Dataset of Natural Language Queries for E-Commerce

Papenmeier, Andrea; Kern, Dagmar; Hienert, Daniel; Sliwa, Alfred; Aker, Ahmet; Fuhr, Norbert

doi:10.1145/3406522.3446043

Cited by 7 publications

(3 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finding and buying products through conversational interactions is becoming popular (Papenmeier et al, 2021). E-commerce transactions, the process of buying and selling of goods and services online, are steadily increasing.…”

Section: Conversational Information Seeking In E-commercementioning

confidence: 99%

Conversational Information Seeking

Zamani¹,

Trippas²,

Dalton³

et al. 2022

Preprint

View full text Add to dashboard Cite

Conversational information seeking (CIS) is concerned with a sequence of interactions between one or more users and an information system. Interactions in CIS are primarily based on natural language dialogue, while they may include other types of interactions, such as click, touch, and body gestures. This monograph provides a thorough overview of CIS definitions, applications, interactions, interfaces, design, implementation, and evaluation. This monograph views CIS applications as including conversational search, conversational question answering, and conversational recommendation. Our aim is to provide an overview of past research related to CIS, introduce the current state-of-the-art in CIS, highlight the challenges still being faced in the community. and suggest future directions.

show abstract

Section: Conversational Information Seeking In E-commercementioning

confidence: 99%

Conversational Information Seeking

Zamani¹,

Trippas²,

Dalton³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…For experimenting with data points at different levels of classification difficulty, we needed a dataset with a proper (i.e., objectively correct) ground truth that we can enrich with information about the classification difficulty of data points. We, therefore, chose the VACOS_NLQ dataset [41] that provides 3,560 product descriptions of laptops and jackets collected from English native speakers. During the collection of the VACOS_NLQ dataset, each participant was prompted to describe a jacket and a laptop.…”

Section: Task and Materialsmentioning

confidence: 99%

How Accurate Does It Feel? – Human Perception of Different Types of Classification Mistakes

Papenmeier

Kern

Hienert

et al. 2022

CHI Conference on Human Factors in Computing Systems

Self Cite

View full text Add to dashboard Cite

Supervised machine learning utilizes large datasets, often with ground truth labels annotated by humans. While some data points are easy to classify, others are hard to classify, which reduces the inter-annotator agreement. This causes noise for the classifier and might affect the user's perception of the classifier's performance. In our research, we investigated whether the classification difficulty of a data point influences how strongly a prediction mistake reduces the "perceived accuracy". In an experimental online study, 225 participants interacted with three fictive classifiers with equal accuracy (73%). The classifiers made prediction mistakes on three different types of data points (easy, difficult, impossible). After the interaction, participants judged the classifier's accuracy. We found that not all prediction mistakes reduced the perceived accuracy equally. Furthermore, the perceived accuracy differed significantly from the calculated accuracy. To conclude, accuracy and related measures seem unsuitable to represent how users perceive the performance of classifiers. CCS CONCEPTS• Human-centered computing → Empirical studies in HCI; Human computer interaction (HCI).

show abstract

“…The recent research on this NLP niche by [5] contributed to a dataset of 3,540 natural language queries. Most ecommerce systems use English as the transaction medium.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Malay Computational Grammar Formalism based-on Enhanced Pola Grammar

Mohamed

Fataf

Sembok

2023

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

In the era of IR4.0, Natural Language Processing (NLP) is one of the major focuses because text is stored digitally to code the information. Natural language understanding requires a computational grammar for syntax and semantics of the language in question for this information to be manipulated digitally. Many languages around the world have their own computational grammars for processing syntax and semantics. However, when it comes to the Malay language, the researchers have yet to come across a substantial computational grammar that can process Malay syntax and semantics based on a computational theoretical framework that can be applied in systems such as e-commerce. Hence, we intend to propose a formalism framework based on enhanced Pola Grammar with syntactic and semantic features. The objectives of this proposed framework are to create a linguistic computational formalism for the Malay language based on theoretical linguistic; implement templates for Malay words to handle syntax and semantic features in accordance with the enhanced Pola Grammar; and create a Malay Language Parser Algorithm that can be used for digital applications. To accomplish the objectives, the proposed framework will recursively formalise the computational Malay grammar and lexicon using a combination of solid theoretical linguistic foundations such as Dependency Grammar. A Malay parsing algorithm will be developed for the proposed model until the formalised grammar is deemed reliable. The findings of this indigenous Malay parser will help to advance Malay language applications in the digital economy.

show abstract

Dataset of Natural Language Queries for E-Commerce

Cited by 7 publications

References 23 publications

Conversational Information Seeking

Conversational Information Seeking

How Accurate Does It Feel? – Human Perception of Different Types of Classification Mistakes

A Framework for Malay Computational Grammar Formalism based-on Enhanced Pola Grammar

Contact Info

Product

Resources

About