Adrian-Gabriel Chifu

Word sense discrimination in information retrieval: a spectral clustering-based approach

Word sense ambiguity has been identified as a cause of poor precision in information retrieval (IR) systems. Word sense disambiguation and discrimination methods have been defined to help systems choose which documents should be retrieved in relation to an ambiguous query. However, the only approaches that show a genuine benefit for word sense discrimination or disambiguation in IR are generally supervised ones. In this paper we propose a new unsupervised method that uses word sense discrimination in IR. The method we develop is based on spectral clustering and reorders an initially retrieved document list by boosting documents that are semantically similar to the target query. For several TREC ad hoc collections we show that our method is useful in the case of queries which contain ambiguous terms. We are interested in improving the level of precision after 5, 10 and 30 retrieved documents (P@5, P@10, P@30) respectively. We show that precision can be improved by 8% above current state-of-the-art baselines. We also focus on poor performing queries.

Word sense disambiguation to improve precision for ambiguous queries

Success in Information Retrieval (IR) depends on many variables. Several interdisciplinary approaches try to improve the quality of the results obtained by an IR system. In this paper we propose a new way of using word sense disambiguation (WSD) in IR. The method we develop is based on Naïve Bayes classification and can be used both as a filtering and as a re-ranking technique. We show on the TREC ad-hoc collection that WSD is useful in the case of queries which are difficult due to sense ambiguity. Our interest regards improving the precision after 5, 10 and 30 retrieved documents (P@5, P@10, P@30), respectively, for such lowest precision queries.

Human-Based Query Difficulty Prediction

The purpose of an automatic query difficulty predictor is to decide whether an information retrieval system is able to provide the most appropriate answer for a current query. Researchers have investigated many types of automatic query difficulty predictors. These are mostly related to how search engines process queries and documents: they are based on the inner workings of searching/ranking system functions, and therefore they do not provide any really insightful explanation as to the reasons for the difficulty, and they neglect user-oriented aspects. In this paper we study if humans can provide useful explanations, or reasons, of why they think a query will be easy or difficult for a search engine. We run two experiments with variations in the TREC reference collection, the amount of information available about the query, and the method of annotation generation. We examine the correlation between the human prediction, the reasons they provide, the automatic prediction, and the actual system effectiveness. The main findings of this study are twofold. First, we confirm the result of previous studies stating that human predictions correlate only weakly with system effectiveness. Second, and probably more important, after analyzing the reasons given by the annotators we find that: (i) overall, the reasons seem coherent, sensible, and informative; (ii) humans have an accurate picture of some query or term characteristics; and (iii) yet, they cannot reliably predict system/query difficulty.

SegChainW2V: Towards a Generic Automatic Video Segmentation Framework, Based on Lexical Chains of Audio Transcriptions and Word Embeddings

Abstract With the advances in multimedia broadcasting through a rich variety of channels and with the vulgarization of video production, it becomes essential to be able to provide reliable means of retrieving information within videos, not only the videos themselves. Research in this area has been widely focused on the context of TV news broadcasts, for which the structure itself provides clues for story segmentation. The systematic employment of these clues would lead to thematically driven systems that would not be easily adaptable in the case of videos of other types. The systems are therefore dependent on the type of videos for which they have been designed. In this paper we aim at introducing SegChainW2V, a generic unsupervised framework for story segmentation, based on lexical chains from transcriptions and their vectorization. SegChainW2V takes into account the topic changes by perceiving the fiuctuations of the most frequent terms throughout the video, as well as their semantics through the word embedding vectorization.

Query Performance Prediction Focused on Summarized Letor Features

Query performance prediction (QPP) aims at automatically estimating the information retrieval system effectiveness for any users query. Previous work has investigated several types of pre- and post-retrieval query performance predictors; the latter has been shown to be more effective. In this paper we investigate the use of features that were initially defined for learning to rank in the task of QPP. While these features have been shown to be useful for learning to rank documents, they have never been studied as query performance predictors. We developed more than 350 variants of them based on summary functions. Conducting experiments on four TREC standard collections, we found that Letor-based features appear to be better QPP than predictors from the literature. Moreover, we show that combining the best Letor features outperforms the state of the art query performance predictors. This is the first study that considers such an amount and variety of Letor features for QPP and that demonstrates they are appropriate for this task.

SegChain: Towards a generic automatic video segmentation framework, based on lexical chains of audio transcriptions

With the advances in multimedia broadcasting through a rich variety of channels and with the vulgarization of video production, it becomes essential to be able to provide reliable means of retrieving information within videos, not only the videos themselves. Research in this area has been widely focused on the context of TV news broadcasts, for which the structure itself provides clues for story segmentation. The systematic employment of these clues would lead to thematically driven systems that would not be easily adaptable in the case of videos of other types. The systems are therefore dependent on the type of videos for which they have been designed. In this paper we aim at introducing SegChain, a generic unsupervised framework for story segmentation, based on lexical chains from transcriptions. SegChain takes into account the topic changes by perceiving the fluctuations of the most frequent terms throughout the video.

DeShaTo: Describing the Shape of Cumulative Topic Distributions to Rank Retrieval Systems Without Relevance Judgments

This paper investigates an approach for estimating the effectiveness of any IR system. The approach is based on the idea that a set of documents retrieved for a specific query is highly relevant if there are only a small number of predominant topics in the retrieved documents. The proposed approach is to determine the topic probability distribution of each document offline, using Latent Dirichlet Allocation. Then, for a retrieved set of documents, a set of probability distribution shape descriptors, namely the skewness and the kurtosis, are used to compute a score based on the shape of the cumulative topic distribution of the respective set of documents. The proposed model is termed DeShaTo, which is short for Describing the Shape of cumulative Topic distributions. In this work, DeShaTo is used to rank retrieval systems without relevance judgments. In most cases, the empirical results are better than the state of the art approach. Compared to other approaches, DeShaTo works independently for each system. Therefore, it remains reliable even when there are less systems to be ranked by relevance.