Similarity based smoothing in language modeling

In this paper, we improve our previously proposed Similarity Based Smoothing (SBS) algorithm. The idea of the SBS is to map words or part of sentences to an Euclidean space, and approximate the language model in that space. The bottleneck of the original algorithm was to train a regularized logistic regression model, which was incapable to deal with real world data. We replace the logistic regression by regularized maximum entropy estimation and a Gaussian mixture approach to model the language in the Euclidean space, showing other possibilities to use the main idea of SBS. We show that the regularized maximum entropy model is flexible enough to handle conditional probability density estimation, thus enable parallel computation tasks with significantly decreased iteration steps. The experimental results demonstrate the success of our method, we achieve 14% improvement on a reail world corpus

Memory-Based Learning: Using Similarity for Smoothing

This paper analyses the relation between the use of similarity in Memory-Based Learning and the notion of backed-off smoothing in statistical language modeling. We show that the two approaches are closely related, and we argue that feature weighting methods in the Memory-Based paradigm can offer the advantage of automatically specifying a suitable domain-specific hierarchy between most specific and most general conditioning information without the need for a large number of parameters. We report two applications of this approach: PP-attachment and POS-tagging. Our method achieves state-of-the-art performance in both domains, and allows the easy integration of diverse information sources, such as rich lexical representations.Comment: 8 pages, uses aclap.sty, To appear in Proc. ACL/EACL 9

Similarity-Based Models of Word Cooccurrence Probabilities

In many applications of natural language processing (NLP) it is necessary to determine the likelihood of a given word combination. For example, a speech recognizer may need to determine which of the two word combinations ``eat a peach'' and ``eat a beach'' is more likely. Statistical NLP methods determine the likelihood of a word combination from its frequency in a training corpus. However, the nature of language is such that many word combinations are infrequent and do not occur in any given corpus. In this work we propose a method for estimating the probability of such previously unseen word combinations using available information on ``most similar'' words. We describe probabilistic word association models based on distributional word similarity, and apply them to two tasks, language modeling and pseudo-word disambiguation. In the language modeling task, a similarity-based model is used to improve probability estimates for unseen bigrams in a back-off language model. The similarity-based method yields a 20% perplexity improvement in the prediction of unseen bigrams and statistically significant reductions in speech-recognition error. We also compare four similarity-based estimation methods against back-off and maximum-likelihood estimation methods on a pseudo-word sense disambiguation task in which we controlled for both unigram and bigram frequency to avoid giving too much weight to easy-to-disambiguate high-frequency configurations. The similarity-based methods perform up to 40% better on this particular task.Comment: 26 pages, 5 figure

Improving Term Frequency Normalization for Multi-topical Documents, and Application to Language Modeling Approaches

Term frequency normalization is a serious issue since lengths of documents are various. Generally, documents become long due to two different reasons - verbosity and multi-topicality. First, verbosity means that the same topic is repeatedly mentioned by terms related to the topic, so that term frequency is more increased than the well-summarized one. Second, multi-topicality indicates that a document has a broad discussion of multi-topics, rather than single topic. Although these document characteristics should be differently handled, all previous methods of term frequency normalization have ignored these differences and have used a simplified length-driven approach which decreases the term frequency by only the length of a document, causing an unreasonable penalization. To attack this problem, we propose a novel TF normalization method which is a type of partially-axiomatic approach. We first formulate two formal constraints that the retrieval model should satisfy for documents having verbose and multi-topicality characteristic, respectively. Then, we modify language modeling approaches to better satisfy these two constraints, and derive novel smoothing methods. Experimental results show that the proposed method increases significantly the precision for keyword queries, and substantially improves MAP (Mean Average Precision) for verbose queries.Comment: 8 pages, conference paper, published in ECIR '0

Finding similar research papers using language models

The task of assessing the similarity of research papers is of interest in a variety of application contexts. It is a challenging task, however, as the full text of the papers is often not available, and similarity needs to be determined based on the papers' abstract, and some additional features such as authors, keywords, and journal. Our work explores the possibility of adapting language modeling techniques to this end. The basic strategy we pursue is to augment the information contained in the abstract by interpolating the corresponding language model with language models for the authors, keywords and journal of the paper. This strategy is then extended by finding topics and additionally interpolating with the resulting topic models. These topics are found using an adaptation of Latent Dirichlet Allocation (LDA), in which the keywords that were provided by the authors are used to guide the process

Information Retrieval Models

Many applications that handle information on the internet would be completely\ud inadequate without the support of information retrieval technology. How would\ud we find information on the world wide web if there were no web search engines?\ud How would we manage our email without spam filtering? Much of the development\ud of information retrieval technology, such as web search engines and spam\ud filters, requires a combination of experimentation and theory. Experimentation\ud and rigorous empirical testing are needed to keep up with increasing volumes of\ud web pages and emails. Furthermore, experimentation and constant adaptation\ud of technology is needed in practice to counteract the effects of people that deliberately\ud try to manipulate the technology, such as email spammers. However,\ud if experimentation is not guided by theory, engineering becomes trial and error.\ud New problems and challenges for information retrieval come up constantly.\ud They cannot possibly be solved by trial and error alone. So, what is the theory\ud of information retrieval?\ud There is not one convincing answer to this question. There are many theories,\ud here called formal models, and each model is helpful for the development of\ud some information retrieval tools, but not so helpful for the development others.\ud In order to understand information retrieval, it is essential to learn about these\ud retrieval models. In this chapter, some of the most important retrieval models\ud are gathered and explained in a tutorial style