Food Sharing Initiatives (FSIs) are a vital but often hidden part of urban life. The EU CULTIVATE project has developed the SHARECITY 200 database and an interactive graphical exploration application to make these practices visible across 200 cities. In this demonstration, we present the CULTIVATE system, which automates the discovery, classification, and updating of FSIs from online sources, but our interactive tool enables audiences to engage directly with the results through an interactive geo-spatial map. Our demonstration combines multilingual query construction with LLM-based rewriting, web searching, automated FSI classification with final expert verification, scheduled re-crawls to sustain accuracy and user navigation of the database through our graphical Food Sharing Map. The methods used in this system can easily be adapted for the exploration of online information in other domains.

Large Language Models (LLMs) have transformed information access by enabling human-like text understanding and generation. This workshop explores the next step for conversational AI: building proactive information-seeking assistants that go beyond reactive question answering. We aim to investigate how LLMs can anticipate user needs, model complex context, support mixed-initiative interactions, integrate retrieval and external tools, personalize responses, adapt through feedback, and ensure fairness, transparency, and cognitive grounding. Bringing together experts from NLP, IR, HCI, and cognitive science, the workshop will serve as a timely forum for advancing intelligent, proactive dialogue systems. It will also foster interdisciplinary collaboration.

Lifelogging is the passive collection, storage and analysis of daily data through wearable sensors. Question Answering (QA) for lifelog data enables natural language interactions with personal daily life records, providing insights into individual routines and behaviours. While this task has great potential for personal analytics and memory augmentation, progress has been limited due to the challenges of lifelog management, since they can comprise of enormous multi-modal data sets spanning a lifetime. We introduce a Retrieval-Augmented Generation (RAG) approach for addressing the lifelog QA task. A RAG approach first includes a retrieval model finding the correct lifelog events containing answers and then a large language model (LLM) generating answers from the questions. In addition, we construct an open-ended lifelog QA benchmark with 14,187 QA pairs to examine the RAG approach to lifelog QA. Using an embedding-based retrieval approach, our lifelog context retriever achieves a performance of 77.67% Recall@5 and 94.35% Recall@20 using an embedding-based retrieval approach with the Stella 1.5B model. Combined with the Mistral 7B model, the model achieves scores of 39.54% ROUGE-L and 3.475 Accuracy on a scale of 5 scored by GPT-4o. This approach potentially provides an effective approach to lifelog QA with high performance that does not require fine-tuning.

Urban and peri-urban (UPU) food systems encounter challenges in sustainability and are fragile and vulnerable to shocks.Addressing these issues is one of the key drivers of food sharing initiatives (FSIs) which focus on collective acts around food across the food system.FSIs range from seed sharing and surplus food redistribution to community composting.We describe our development and deployment of web retrieval and content classification tools designed to provide automated mapping of FSIs at scale to populate databases of FSIs within cities.We present our novel automated system tailored for retrieving, identifying, categorizing and realtime monitoring of FSIs in over 200 European cities. Developed within the European CULTIVATE project, this system not only aids in comprehending the complex dynamics of the food sharing economy, but also enhances its visibility and operational efficiency.The automation of these processes plays a vital role in supporting the goals of the CULTIVATE project, notably in promoting sustainable food practices and resilient local food networks.Our system integrates web search using queries constructed automatically using domain-specific vocabulary resources with Large Language Model (LLM) query writing and classification methods.Experimental results using a collection of data derived from real online FSI content underscore the potential of digital automation to make significant contributions to innovative digital solutions to contemporary sustainability challenges.As such, the findings of this work pave the way for future research and implementation in similar contexts.

Conversational search system seek to support users in their search activities to improve the effectiveness and efficiency of search while reducing their cognitive load. The challenges of multimedia search mean that search supports provided by conversational search have the potential to improve the user search experience. For example, by assisting users in constructing better queries and making more informed decisions in relevance feedback stages whilst searching. However, previous research on conversational search has been focused almost exclusively on text archives. This demonstration illustrates the potential for the application of conversational methods in multimedia search. We describe a framework to enable multimodal conversational search for use with multimedia archives. Our current prototype demonstrates the use of an conversational AI assistant during the multimedia information retrieval process for both image and video collections.

Lifelogging is the process of automatically logging data about an individual's daily life, which can then be used in various domains, such as behavior analysis and health monitoring. Various technological devices, including wearable cameras and smartwatches, can help record lifelog data, but getting access to lifelog cameras has proven difficult in recent years, due to a lack of such devices on the market. Creating a lifelog camera that is not only easy to use and cost-efficient but also provides comprehensive functions to log all images about life is challenging due to the lack of hardware and software. This paper introduces MemoriLens, a low-cost camera that efficiently collects, organizes, and stores lifelog data using a readily available custom-designed Raspberry Pi Zero board. The camera is designed to capture images automatically and send them to a private account in cloud services for storage. We open-source the implementing of the camera at: https://github.com/linh222/raspberry_lifelog_camera and we encourage lifelog researchers to use our designs and software as required.

The AudioCHI 2022 workshop focusses on human engagement with spoken material in search settings, including live stream audio and collections. Spoken material comes in many forms, including for example: factual or entertaining (or both!), timely or of historical interest, local or global, single speaker or conversations. Users engage with spoken material for a variety of reasons, including entertainment, current affairs, education, and research. While there has been considerable previous work studying spoken document retrieval or more generally spoken content retrieval, AudioCHI 2022 is the first meeting to explore user engagement with audio content, including discussing: (i) how content analysis might establish verbal and non-verbal features for rich content representations, and (ii) and use cases and human factors in interaction with spoken audio content, and their interaction with more established topics relating to spoken content retrieval. The workshop brings together researchers in spoken content retrieval with expertise on human computer interaction in information access to examine opportunities and challenges for advancing technologies for search and interaction with spoken content.

Established information retrieval (IR) systems are generally reactive in that they respond to active entry of a search query by a user. Information is thus only provided to a user when they identify a need for information, and invest the effort to address this need using a search engine. As such, users end up spending more effort to satisfy their information need. Different from reactive systems, proactive information retrieval (PIR) systems seek to retrieve relevant content without the user explicitly submitting a query. A PIR system usually achieves this by using a combination of observed user activities, their contexts and user profile information firstly to automatically create search queries, and then perform search operations and present retrieved results. This new paradigm of PIR methodology thus presents opportunities for investigating a number of research questions for improving user satisfaction, such as when would be the right opportunities to deliver information to users, what should be the presentation mode of search results, establishing a conversational communication channel with the users to ask clarifying questions to refine the retrieved information, and even providing users with explanations for the retrieved information. The aim of the workshop thus is to solicit submissions from the research community targeted towards these topics, and consolidate the research directions in this domain via exchange of ideas.

Reducing user effort in finding relevant information is one of the key objectives of search systems. Existing approaches have been shown to effectively exploit the context from the current search session of users for automatically suggesting queries to reduce their search efforts. However, these approaches do not accomplish the end goal of a search system—that of retrieving a set of potentially relevant documents for the evolving information need during a search session. This article takes the problem of query prediction one step further by investigating the problem of contextual recommendation within a search session. More specifically, given the partial context information of a session in the form of a small number of queries, we investigate how a search system can effectively predict the documents that a user would have been presented with had he continued the search session by submitting subsequent queries. To address the problem, we propose a model of contextual recommendation that seeks to capture the underlying semantics of information need transitions of a current user’s search context. This model leverages information from a number of past interactions of other users with similar interactions from an existing search log. To identify similar interactions, as a novel contribution, we propose an embedding approach that jointly learns representations of both individual query terms and also those of queries (in their entirety) from a search log data by leveraging session-level containment relationships. Our experiments conducted on a large query log, namely the AOL, demonstrate that using a joint embedding of queries and their terms within our proposed framework of document retrieval outperforms a number of text-only and sequence modeling based baselines.

Podcasts have continued to experience rapid growth in both cultural relevance as well as research attention. Coming off the success of the first PodRecs Workshop for Podcast Recommendations at RecSys in 2020, as well as to build upon the research datasets and prior work released in the last year, the second PodRecs Workshop for Podcast Recommendations was held at RecSys 2021 to further develop the community of researchers and practitioners interested in the recommendation of podcasts.

Rapidly growing online podcast archives contain diverse content on a wide range of topics. These archives form an important resource for entertainment and professional use, but their value can only be realized if users can rapidly and reliably locate content of interest. Search for relevant content can be based on metadata provided by content creators, but also on transcripts of the spoken content itself. Excavating relevant content from deep within these audio streams for diverse types of information needs requires varying the approach to systems prototyping. We describe a set of diverse podcast information needs and different approaches to assessing retrieved content for relevance. We use these information needs in an investigation of the utility and effectiveness of these information sources. Based on our analysis, we recommend approaches for indexing and retrieving podcast content for ad hoc search.

The workshop on Supporting and Understanding of (multi-party) conversational Dialogues (SUD) seeks to encourage researchers to investigate automated methods to analyze and understand conversations, and also explore methodologies for proactively providing assistance to the communicating parties during conversations, ranging from summarizing the minutes of meetings to automatically keeping track of action items etc. The workshop will have (1) a regular research paper track, and a more focused (2) data challenge track, inviting papers on a specific task of contextualizing entities of interest from conversation dialogues.

Evaluation in non-factoid question answering tasks generally takes the form of computation of automatic metric scores for systems on a sample test set of questions against human-generated reference answers. Conclusions drawn from the scores produced by automatic metrics inevitably lead to important decisions about future directions. Metrics commonly applied include ROUGE, adopted from the related field of summarization, BLEU and Meteor, both of the latter originally developed for evaluation of machine translation. In this paper, we pose the important question, given that question answering is evaluated by application of automatic metrics originally designed for other tasks, to what degree do the conclusions drawn from such metrics correspond to human opinion about system-generated answers? We take the task of machine reading comprehension (MRC) as a case study and to address this question, provide a new method of human evaluation developed specifically for the task at hand.

The Third WEPIR 2020 workshop builds on the success of the first two WEPIR meetings held at CHIIR 2018 and CHIIR 2019. WEPIR 2020 again brings together researchers from different backgrounds interested in continuing to explore and advance the evaluation of personalisation in information retrieval. Similar to the first two workshops, WEPIR 2020 has a strong emphasis on active participation by workshop attendees. This was very successfully achieved in the first two workshops by the use of workshop breakout groups exploring topics related to personalisation and information retrieval, and the evaluation of personalisation in information retrieval in general, with subsequent report back to the workshop as a whole. However, a key difference for WEPIR 2020 is that while the first two workshops focused on developing and articulating principles and ideas relating general topics relating to these topics, identified as interesting and important by the attendees at the workshops, WEPIR 2020 focuses breakout discussion on a number of relevant specific use cases of the evaluation of personalisation in information retrieval. A use case is assigned to each breakout group with the plan being to have more than one group work on each use case. The task for each group is to identify specific relevant factors relating to the use case in terms of user activities, data to be collected, ethical issues, and evaluation metrics. Groups will make reports of their discussions to the assembled workshop in the final session with discussion of the alternative solutions relating to the same use case, and contrasting the issues raised by the different use cases. The overall goal of the workshop is to work towards developing a general set of principles and guidelines for addressing the evaluation of specific instances of the use of personalisation in information retrieval tasks. This is consistent with the activities and goals of the first two workshops, but represents a significant progression of the activities towards concrete outcomes of benefit to those exploring personalisation in search, the issues arising in its evaluation, and how researchers might go about tackling this in specific situations.

Recent years have seen a growth in interest in the development of conversational search systems intended to support users in their information seeking activities using incremental iterative dialogue-based search. This work has primarily focused on communication of information exclusively via spoken dialogue. While this is sufficient for simple question type queries, it is an inefficient means of engagement for more complex or exploratory queries. We introduce a prototype multi-view search interface which combines a conversational search assistant with an extended standard graphical search interface.

It is often useful for an IR practitioner to analyze the similarity function of an IR model, or for a non-technical search engine user to understand why a document was shown at a certain rank, in terms of the three fundamental aspects of a similarity function, namely the a) frequency of a term in a document, b) frequency of a term in a collection and c) the length of a document. We propose a general methodology of approximating an IR model as the coefficients of a linear function of these three fundamental aspects (and an additional aspect of semantic similarity between terms for neural models), which potentially can help IR practitioners to optimize the relative importance of each aspect on specific document collection and types of queries. Our analysis shows that the coefficients, which represent the relative importance of the three fundamental aspects, are useful to compare a model's different parametric instantiations or compare across different models.

The second WEPIR 2019 workshop brings together researchers with different backgrounds interested in continuing to explore and advance the evaluation of personalisation in information retrieval. The workshop builds on the first WEPIR workshop held at CHIIR 2018, and will focus on further developing a common understanding of the challenges, requirements and practical limitations of meaningful evaluation of personalisation in information retrieval. In particular, the planned outcome of the workshop is to progress the work from WEPIR 2018 towards the development of concrete proposals for novel and innovative methodologies to support evaluation of personalised information retrieval from both the perspectives of the user experience in interactive search settings, and of user models for personalised information retrieval and their algorithmic incorporation in the search process.

The purpose of the WEPIR 2018 workshop is to bring together researchers from different backgrounds, interested in advancing the evaluation of personalisation in information retrieval. The workshop focus is on the development of a common understanding of the challenges, requirements and practical limitations of meaningful evaluation of personalisation in information retrieval. The planned outcome of the workshop is the proposal of methodologies to support evaluation of personalised information retrieval from both the perspectives of the user experience in interactive search settings, and of user models for personalised information retrieval and their algorithmic incorporation in the search process.

Users of current search systems actively interact with the system to complete their search task. This can encompass formulating and reformulating a series queries expressing evolving of different information needs. We believe that the next generation of search systems will see a shift towards proactive understanding of user intent based on analysis of user activities. Such a proactive search system could start recommending documents that are likely to help users accomplish their tasks without requiring them to explicitly submit queries to the system. We propose a framework to evaluate such a search system. The key idea behind our proposed metric is to aggregate a correlation measure over a search session between the expected outcome, which in this case refers to the list of documents retrieved with a true user query, and the predicted outcome, which refers to the list of documents recommended by a proactive search system. Experiments on the AOL query log data show that the ranking of two sample proactive IR systems induced by our metric conforms to the expected ranking between these systems.

We describe an initial study into the identification of important and useful information units within documents retrieved by an information retrieval system in response to a user query created in response to an underlying information need. This study is part of a large investigation of the exploitation of useful and important units from retrieved documents to generate rich document surrogates to improve user search experience. We report three user studies using a crowdsourcing platform, where participants were first asked to read an information need and contents of a relevant document and then to perform actions depending on the type of study: i) write important information units (WIIU), ii) highlight important information units (HIIU) and iii) assess importance of already highlighted information units (AIHIU). Further, we discuss a novel mechanism of measuring similarities between content annotations. We find majority agreement of about 0.489 and pairwise agreement of 0.340 among users annotation in the AIHIU study, and average cosine similarity of 0.50 and 0.57 between participant annotations and documents in the WIIU and HIIU studies respectively.

We present a novel method for the generation of automatic video summaries of academic presentations. We base our investigation on a corpus of multimodal academic conference presentations combining transcripts with paralinguistic multimodal features. We first generate summaries based on keywords by using transcripts created using automatic speech recognition (ASR). Start and end times for each spoken phrase are identified from the ASR transcript, then a value for each phrase created. Spoken phrases are then augmented by incorporating scores for human annotation of paralinguistic features. These features measure audience engagement, comprehension and speaker emphasis. We evaluate the effectiveness of summaries generated for individual presentations, created using speech transcripts and paralinguistic multimodal features, by performing eye-tracking evaluation of participants as they watch summaries and full presentations, and by questionnaire of participants upon completion of eye-tracking studies. Summaries were also evaluated for effectiveness by performing comparisons with an enhanced digital video browser.

A limitation of standard information retrieval (IR) models is that the notion of term composionality is restricted to pre-defined phrases and term proximity. Standard text based IR models provide no easy way of representing semantic relations between terms that are not necessarily phrases, such as the equivalence relationship between `osteoporosis' and the terms `bone' and `decay'. To alleviate this limitation, we introduce a relevance feedback (RF) method which makes use of word embedded vectors. We leverage the fact that the vector addition of word embeddings leads to a semantic composition of the corresponding terms, e.g. addition of the vectors for `bone' and `decay' yields a vector that is likely to be close to the vector for the word `osteoporosis'. Our proposed RF model enables incorporation of semantic relations by exploiting term compositionality with embedded word vectors. We develop our model for RF as a generalization of the relevance model (RLM). Our experiments demonstrate that our word embedding based RF model significantly outperforms the RLM model on standard TREC test collections, namely the TREC 6,7,8 and Robust ad-hoc and the TREC 9 and 10 WT10G test collections.

No abstract available.

Mixing multiple languages within the same document, a phenomenon called (linguistic) code mixing or code switching, is a frequent trend among multilingual users of social media. In the context of information retrieval (IR), code mixing may affect retrieval effectiveness due to the mixing of different vocabularies with different collection statistics within a single collection of documents. In this paper, we investigate the indexing and retrieval strategies for a mixed collection of documents, comprising of code-mixed and the monolingual documents. In particular, we address three alternative modes of indexing, namely (a) a single index for the two sub-collections; (b) a separate index for each sub-collection; and (c) a clustered index with two individual sub-collection statistics coupled with the overall one. We make use of the expected retrievability scores of the two classes of documents to empirically show that indexing strategies (a) and (b) mostly retrieve the monolingual documents at top ranks with standard retrieval approaches. Our experiments show that, by contrast, the clustered index (c) is able to alleviate this problem by improving the retrievability of the code-mixed documents.

In passage and XML retrieval, contextualisation techniques seek to improve the rank of a relevant element by considering information from its surrounding elements and its container document. Recent research has demonstrated that some of these techniques are also particularly effective in spoken content retrieval tasks (SCR). However, no previous research has directly compared contextualisation techniques in an SCR setting, nor has it studied their potential to provide robustness to speech recognition errors. In this paper, we evaluate different contextualisation techniques, including a recently proposed technique based on positional language models (PLM) on the task of retrieving relevant spoken passages in response to a spoken query. We study the benefits of these techniques when queries and documents are transcribed with increasingly higher error rates. Experimental results over the Japanese NTCIR SpokenQuery&Doc collection show that combining global and local context is beneficial for SCR and that models usually benefit from using larger amounts of context in highly noisy conditions.

A rapid increase in the number of questions posted on community question answering (CQA) forums is creating a need for automated methods of question quality moderation to improve the effectiveness of such forums in terms of response time and quality. Such automated approaches should aim to classify questions as good or bad for a particular forum as soon as they are posted based on the guidelines and quality standards defined/listed by the forum. Thus, if a question meets the standard of the forum then it is classified as good else we classify it as bad. In this paper, we propose a method to address this problem of question classification by retrieving similar questions previously asked in the same forum, and then using the text from these previously asked similar questions to predict the quality of the current question. We empirically validate our proposed approach on the set of StackOverflow data, a massive CQA forum for programmers, comprising of about 8M questions. With the use of these additional text retrieved from similar questions, we are able to improve the question quality prediction accuracy by about 2.8% and improve the recall of negatively scored questions by about 4.2%. This improvement of 4.2% in recall would be helpful in automatically flagging questions as bad (unsuitable) for the forum and will speed up the moderation process thus saving time and human effort.

Word2vec, a state-of-the-art word embedding technique has gained a lot of interest in the NLP community. The embedding of the word vectors helps to retrieve a list of words that are used in similar contexts with respect to a given word. In this paper, we focus on using the word embeddings for enhancing retrieval effectiveness. In particular, we construct a generalized language model, where the mutual independence between a pair of words (say t and t') no longer holds. Instead, we make use of the vector embeddings of the words to derive the transformation probabilities between words. Specifically, the event of observing a term t in the query from a document d is modeled by two distinct events, that of generating a different term t', either from the document itself or from the collection, respectively, and then eventually transforming it to the observed query term t. The first event of generating an intermediate term from the document intends to capture how well does a term contextually fit within a document, whereas the second one of generating it from the collection aims to address the vocabulary mismatch problem by taking into account other related terms in the collection. Our experiments, conducted on the standard TREC collection, show that our proposed method yields significant improvements over LM and LDA-smoothed LM baselines.

We address the problem of retrieving chess game positions similar to a given query position from a collection of archived chess games. We investigate this problem from an information retrieval (IR) perspective. The advantage of our proposed IR-based approach is that it allows using the standard inverted organization of stored chess positions, leading to an efficient retrieval. Moreover, in contrast to retrieving exactly identical board positions, the IR-based approach is able to provide approximate search functionality. In order to define the similarity between two chess board positions, we encode each game state with a textual representation. This textual encoding is designed to represent the position, reachability and the connectivity between chess pieces. Due to the absence of a standard IR dataset that can be used for this search task, a new evaluation benchmark dataset was constructed comprising of documents (chess positions) from a freely available chess game archive. Experiments conducted on this dataset demonstrate that our proposed method of similarity computation, which takes into account a combination of the mobility and the connectivities between the chess pieces, performs well on the search task, achieving MAP and nDCG values of 0:4233 and 0:6922 respectively.

Medical information is accessible from diverse sources including the general web, social media, journal articles, and hospital records; information searchers can be patients and their families, researchers, practitioners and clinicians. Challenges in medical information retrieval include: diversity of users and user knowledge and expertise; variations in the format, reliability, and quality of biomedical and medical information; the multi-modal nature of much of the data; and the need for accuracy and reliability of medical information. The aim of the workshop is to bring together researchers interested in medical information search with the goal of identifying specific challenges that need to be addressed to advance the state-of-the-art.

No abstract available.

Information retrieval (IR) systems exploit relevant information when tailoring search results to individual information needs. However, current search experience becomes poor without considering similar queries entered by previous searchers. In the following paper, we discuss a solution to this problem, which combines collaborative filtering algorithms with traditional IR models to enable EIR. We also present various iterative refinement methods for improving the raw performance of this system. We validate our theories in an experiment using queries extracted from the click-through log of a commercial search engine. According to our results, an IR system employing iteratively refined, collaborative retrieval significantly outperforms various baseline retrieval models.

Searching for relevant webpages and following hyperlinks to related content is a widely accepted and effective approach to information seeking on the textual web. Existing work on multimedia information retrieval has focused on search for individual relevant items or on content linking without specific attention to search results. We describe our research exploring integrated multimodal search and hyperlinking for multimedia data. Our investigation is based on the MediaEval 2012 Search and Hyperlinking task. This includes a known-item search task using the Blip10000 internet video collection, where automatically created hyperlinks link each relevant item to related items within the collection. The search test queries and link assessment for this task was generated using the Amazon Mechanical Turk crowdsourcing platform. Our investigation examines a range of alternative methods which seek to address the challenges of search and hyperlinking using multimodal approaches. The results of our experiments are used to propose a research agenda for developing effective techniques for search and hyperlinking of multimedia content.

This paper describes a search system which includes topic model visualization to improve the user search experience. The system graphically renders the topics in a retrieved set of documents, enables a user to selectively refine search results and allows easy navigation through information on selective topics within documents.

Document expansion (DE) in information retrieval (IR) involves modifying each document in the collection by introducing additional terms into the document. It is particularly useful to improve retrieval of short and noisy documents where the additional terms can improve the description of the document content. Existing approaches to DE assume that documents to be expanded are from a single topic. In the case of multi-topic documents this can lead to a topic bias in terms selected for DE and hence may result in poor retrieval quality due to the lack of coverage of the original document topics in the expanded document. This paper proposes a new DE technique providing a more uniform selection and weighting of DE terms from all constituent topics. We show that our proposed method significantly outperforms the most recently reported relevance model based DE method on a spoken document retrieval task for both manual and automatic speech recognition transcripts.

With the increasing popularity of mobile and hand-held devices, automatic approaches for adapting results to the limited screen size of mobile devices are becoming more important. Traditional approaches for reducing the length of textual results include summarization and snippet extraction. In this study, we investigate document rewriting techniques which retain the meaning and readability of the original text. Evaluations on different document sets show that i) rewriting documents considerably reduces document length and thus, scrolling effort on devices with limited screen size, and ii) the rewritten documents have a higher readability.

The number of images available online is growing steadily and current web search engines have indexed more than 10 billion images. Approaches to image retrieval are still often text-based and operate on image annotations and captions. Image annotations (i.e. image tags) are typically short, user-generated, and of varying quality, which increases the mismatch problem between query terms and image tags. For example, a user might enter the query "wedding dress" while all images are annotated with "bridal gown" or "wedding gown". This demonstration presents an image search system using reduction and expansion of image annotations to overcome vocabulary mismatch problems by enriching the sparse set of image tags.

Queries in patent prior art search are full patent applications and much longer than standard ad hoc search and web search topics. Standard information retrieval (IR) techniques are not entirely effective for patent prior art search because of ambiguous terms in these massive queries. Reducing patent queries by extracting key terms has been shown to be ineffective mainly because it is not clear what the focus of the query is. An optimal query reduction algorithm must thus seek to retain the useful terms for retrieval favouring recall of relevant patents, but remove terms which impair IR effectiveness. We propose a new query reduction technique decomposing a patent application into constituent text segments and computing the Language Modeling (LM) similarities by calculating the probability of generating each segment from the top ranked documents. We reduce a patent query by removing the least similar segments from the query, hypothesising that removal of these segments can increase the precision of retrieval, while still retaining the useful context to achieve high recall. Experiments on the patent prior art search collection CLEF-IP 2010 show that the proposed method outperforms standard pseudo-relevance feedback (PRF) and a naive method of query reduction based on removal of unit frequency terms (UFTs).

Topics in prior-art patent search are typically full patent applications and relevant items are patents often taken from sources in different languages. Cross language patent retrieval (CLPR) technologies support searching for relevant patents across multiple languages. As such, CLPR requires a translation process between topic and document languages. The most popular method for crossing the language barrier in cross language information retrieval (CLIR) in general is machine translation (MT). High quality MT systems are becoming widely available for many language pairs and generally have higher effectiveness for CLIR than dictionary based methods. However for patent search, using MT for translation of the very long search queries requires significant time and computational resources. We present a novel MT approach specifically designed for CLIR in general and CLPR in particular. In this method information retrieval (IR) text pre-processing in the form of stop word removal and stemming are applied to the MT training corpus prior to the training phase of the MT system. Applying this step leads to a significant decrease in the MT computational and resource requirements in both the training and translation phases. Experiments on the CLEF-IP 2010 CLPR task show the new technique to be 5 to 23 times faster than standard MT for query translation, while maintaining statistically indistinguishable IR effectiveness. Furthermore the new method is significantly better than standard MT when only limited translation training resources are available.

Automatically generated tags and geotags hold great promise to improve access to video collections and online communities. We overview three tasks offered in the MediaEval 2010 benchmarking initiative, for each, describing its use scenario, definition and the data set released. For each task, a reference algorithm is presented that was used within MediaEval 2010 and comments are included on lessons learned. The Tagging Task, Professional involves automatically matching episodes in a collection of Dutch television with subject labels drawn from the keyword thesaurus used by the archive staff. The Tagging Task, Wild Wild Web involves automatically predicting the tags that are assigned by users to their online videos. Finally, the Placing Task requires automatically assigning geo-coordinates to videos. The specification of each task admits the use of the full range of available information including user-generated metadata, speech recognition transcripts, audio, and visual features.

Decompounding has been found to improve information retrieval (IR) effectiveness in general domains for languages such as German or Dutch. We investigate if cross-language patent retrieval can profit from decompounding. This poses two challenges: i) There may be few resources such as parallel corpora available for training an machine translation system for a compounding language. ii) Patents have a specific writing style and vocabulary ("patentese"), which may affect the performance of decompounding and translation methods. Experiments on data from the CLEF-IP 2010 task show that decompounding patents for translation can overcome out-of-vocabulary problems (OOV) and that decompounding improves IR performance significantly for small training corpora.

User queries to search engines are observed to predominantly contain inflected content words but lack stopwords and capitalization. Thus, they often resemble natural language queries after case folding and stopword removal. Query recovery aims to generate a linguistically well-formed query from a given user query as input to provide natural language processing tasks and cross-language information retrieval (CLIR). The evaluation of query translation shows that translation scores (NIST and BLEU) decrease after case folding, stopword removal, and stemming. A baseline method for query recovery reconstructs capitalization and stopwords, which considerably increases translation scores and significantly increases mean average precision for a standard CLIR task.

Information retrieval (IR) evaluation scores are generally designed to measure the effectiveness with which relevant documents are identified and retrieved. Many scores have been proposed for this purpose over the years. These have primarily focused on aspects of precision and recall, and while these are often discussed with equal importance, in practice most attention has been given to precision focused metrics. Even for recall-oriented IR tasks of growing importance, such as patent retrieval, these precision based scores remain the primary evaluation measures. Our study examines different evaluation measures for a recall-oriented patent retrieval task and demonstrates the limitations of the current scores in comparing different IR systems for this task. We introduce PRES, a novel evaluation metric for this type of application taking account of recall and the user's search effort. The behaviour of PRES is demonstrated on 48 runs from the CLEF-IP 2009 patent retrieval track. A full analysis of the performance of PRES shows its suitability for measuring the retrieval effectiveness of systems from a recall focused perspective taking into account the user's expected search effort.

Pseudo-relevance feedback (PRF) via query-expansion has been proven to be e®ective in many information retrieval (IR) tasks. In most existing work, the top-ranked documents from an initial search are assumed to be relevant and used for PRF. One problem with this approach is that one or more of the top retrieved documents may be non-relevant, which can introduce noise into the feedback process. Besides, existing methods generally do not take into account the significantly different types of queries that are often entered into an IR system. Intuitively, Wikipedia can be seen as a large, manually edited document collection which could be exploited to improve document retrieval effectiveness within PRF. It is not obvious how we might best utilize information from Wikipedia in PRF, and to date, the potential of Wikipedia for this task has been largely unexplored. In our work, we present a systematic exploration of the utilization of Wikipedia in PRF for query dependent expansion. Specifically, we classify TREC topics into three categories based on Wikipedia: 1) entity queries, 2) ambiguous queries, and 3) broader queries. We propose and study the effectiveness of three methods for expansion term selection, each modeling the Wikipedia based pseudo-relevance information from a different perspective. We incorporate the expansion terms into the original query and use language modeling IR to evaluate these methods. Experiments on four TREC test collections, including the large web collection GOV2, show that retrieval performance of each type of query can be improved. In addition, we demonstrate that the proposed method out-performs the baseline relevance model in terms of precision and robustness.

Term weighting methods have been shown to give significant increases in information retrieval performance. The presence of pronomial references in documents reduces the term frequencies of associated words with a consequent effect on term weights and information retrieval behaviour. This investigation explores the impact on information retrieval performance of broad coverage automatic pronoun resolution. Results indicate that this approach has potential to improve both precision at fixed cutoff levels and average precision.

Topic Detection and Tracking (TDT) research explores the development of algorithms to detect novel events and track their development over time for online reports. Development of these methods requires careful evaluation and analysis. Traditional reductive methods of evaluation only represent some of the available information of algorithm behaviour. We describe a visualisation tool for topic tracking which makes it easy to analysis and compare the temporal behaviour of tracking algorithms.

Query-expansion is an effective Relevance Feedback technique for improving performance in Information Retrieval. In general query-expansion methods select terms from the complete contents of relevant documents. One problem with this approach is that expansion terms unrelated to document relevance can be introduced into the modified query due to their presence in the relevant documents and distribution in the document collection. Motivated by the hypothesis that query-expansion terms should only be sought from the most relevant areas of a document, this investigation explores the use of document summaries in query-expansion. The investigation explores the use of both context-independent standard summaries and query-biased summaries. Experimental results using the Okapi BM25 probabilistic retrieval model with the TREC-8 ad hoc retrieval task show that query-expansion using document summaries can be considerably more effective than using full-document expansion. The paper also presents a novel approach to term-selection that separates the choice of relevant documents from the selection of a pool of potential expansion terms. Again, this technique is shown to be more effective that standard methods.

The emergence of networked context-aware mobile computing appliances potentially offers opportunities for remote access to huge online information resources. Information access in context-aware information appliances can utilize existing techniques developed for effective information retrieval and information filtering; however, practical physical and operational features of these devices and the availability of context information itself suggest that the document selection process should make use of this contextual data.

This paper describes a structured investigation into the retrieval of Japanese text.The study includes a comparison of different indexing strategies for documents and queries, investigation of term weighting strategies principally derived for use with English texts, and the application of relevance feedback for query expansion.Results on the standard BMIR-Jl and BMIR-J2 Japanese retrieval collections indicate that term weighting transfers well to Japanese text.Indexing using dictionary based morphological analysis and character strings are both shown to be individually effective, but marginally better in combination.We also demonstrate that relevance feedback can be used effectively for query expansion in Japanese routing applications.

This paper presents domain-independent methods of spoken document retrieval.Both a continuous-speech large vocabulary recognition system, and a phone-lattice word spotter,