Lexical Query Modeling in Session Search

Lexical query modeling has been the leading paradigm for session search. In this paper, we analyze TREC session query logs and compare the performance of different lexical matching approaches for session search. Naive methods based on term frequency weighing perform on par with specialized session models. In addition, we investigate the viability of lexical query models in the setting of session search. We give important insights into the potential and limitations of lexical query modeling for session search and propose future directions for the field of session search.Comment: ICTIR2016, Proceedings of the 2nd ACM International Conference on the Theory of Information Retrieval. 201

Overview of the personalized and collaborative information retrieval (PIR) track at FIRE-2011

The Personalized and collaborative Information Retrieval (PIR) track at FIRE 2011 was organized with an aim to extend standard information retrieval (IR) ad-hoc test collection design to facilitate research on personalized and collaborative IR by collecting additional meta-information during the topic (query) development process. A controlled query generation process through task-based activities with activity logging was used for each topic developer to construct the final list of topics. The standard ad-hoc collection is thus accompanied by a new set of thematically related topics and the associated log information. We believe this can better simulate a real-world search scenario and encourage mining user information from the logs to improve IR effectiveness. A set of 25 TREC formatted topics and the associated metadata of activity logs were released for the participants to use. In this paper we illustrate the data construction phase in detail and also outline two simple ways of using the additional information from the logs to improve retrieval effectiveness

DCU@TRECMed 2012: Using ad-hoc baselines for domain-specific retrieval

This paper describes the first participation of DCU in the TREC Medical Records Track (TRECMed). We performed some initial experiments on the 2011 TRECMed data based on the BM25 retrieval model. Surprisingly, we found that the standard BM25 model with default parameters, performs comparable to the best automatic runs submitted to TRECMed 2011 and would have resulted in rank four out of 29 participating groups. We expected that some form of domain adaptation would increase performance. However, results on the 2011 data proved otherwise: concept-based query expansion decreased performance, and filtering and reranking by term proximity also decreased performance slightly. We submitted four runs based on the BM25 retrieval model to TRECMed 2012 using standard BM25, standard query expansion, result filtering, and concept-based query expansion. Official results for 2012 confirm that domain-specific knowledge does not increase performance compared to the BM25 baseline as applied by us

Contextualised Browsing in a Digital Library's Living Lab

Contextualisation has proven to be effective in tailoring \linebreak search results towards the users' information need. While this is true for a basic query search, the usage of contextual session information during exploratory search especially on the level of browsing has so far been underexposed in research. In this paper, we present two approaches that contextualise browsing on the level of structured metadata in a Digital Library (DL), (1) one variant bases on document similarity and (2) one variant utilises implicit session information, such as queries and different document metadata encountered during the session of a users. We evaluate our approaches in a living lab environment using a DL in the social sciences and compare our contextualisation approaches against a non-contextualised approach. For a period of more than three months we analysed 47,444 unique retrieval sessions that contain search activities on the level of browsing. Our results show that a contextualisation of browsing significantly outperforms our baseline in terms of the position of the first clicked item in the result set. The mean rank of the first clicked document (measured as mean first relevant - MFR) was 4.52 using a non-contextualised ranking compared to 3.04 when re-ranking the result lists based on similarity to the previously viewed document. Furthermore, we observed that both contextual approaches show a noticeably higher click-through rate. A contextualisation based on document similarity leads to almost twice as many document views compared to the non-contextualised ranking.Comment: 10 pages, 2 figures, paper accepted at JCDL 201

Utilizing sub-topical structure of documents for information retrieval.

Text segmentation in natural language processing typically refers to the process of decomposing a document into constituent subtopics. Our work centers on the application of text segmentation techniques within information retrieval (IR) tasks. For example, for scoring a document by combining the retrieval scores of its constituent segments, exploiting the proximity of query terms in documents for ad-hoc search, and for question answering (QA), where retrieved passages from multiple documents are aggregated and presented as a single document to a searcher. Feedback in ad hoc IR task is shown to benefit from the use of extracted sentences instead of terms from the pseudo relevant documents for query expansion. Retrieval effectiveness for patent prior art search task is enhanced by applying text segmentation to the patent queries. Another aspect of our work involves augmenting text segmentation techniques to produce segments which are more readable with less unresolved anaphora. This is particularly useful for QA and snippet generation tasks where the objective is to aggregate relevant and novel information from multiple documents satisfying user information need on one hand, and ensuring that the automatically generated content presented to the user is easily readable without reference to the original source document

Towards evaluation of personalized and collaborative information retrieval

We propose to extend standard information retrieval (IR) ad-hoc test collection design to facilitate research on personalized and collaborative IR by gathering additional meta-information during the topic (query) development process. We propose a controlled query generation process with activity logging for each topic developer. The standard ad-hoc collection will thus be accompanied by a new set of thematically related topics and the associated log information, and has the potential to simulate a real-world search scenario to encourage retrieval systems to mine user information from the logs to improve IR effectiveness. The proposed methodology described in this paper will be applied in a pilot task which is scheduled to run in the FIRE 2011 evaluation campaign. The task aims at investigating the research question of whether personalized and collaborative IR retrieval experiments and evaluation can be pursued by enriching a standard ad-hoc collection with such meta-information

Query Resolution for Conversational Search with Limited Supervision

In this work we focus on multi-turn passage retrieval as a crucial component of conversational search. One of the key challenges in multi-turn passage retrieval comes from the fact that the current turn query is often underspecified due to zero anaphora, topic change, or topic return. Context from the conversational history can be used to arrive at a better expression of the current turn query, defined as the task of query resolution. In this paper, we model the query resolution task as a binary term classification problem: for each term appearing in the previous turns of the conversation decide whether to add it to the current turn query or not. We propose QuReTeC (Query Resolution by Term Classification), a neural query resolution model based on bidirectional transformers. We propose a distant supervision method to automatically generate training data by using query-passage relevance labels. Such labels are often readily available in a collection either as human annotations or inferred from user interactions. We show that QuReTeC outperforms state-of-the-art models, and furthermore, that our distant supervision method can be used to substantially reduce the amount of human-curated data required to train QuReTeC. We incorporate QuReTeC in a multi-turn, multi-stage passage retrieval architecture and demonstrate its effectiveness on the TREC CAsT dataset.Comment: SIGIR 2020 full conference pape