A comparison of cooking recipe named entities between Japanese and English

In this paper, we analyze the structural differences between the instructional text in Japanese and English cooking recipes. First, we constructed an English recipe corpus of 100 recipes, designed to be comparable to an existing Japanese recipe corpus. We annotated recipe named entities (r-NEs) in the English corpus according to guidelines previously defined for Japanese. We trained a state-of-art NE recognizer, PWNER, on the English r-NEs, and achieved very similar accuracy and coverage to previous results for the Japanese corpus, thus demonstrating the quality and consistency of the annotations. Second, we compared the r-NEs annotated in the Japanese and English corpora, and uncovered lexical, semantic, and underlying structural differences between Japanese and English recipes. We discuss reasons for these differences, which have significant implications for cross-language retrieval and automatic translation of recipes

English recipe flow graph corpus

We present an annotated corpus of English cooking recipe procedures, and describe and evaluate computational methods for learning these annotations. The corpus consists of 300 recipes written by members of the public, which we have annotated with domain-specific linguistic and semantic structure. Each recipe is annotated with (1) `recipe named entities' (r-NEs) specific to the recipe domain, and (2) a flow graph representing in detail the sequencing of steps, and interactions between cooking tools, food ingredients and the products of intermediate steps. For these two kinds of annotations, inter-annotator agreement ranges from 82.3 to 90.5 F1, indicating that our annotation scheme is appropriate and consistent. We experiment with producing these annotations automatically. For r-NE tagging we train a deep neural network NER tool; to compute flow graphs we train a dependency-style parsing procedure which we apply to the entire sequence of r-NEs in a recipe.In evaluations, our systems achieve 71.1 to 87.5 F1, demonstrating that our annotation scheme is learnable

A comparison of cooking recipe named entities between Japanese and English

In this paper, we analyze the structural differences between the instructional text in Japanese and English cooking recipes. First, we constructed an English recipe corpus of 100 recipes, designed to be comparable to an existing Japanese recipe corpus. We annotated recipe named entities (r-NEs) in the English corpus according to guidelines previously defined for Japanese. We trained a state-of-art NE recognizer, PWNER, on the English r-NEs, and achieved very similar accuracy and coverage to previous results for the Japanese corpus, thus demonstrating the quality and consistency of the annotations. Second, we compared the r-NEs annotated in the Japanese and English corpora, and uncovered lexical, semantic, and underlying structural differences between Japanese and English recipes. We discuss reasons for these differences, which have significant implications for cross-language retrieval and automatic translation of recipes

An experimental framework for designing document structure for users' decision making -- An empirical study of recipes

Textual documents need to be of good quality to ensure effective asynchronous communication in remote areas, especially during the COVID-19 pandemic. However, defining a preferred document structure (content and arrangement) for improving lay readers' decision-making is challenging. First, the types of useful content for various readers cannot be determined simply by gathering expert knowledge. Second, methodologies to evaluate the document's usefulness from the user's perspective have not been established. This study proposed the experimental framework to identify useful contents of documents by aggregating lay readers' insights. This study used 200 online recipes as research subjects and recruited 1,340 amateur cooks as lay readers. The proposed framework identified six useful contents of recipes. Multi-level modeling then showed that among the six identified contents, suitable ingredients or notes arranged with a subheading at the end of each cooking step significantly increased recipes' usefulness. Our framework contributes to the communication design via documents

Towards Multi-Language Recipe Personalisation and Recommendation

Multi-language recipe personalisation and recommendation is an under-explored field of information retrieval in academic and production systems. The existing gaps in our current understanding are numerous, even on fundamental questions such as whether consistent and high-quality recipe recommendation can be delivered across languages. In this paper, we introduce the multi-language recipe recommendation setting and present grounding results that will help to establish the potential and absolute value of future work in this area. Our work draws on several billion events from millions of recipes and users from Arabic, English, Indonesian, Russian, and Spanish. We represent recipes using a combination of normalised ingredients, standardised skills and image embeddings obtained without human intervention. In modelling, we take a classical approach based on optimising an embedded bi-linear user-item metric space towards the interactions that most strongly elicit cooking intent. For users without interaction histories, a bespoke content-based cold-start model that predicts context and recipe affinity is introduced. We show that our approach to personalisation is stable and easily scales to new languages. A robust cross-validation campaign is employed and consistently rejects baseline models and representations, strongly favouring those we propose. Our results are presented in a language-oriented (as opposed to model-oriented) fashion to emphasise the language-based goals of this work. We believe that this is the first large-scale work that comprehensively considers the value and potential of multi-language recipe recommendation and personalisation as well as delivering scalable and reliable models.Comment: 5 table

ユーザ ノ ジユウナ ナズケ ニ ヨリ シテイサレタ ブッタイ ノ ガゾウ ニンシキ

京都大学0048新制・課程博士博士(情報学)甲第12626号情博第223号新制||情||48(附属図書館)UT51-2006-S634京都大学大学院情報学研究科知能情報学専攻(主査)教授 美濃 導彦, 教授 奥乃 博, 教授 河原 達也学位規則第4条第1項該当Doctor of InformaticsKyoto UniversityDA