Randomized Maximum Entropy Language Models

Abstract—We address the memory problem of maximum entropy language models(MELM) with very large feature sets. Randomized techniques are employed to remove all large, exact data structures in MELM implementations. To avoid the dictionary structure that maps each feature to its corresponding weight, the feature hashing trick [1] [2] can be used. We also replace the explicit storage of features with a Bloom filter. We show with extensive experiments that false positive errors of Bloom filters and random hash collisions do not degrade model performance. Both perplexity and WER improvements are demonstrated by building MELM that would otherwise be prohibitively large to estimate or store. I

Fast, Small and Exact: Infinite-order Language Modelling with Compressed Suffix Trees

Efficient methods for storing and querying are critical for scaling high-order n-gram language models to large corpora. We propose a language model based on compressed suffix trees, a representation that is highly compact and can be easily held in memory, while supporting queries needed in computing language model probabilities on-the-fly. We present several optimisations which improve query runtimes up to 2500x, despite only incurring a modest increase in construction time and memory usage. For large corpora and high Markov orders, our method is highly competitive with the state-of-the-art KenLM package. It imposes much lower memory requirements, often by orders of magnitude, and has runtimes that are either similar (for training) or comparable (for querying).Comment: 14 pages in Transactions of the Association for Computational Linguistics (TACL) 201

Language Modeling for limited-data domains

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 99-109).With the increasing focus of speech recognition and natural language processing applications on domains with limited amount of in-domain training data, enhanced system performance often relies on approaches involving model adaptation and combination. In such domains, language models are often constructed by interpolating component models trained from partially matched corpora. Instead of simple linear interpolation, we introduce a generalized linear interpolation technique that computes context-dependent mixture weights from features that correlate with the component confidence and relevance for each n-gram context. Since the n-grams from partially matched corpora may not be of equal relevance to the target domain, we propose an n-gram weighting scheme to adjust the component n-gram probabilities based on features derived from readily available corpus segmentation and metadata to de-emphasize out-of-domain n-grams. In scenarios without any matched data for a development set, we examine unsupervised and active learning techniques for tuning the interpolation and weighting parameters. Results on a lecture transcription task using the proposed generalized linear interpolation and n-gram weighting techniques yield up to a 1.4% absolute word error rate reduction over a linearly interpolated baseline language model. As more sophisticated models are only as useful as they are practical, we developed the MIT Language Modeling (MITLM) toolkit, designed for efficient iterative parameter optimization, and released it to the research community.(cont.) With a compact vector-based n-gram data structure and optimized algorithm implementations, the toolkit not only improves the running time of common tasks by up to 40x, but also enables the efficient parameter tuning for language modeling techniques that were previously deemed impractical.by Bo-June (Paul) Hsu.Ph.D

Indexing Proximity-based Dependencies for Information Retrieval

Research into term dependencies for information retrieval has demonstrated that dependency retrieval models are able to consistently improve retrieval effectiveness over bag-of-words models. However, the computation of term dependency statistics is a major efficiency bottleneck in the execution of these retrieval models. This thesis investigates the problem of improving the efficiency of dependency retrieval models without compromising the effectiveness benefits of the term dependency features. Despite the large number of published comparisons between dependency models and bag-of-words approaches, there has been a lack of direct comparisons between alternate dependency models. We provide this comparison and investigate different types of proximity features. Several bi-term and many-term dependency models over a range of TREC collections, for both short (title) and long (description) queries, are compared to determine the strongest benchmark models. We observe that the weighted sequential dependence model is the most effective model studied. Additionally, we observe that there is some potential in many-term dependencies, but more selective methods are required to exploit these features. We then investigate two novel index structures to directly index the proximitybased dependencies used in the sequential dependence model and weighted sequential dependence model. The frequent index and the sketch index data structures can both provide efficient access to collection and document level statistics for all indexed term dependencies, while minimizing space costs, relative to a full inverted index of term dependencies. We test whether these structures can improve retrieval efficiency without incurring large space requirements, or degrading retrieval effectiveness significantly. A secondary requirement is that each data structure must be able to be constructed for an input text collection in a scalable and distributed manner. Based on the observation that the vast majority of term dependencies extracted from queries are relatively frequent in the collection, the “frequent” index of term dependencies omits data for infrequent term dependencies. The sketch index of term dependencies uses techniques from sketch data structures to store probabilisticallybounded estimates of the required statistics. We present analyses of these data structures that include construction and space costs, retrieval efficiency and investigation of any degradation of retrieval effectiveness. Finally, we investigate the application of these data structures to the execution of the strongest performing dependency models identified. We compare the retrieval efficiency of each of these structures across two query processing algorithms, and across both short and long queries, using two large web collections. We observe that these newly proposed data structures allow the execution of queries considerably faster than when using positional indexes, and as fast as a full index of term dependencies, but with lowered storage overhead

Dealing with spelling variation in Early Modern English texts

Early English Books Online contains facsimiles of virtually every English work printed between 1473 and 1700; some 125,000 publications. In September 2009, the Text Creation Partnership released the second instalment of transcriptions of the EEBO collection, bringing the total number of transcribed works to 25,000. It has been estimated that this transcribed portion contains 1 billion words of running text. With such large datasets and the increasing variety of historical corpora available from the Early Modern English period, the opportunities for historial corpus linguistic research have never been greater. However, it has been observed in prior research, and quantified on a large-scale for the first time in this thesis, that texts from this period contain significant amounts of spelling variation until the eventual standardisation of orthography in the 18th century. The problems caused by this historical spelling variation are the focus of this thesis. It will be shown that the high levels of spelling variation found have a significant impact on the accuracy of two widely used automatic corpus linguistic methods - Part-of-Speech annotation and key word analysis. The development of historical spelling normalisation methods which can alleviate these issues will then be presented. Methods will be based on techniques used in modern spellchecking, with various analyses of Early Modern English spelling variation dictating how the techniques are applied. With the methods combined into a single procedure, automatic normalisation can be performed on an entire corpus of any size. Evaluation of the normalisation performance shows that after training, 62% of required normalisations are made, with a precision rate of 95%

Robust Text Correction for Grammar and Fluency

Grammar is one of the most important properties of natural language. It is a set of structural (i.e., syntactic and morphological) rules that are shared among native speakers in order to engage smooth communication. Automated grammatical error correction (GEC) is a natural language processing (NLP) application, which aims to correct grammatical errors in a given source sentence by computational models. Since the data-driven statistical methods began in 1990s and early 2000s, the GEC com- munity has worked on establishing a common framework for its evaluation (i.e., dataset and metric for benchmarking) in order to compare GEC models’ performance quantitatively. A series of shared tasks since early 2010s is a good example of this. In the first half of this thesis, I propose character-level and token-level error correction algorithms. For the character-level error correction, I introduce a semi-character recurrent neural network, which is motivated by a finding in psycholinguistics, called the Cmabrigde Uinervtisy (Cambridge University) effect or typoglycemia. For word-level error correc- tion, I propose an error-repair dependency parsing algorithm for ungrammatical texts. The algorithm can parse sentences and correct grammatical errors simultaneously. However, it is important to note that grammatical errors are not usually limited to mor- phological or syntactic errors. For example, collocational errors such as *quick/fast food and *fast/quick meal are not fully explained by only syntactic rules. This is another im- portant property of natural language, called fluency (or acceptability). Fluency is a level of mastery that goes beyond knowledge of how to follow the rules, and includes know- ing when they can be broken or flouted. In fact, the GEC community has also extended the scope of error types from closed class errors (e.g., noun numbers, verb forms) to the fluency-oriented errors. The second half of this thesis investigates GEC while considering fluency as well as grammaticality. When it comes to “whole-sentence” correction, by extending the scope of errors considering fluency as well as grammaticality, the GEC community has overlooked the reliability and validity of the task scheme (i.e., evaluation metric and dataset for bench- marking). Thus, I reassess the goals of GEC as a “whole-sentence” rewriting task while considering fluency. Following the fluency-oriented GEC framework, I introduce a new benchmark corpus that is more diverse in various aspects such as proficiency, topics, and learners’ native languages. Based on the fluency-oriented metric and dataset, I propose a new “whole-sentence” error correction model with neural reinforcement learning. Unlike conventional maximum likelihood estimation (MLE), the model directly optimizes toward an objective that consid- ers a sentence-level, task-specific evaluation metric. I demonstrate that the proposed model outperforms MLE in human and automated evaluation metrics. Finally, I conclude the thesis and outline ideas and suggestions for future GEC research