  Analysis of acknowledgments is particularly interesting as acknowledgments
may give information not only about funding, but they are also able to reveal
hidden contributions to authorship and the researcher's collaboration patterns,
context in which research was conducted, and specific aspects of the academic
work. The focus of the present research is the analysis of a large sample of
acknowledgement texts indexed in the Web of Science (WoS) Core Collection.
Record types 'article' and 'review' from four different scientific domains,
namely social sciences, economics, oceanography and computer science, published
from 2014 to 2019 in a scientific journal in English were considered. Six types
of acknowledged entities, i.e., funding agency, grant number, individuals,
university, corporation and miscellaneous, were extracted from the
acknowledgement texts using a Named Entity Recognition (NER) tagger and
subsequently examined. A general analysis of the acknowledgement texts showed
that indexing of funding information in WoS is incomplete. The analysis of the
automatically extracted entities revealed differences and distinct patterns in
the distribution of acknowledged entities of different types between different
scientific domains. A strong association was found between acknowledged entity
and scientific domain and acknowledged entity and entity type. Only negligible
correlation was found between the number of citations and the number of
acknowledged entities. Generally, the number of words in the acknowledgement
texts positively correlates with the number of acknowledged funding
organizations, universities, individuals and miscellaneous entities. At the
same time, acknowledgement texts with the larger number of sentences have more
acknowledged individuals and miscellaneous categories.
