Web Query Reformulation via Joint Modeling of Latent Topic Dependency and Term Context

An important way to improve users’ satisfaction in Web search is to assist them by issuing more effective queries. One such approach is query reformulation, which generates new queries according to the current query issued by users. A common procedure for conducting reformulation is to generate some candidate queries first, then a scoring method is employed to assess these candidates. Currently, most of the existing methods are context based. They rely heavily on the context relation of terms in the history queries and cannot detect and maintain the semantic consistency of queries. In this article, we propose a graphical model to score queries. The proposed model exploits a latent topic space, which is automatically derived from the query log, to detect semantic dependency of terms in a query and dependency among topics. Meanwhile, the graphical model also captures the term context in the history query by skip-bigram and n-gram language models. In addition, our model can be easily extended to consider users’ history search interests when we conduct query reformulation for different users. In the task of candidate query generation, we investigate a social tagging data resource—Delicious bookmark—to generate addition and substitution patterns that are employed as supplements to the patterns generated from query log data

Statistical parsing of noun phrase structure

Noun phrases (NPs) are a crucial part of natural language, exhibiting in many cases an extremely complex structure. However, NP structure is largely ignored by the statistical parsing field, as the most widely-used corpus is not annotated with it. This lack of gold-standard data has restricted all previous efforts to parse NPs, making it impossible to perform the supervised experiments that have achieved high performance in so many Natural Language Processing (NLP) tasks. We comprehensively solve this problem by manually annotating NP structure for the entire Wall Street Journal section of the Penn Treebank. The inter-annotator agreement scores that we attain refute the belief that the task is too difficult, and demonstrate that consistent NP annotation is possible. Our gold-standard NP data is now available and will be useful for all parsers. We present three statistical methods for parsing NP structure. Firstly, we apply the Collins (2003) model, and find that its recovery of NP structure is significantly worse than its overall performance. Through much experimentation, we determine that this is not a result of the special base-NP model used by the parser, but primarily caused by a lack of lexical information. Secondly, we construct a wide-coverage, large-scale NP Bracketing system, applying a supervised model to achieve excellent results. Our Penn Treebank data set, which is orders of magnitude larger than those used previously, makes this possible for the first time. We then implement and experiment with a wide variety of features in order to determine an optimal model. Having achieved this, we use the NP Bracketing system to reanalyse NPs outputted by the Collins (2003) parser. Our post-processor outperforms this state-of-the-art parser. For our third model, we convert the NP data to CCGbank (Hockenmaier and Steedman, 2007), a corpus that uses the Combinatory Categorial Grammar (CCG) formalism. We experiment with a CCG parser and again, implement features that improve performance. We also evaluate the CCG parser against the Briscoe and Carroll (2006) reannotation of DepBank (King et al., 2003), another corpus that annotates NP structure. This supplies further evidence that parser performance is increased by improving the representation of NP structure. Finally, the error analysis we carry out on the CCG data shows that again, a lack of lexicalisation causes difficulties for the parser. We find that NPs are particularly reliant on this lexical information, due to their exceptional productivity and the reduced explicitness present in modifier sequences. Our results show that NP parsing is a significantly harder task than parsing in general. This thesis comprehensively analyses the NP parsing task. Our contributions allow wide-coverage, large-scale NP parsers to be constructed for the first time, and motivate further NP parsing research for the future. The results of our work can provide significant benefits for many NLP tasks, as the crucial information contained in NP structure is now available for all downstream systems

Sentiment Analysis Using Machine Learning Techniques

Before buying a product, people usually go to various shops in the market, query about the product, cost, and warranty, and then finally buy the product based on the opinions they received on cost and quality of service. This process is time consuming and the chances of being cheated by the seller are more as there is nobody to guide as to where the buyer can get authentic product and with proper cost. But now-a-days a good number of persons depend upon the on-line market for buying their required products. This is because the information about the products is available from multiple sources; thus it is comparatively cheap and also has the facility of home delivery. Again, before going through the process of placing order for any product, customers very often refer to the comments or reviews of the present users of the product, which help them take decision about the quality of the product as well as the service provided by the seller. Similar to placing order for products, it is observed that there are quite a few specialists in the field of movies, who go though the movie and then finally give a comment about the quality of the movie, i.e., to watch the movie or not or in five-star rating. These reviews are mainly in the text format and sometimes tough to understand. Thus, these reports need to be processed appropriately to obtain some meaningful information. Classification of these reviews is one of the approaches to extract knowledge about the reviews. In this thesis, different machine learning techniques are used to classify the reviews. Simulation and experiments are carried out to evaluate the performance of the proposed classification methods. It is observed that a good number of researchers have often considered two different review datasets for sentiment classification namely aclIMDb and Polarity dataset. The IMDb dataset is divided into training and testing data. Thus, training data are used for training the machine learning algorithms and testing data are used to test the data based on the training information. On the other hand, polarity dataset does not have separate data for training and testing. Thus, k-fold cross validation technique is used to classify the reviews. Four different machine learning techniques (MLTs) viz., Naive Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), and Linear Discriminant Analysis (LDA) are used for the classification of these movie reviews. Different performance evaluation parameters are used to evaluate the performance of the machine learning techniques. It is observed that among the above four machine learning algorithms, RF technique yields the classification result, with more accuracy. Secondly, n-gram based classification of reviews are carried out on the aclIMDb dataset..

A Hybrid Environment for Syntax-Semantic Tagging

The thesis describes the application of the relaxation labelling algorithm to NLP disambiguation. Language is modelled through context constraint inspired on Constraint Grammars. The constraints enable the use of a real value statind "compatibility". The technique is applied to POS tagging, Shallow Parsing and Word Sense Disambigation. Experiments and results are reported. The proposed approach enables the use of multi-feature constraint models, the simultaneous resolution of several NL disambiguation tasks, and the collaboration of linguistic and statistical models.Comment: PhD Thesis. 120 page