Neural information retrieval: at the end of the early years

Onal, Kezban Dilek; Zhang, Ye; Altıngövde, İsmail Sengör; Rahman, Md. Mustafizur; Karagöz, Pınar; Braylan, Alex; Dang, Brandon; Chang, Hua‐Hua; Kim, Henna; McNamara, Quinten; Angert, Aaron; Banner, Edward A.; Khetan, Vivek; McDonnell, Tyler; Nguyen, An; Xu, Dan; Wallace, Byron C.; Rijke, Maarten de; Lease, Matthew

doi:10.1007/s10791-017-9321-y

Cited by 106 publications

(59 citation statements)

References 159 publications

(330 reference statements)

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“…Embeddings are obtained by considering word neighborhood as the context, and hence capture the semantics of text even without exact word match. These methods are very effective and popular for all NLP tasks across the domains (Onal et al, 2018). One of the limitations of deep learning based vector embedding as highlighted in (Moody, 2016) is the inability to provide interpretative insights.…”

Section: Methodsmentioning

confidence: 99%

“…Until recently, Vector Space Model and Latent Semantic Indexing, LSI with its variants were used largely for semantic representation of text. With the emergence of word/document embeddings, information retrieval is now shifted to neural information retrieval (Onal et al, 2018). Dense vector representations of word and document obtained using deep learning based models are used as input for machine learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Legal document similarity: a multi-criteria decision-making perspective

Wagh

Anand

2020

PeerJ Computer Science

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The vast volume of documents available in legal databases demands effective information retrieval approaches which take into consideration the intricacies of the legal domain. Relevant document retrieval is the backbone of the legal domain. The concept of relevance in the legal domain is very complex and multi-faceted. In this work, we propose a novel approach of concept based similarity estimation among court judgments. We use a graph-based method, to identify prominent concepts present in a judgment and extract sentences representative of these concepts. The sentences and concepts so mined are used to express/visualize likeness among concepts between a pair of documents from different perspectives. We also propose to aggregate the different levels of matching so obtained into one measure quantifying the level of similarity between a judgment pair. We employ the ordered weighted average (OWA) family of aggregation operators for obtaining the similarity value. The experimental results suggest that the proposed approach of concept based similarity is effective in the extraction of relevant legal documents and performs better than other competing techniques. Additionally, the proposed two-level abstraction of similarity enables informative visualization for deeper insights into case relevance.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Legal document similarity: a multi-criteria decision-making perspective

Wagh

Anand

2020

PeerJ Computer Science

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“…Other topics related to scoring and ranking are only partly covered, e.g., term dependency in [27,56] and semantic scoring in [15,27,56]. This is natural as some topics, such as word embeddings for ranking [2] and neural IR [48,58,60] have only been introduced recently. Given the above observations, we believe that today's IR textbooks have to be updated to include recent developments in scoring and ranking, especially in the fields of LTR and semantic learning to match (especially, neural IR).…”

Section: Offline Phasementioning

confidence: 99%

“…Ai et al [3] Unbiased learning to rank Arguello [5] Aggregated search Barocas and Hardt [8] Fairness in machine learning Bast et al [9] Semantic search, knowledge graphs Budylin et al [12,13] Online evaluation Burges [14] Learning to rank Cai and de Rijke [16] Query auto-completion Cambazoglu and Baeza-Yates [17] Infrastructure Chuklin et al [21,22,23,24] Click models Crestani et al [26] Mobile information retrieval Gao et al [30] Conversational search Glowacka [32] Bandit algorithms Grotov and de Rijke [33] Online learning to rank Hajian et al [35] Algorithmic bias Hofmann et al [40] Online evaluation Hui Yang and Zhang [41] Differential privacy in information retrieval Joachims and Swaminathan [44] Counterfactual evaluation and learning Jones [45] Mobile search Kanoulas [46] Online and offline evaluation Kelly [47] User studies Kenter et al [48] Neural methods in information retrieval Knijnenburg and Berkovsky [49] Privacy in recommender systems Lalmas [51] XML retrieval Lattimore and Szepesvári [52] Bandit algorithms Liu [55] Offline learning to rank Mehrotra et al [57] Task understanding Mitra and Craswell [58] Neural methods in information retrieval Onal et al [60] Neural methods in information retrieval Oosterhuis [61] Online evaluation and ranking Ren et al [66] E-commerce Sakai [67] Experimental design and methodology Santos et al [68] Diversification Silvestri…”

Section: Author(s) Topicmentioning

confidence: 99%

What Should We Teach in Information Retrieval?

Markov¹,

Rijke

2019

SIGIR Forum

Self Cite

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Modern Information Retrieval (IR) systems, such as search engines, recommender systems, and conversational agents, are best thought of as interactive systems. And their development is best thought of as a two-stage development process: offline development followed by continued online adaptation and development based on interactions with users. In this opinion paper, we take a closer look at existing IR textbooks and teaching materials, and examine to which degree they cover the offline and online stages of the IR system development process. We notice that current teaching materials in IR focus mostly on search and on the offline development phase. Other scenarios of interacting with information are largely absent from current IR teaching materials, as is the (interactive) online development phase. We identify a list of scenarios and a list of topics that we believe are essential to any modern set of IR teaching materials that claims to fully cover IR system development. In particular, we argue for more attention, in basic IR teaching materials, to scenarios such as recommender systems, and to topics such as query and interaction mining and understanding, online evaluation, and online learning to rank.

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“…The answer selection task is simply: given a question and a set of candidate answers, select the correct answer. This task has recently been investigated as a neural IR problem [9,11]. This paper considers a practical approach to investigating the impact of training dataset size on the performance that can be achieved with various deep neural architectures for the task of answer selection.…”

Section: Introductionmentioning

confidence: 99%

Impact of Training Dataset Size on Neural Answer Selection Models

Linjordet

Balog

2019

Lecture Notes in Computer Science

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It is held as a truism that deep neural networks require large datasets to train effective models. However, large datasets, especially with high-quality labels, can be expensive to obtain. This study sets out to investigate (i) how large a dataset must be to train well-performing models, and (ii) what impact can be shown from fractional changes to the dataset size. A practical method to investigate these questions is to train a collection of deep neural answer selection models using fractional subsets of varying sizes of an initial dataset. We observe that dataset size has a conspicuous lack of effect on the training of some of these models, bringing the underlying algorithms into question. This is the authors version of the work. It is posted here for your personal use. The definitive version is published in:

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Neural information retrieval: at the end of the early years

Cited by 106 publications

References 159 publications

Legal document similarity: a multi-criteria decision-making perspective

Legal document similarity: a multi-criteria decision-making perspective

What Should We Teach in Information Retrieval?

Impact of Training Dataset Size on Neural Answer Selection Models

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