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ISCApad Archive » 2020 » ISCApad #262 » Events » Other Events » (2020-07-11) CfP FinSBD-2, the 2nd shared task on Sentence Boundary Detection in PDF Noisy Text in the Financial Domain, Yokohama, Japan |
ISCApad #262 |
| Tuesday, April 14, 2020 by Chris Wellekens |
3-3-47 (2020-07-11) CfP FinSBD-2, the 2nd shared task on Sentence Boundary Detection in PDF Noisy Text in the Financial Domain, Yokohama, Japan
We would like to invite you to submit to FinSBD-2, the 2nd shared task Register here: https://forms.gle/NixDGuVjrdFMjYhR9 [4]
Collocated with FIN-NLP 2020 workshop: http://finnlp.nlpfin.com [1] Submission deadline: May 8, 2020 Workshop date: IJCAI-PRICAI 2020 @ July 11-13th, 2020, Yokohama, Japan Motivation ======== Sentences Sentences are basic units of the written language. Detecting the beginning and end of sentences, or sentence boundary detection (SBD), is the foundational first step in many Natural Language Processing (NLP) applications such as POS tagging; syntactic, semantic, and discourse parsing; information extraction; or machine translation. Despite its important role in NLP, Sentence Boundary Detection has so far not received enough attention. Especially for noisy texts extracted from machine-readable files (generally PDF file format) such as financial documents. They also contain many visual demarcations indicating a hierarchy of sections including bullets and numbering. There are many sentence fragments and titles, and not just complete sentences. The prospectuses more often than not contain punctuation errors. And in order to structure the dense information in a more easily read format, lists are often used. Lists This year, we have included the task of extracting lists due to their unique structure and common occurrence in financial documents. A list can be similar to a sentence that enumerates several items of the same category. For example, the ?Simple List? from Figure 1 [6] can be easily read as one normal sentence. However, looking at Figure 2 [6], the list cannot be read as one sentence; although it is one unit, because there are multiple sentences included and there is a visible hierarchy of information. It is therefore important to make the distinction between sentences and lists and, for these lists, to create a hierarchy that organizes the items. Mastering this distinction and item hierarchy can pave the way for more accurate information extraction.
Task Description ============= Last year we organized the first edition of FinSBD focusing on extracting well-segmented sentences from Financial prospectuses in PDF format by detecting their beginning and ending boundaries in two languages: English and French. In addition to an improved version of the previously proposed task, this year we are extending this task to include the detection of lists and list items, as well as their hierarchy. FinSBD'2 is split into two sub-tasks: - Extracting sentence boundaries, including list and list item boundaries. - Organizing the lists items hierarchically. For each given PDF, a JSON will be provided containing: - text extracted (key 'text') - sentence boundaries (key 'sentence') - list boundaries (key 'list') - list item boundaries (key 'item') - list item boundaries of level 1 (key 'item1') - list item boundaries of level 2 (key 'item2') - list item boundaries of level 3 (key 'item3') - list item boundaries of level 4 (key 'item4') Item boundaries overlap with item boundaries of different levels. Each item level represents its depth within the list. Boundaries are represented by indexes of starting and ending characters that the system has to predict. We also included the PDF coordinates of each boundaries as metadata (which can be used for visualization on PDF if needed). Example =======
{ 'text': 'Ce document fournit des informations aux investisseurs ...', 'sentence': [{'start': 17, 'end': 53, 'coordinates':...}, ...], 'list': [{'start': 1080, 'end': 1267, 'coordinates':...}, ...], 'item': [...], 'item1': [...], 'item2': [...], 'item3': [...], 'item4': [...] } Sub-task 1 consists in predicting boundaries of sentences, lists and list items. Sub-task 2 consists in predicting boundaries of item1, item2, item3 and item4. We can also see sub-task 2 as refining item boundaries into 4 classes of boundaries (item = item1 + item2 + item3 + item4). Last year, participants were only given indexes of tokens. This year, we are providing indexes of characters as well as coordinates of boundaries to allow different kind of character or word tokenization and/or possible usage of spatial and visual cues. Therefore, we hope to encourage novel approaches based on multimodality, especially since lists are often spatially structured to convey information visually. Improved annotation guidelines will also be provided to explain how the new and richer dataset was created. Participants can choose to work on both languages, or submit systems for one language only. They can participate in one or both sub-tasks. This task is open to everyone. The only exception are the co-chairs of the organizing team, who cannot submit a system, and who will serve as an authority to resolve any disputes concerning ethical issues or completeness of system descriptions. Evaluation ======== For each sub-task, the evaluation metrics will be computed based on boundaries which are pairs of character indexes ('start' and 'end'). The F-score will be the official metric and an evaluation script will be provided to all the teams. Prize ==== A USD$1000 prize will be rewarded to the best-performing teams. Important dates ============ First announcement of the shared task and beginning of registration: 13 March Release of training data and scoring script: before 30 March Test set made available: 1 May
Registration deadline: 8 May Systems' outputs collected: 8 May Shared task system paper submissions due: 15 May Notification of acceptance: 31 May Camera-ready version of shared task system papers due: 15 June FinNLP 2020 Workshop: 11-13 July Contact ====== For any questions on the shared task please contact us on fin.sbd.task@gmail.com [5] Shared Task Organizing committee =========================== Abderrahim AIT-AZZI, Fortia Financial Solutions Willy AU, Fortia Financial Solutions Bianca CHONG, Fortia Financial Solutions Dialekti VALSAMOU-STANISLAWSKI, Fortia Financial Solutions Sincerely, The FinSBD Organizers IJCAI-20 Read more: https://sites.google.com/nlg.csie.ntu.edu.tw/finnlp2020/shared-task-finsbd-2 [1] FinNLP: http://finnlp.nlpfin.com [2] FinSBD-2: https://sites.google.com/nlg.csie.ntu.edu.tw/finnlp2020/shared-task-finsbd-2 [3] IJCAI-20: https://ijcai20.org/ [4] Registration form: https://forms.gle/NixDGuVjrdFMjYhR9 [5] mailto: fin.sbd.task@gmail.com |
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