

We present three approaches for unsupervised grammar induction that are sensitive to data complexity and apply them to Klein and Manning’s Dependency Model with Valence. The first, Baby Steps, bootstraps itself via iterated learning of increasingly longer sentences and requires no initialization. This method substantially exceeds Klein and Manning’s published scores and achieves 39.4 % accuracy on Section 23 (all sentences) of the Wall Street Journal corpus. The second, Less is More, uses a low-complexity subset of the available data: sentences up to length 15. Focusing on fewer but simpler examples trades off quantity against ambiguity; it attains 44.1% accuracy, using the standard linguisticallyinformed prior and batch training, beating state-of-the-art. Leapfrog, our third heuristic, combines Less is More with Baby Steps by mixing their models of shorter sentences, then rapidly ramping up exposure to the full training set, driving up accuracy to 45.0%. These trends generalize to the Brown corpus; awareness of data complexity may improve other parsing models and unsupervised algorithms.

Abstract—In this paper, we use computational intelligence techniques to built quantitative models of player experience for a platform game. The models accurately predict certain key affective states of the player based on both gameplay metrics that relate to the actions performed by the player in the game, and on parameters of the level that was played. For the experiments presented here, a version of the classic Super Mario Bros game is enhanced with parameterizable level generation and gameplay metrics collection. Player pairwise preference data is collected using forced choice questionnaires, and the models are trained using this data and neuro-evolutionary preference learning of multi-layer perceptrons. The derived models will be used to optimize design parameters for particular types of player experience, allowing the designer to automatically generate unique levels that induce the desired experience for the player.

It has been widely observed that search queries are composed in a very different style from that of the body or the title of a document. Many techniques explicitly accounting for this language style discrepancy have shown promising results for information retrieval, yet a large scale analysis on the extent of the language differences has been lacking. In this paper, we present an extensive study on this issue by examining the language model properties of search queries and the three text streams associated with each web document: the body, the title, and the anchor text. Our information theoretical analysis shows that queries seem to be composed in a way most similar to how authors summarize documents in anchor texts or titles, offering a quantitative explanation to the observations in past work. We apply these web scale n-gram language models to three search query processing (SQP) tasks: query spelling correction, query bracketing and long query segmentation. By controlling the size and the order of different language models, we find that the perplexity metric to be a good accuracy indicator for these query processing tasks. We show that using smoothed language models yields significant accuracy gains for query bracketing for instance, compared to using web counts as in the literature. We also demonstrate that applying web-scale language models can have marked accuracy advantage over smaller ones.

We show how web mark-up can be used to improve unsupervised dependency parsing. Starting from raw bracketings of four common HTML tags (anchors, bold, italics and underlines), we refine approximate partial phrase boundaries to yield accurate parsing constraints. Conversion procedures fall out of our linguistic analysis of a newly available million-word hyper-text corpus. We demonstrate that derived constraints aid grammar induction by training Klein and Manning’s Dependency Model with Valence (DMV) on this data set: parsing accuracy on Section 23 (all sentences) of the Wall Street Journal corpus jumps to 50.4%, beating previous state-of-theart by more than 5%. Web-scale experiments show that the DMV, perhaps because it is unlexicalized, does not benefit from orders of magnitude more annotated but noisier data. Our model, trained on a single blog, generalizes to 53.3 % accuracy out-of-domain, against the Brown corpus — nearly 10 % higher than the previous published best. The fact that web mark-up strongly correlates with syntactic structure may have broad applicability in NLP. 1

We present an empirical study of two very simple approaches to unsupervised grammar induction. Both are based on Klein and Manning’s Dependency Model with Valence. The first, Baby Steps, requires no initialization and bootstraps itself via iterated learning of increasingly longer sentences. This method substantially exceeds Klein and Manning’s published numbers and achieves 39.4 % accuracy on Section 23 of the Wall Street Journal corpus — a result that is already competitive with the recent state-of-the-art. The second, Less is More, is based on the observation that there is sometimes a trade-off between the quantity and complexity of training data. Using the standard linguistically-informed prior but training at the “sweet spot ” — sentences up to length 15, it attains 44.1 % accuracy, beating state-of-the-art. Both results generalize to the Brown corpus and shed light on opportunities in the present state of unsupervised dependency parsing. 1

We present a resource for automatically associating strings of text with English Wikipedia concepts. Our machinery is bi-directional, in the sense that it uses the same fundamental probabilistic methods to map strings to empirical distributions over Wikipedia articles as it does to map article URLs to distributions over short, language-independent strings of natural language text. For maximal interoperability, we release our resource as a set of flat line-based text files, lexicographically sorted and encoded with UTF-8. These files capture joint probability distributions underlying concepts (we use the terms article, concept and Wikipedia URL interchangeably) and associated snippets of text, as well as other features that can come in handy when working with Wikipedia articles and related information. Keywords: cross-language information retrieval (CLIR), entity linking (EL), Wikipedia. 1.

The personal stories that people write in their Internet weblogs include a substantial amount of information about the causal relationships between everyday events. In this paper we describe our efforts to use millions of these stories for automated commonsense causal reasoning. Casting the commonsense causal reasoning problem as a Choice of Plausible Alternatives, we describe four experiments that compare various statistical and information retrieval approaches to exploit causal information in story corpora. The top performing system in these experiments uses a simple co-occurrence statistic between words in the causal antecedent and consequent, calculated as the Pointwise Mutual Information between words in a corpus of millions of personal stories.

Large Internet services companies like Google, Yahoo, and Facebook use the MapReduce programming model to process log data. MapReduce is designed to work on data stored in a distributed filesystem like Hadoop’s HDFS. As a result, a number of log collection systems have been built to copy data into HDFS. These systems often lack a unified approach to failure handling, with errors being handled separately by each piece of the collection, transport and processing pipeline. We argue for a unified approach, instead. We present a system, called Chukwa, that embodies this approach. Chukwa uses an end-to-end delivery model that can leverage local on-disk log files for reliability. This approach also eases integration with legacy systems. This architecture offers a choice of delivery models, making subsets of the collected data available promptly for clients that require it, while reliably storing a copy in HDFS. We demonstrate that our system works correctly on a 200-node testbed and can collect in excess of 200 MB/sec of log data. We supplement these measurements with a set of case studies describing real-world operational experience at several sites.

Abstract—There is an increasingly pressing need, by several applications in diverse domains, for developing techniques able to index and mine very large collections of time series. Examples of such applications come from astronomy, biology, the web, and other domains. It is not unusual for these applications to involve numbers of time series in the order of hundreds of millions to billions. However, all relevant techniques that have been proposed in the literature so far have not considered any data collections much larger than onemillion time series. In this paper, we describe iSAX 2.0, a data structure designed for indexing and mining truly massive collections of time series. We show that the main bottleneck in mining such massive datasets is the time taken to build the index, and we thus introduce a novel bulk loading mechanism, the first of this kind specifically tailored to a time series index. We show how our method allows mining on datasets that would otherwise be completely untenable, including the first published experiments to index one billion time series, and experiments in mining massive data from domains as diverse as entomology, DNA and web-scale image collections. Keywords-time series; data mining; representations; indexing I.