id: 06071807
dt: a
an: 06071807
au: Kolya, Anup Kumar; Das, Dipankar; Ekbal, Asif; Bandyaopadhyay, Sivaji
ti: Roles of event actors and sentiment holders in identifying event-sentiment
    association.
so: Gelbukh, Alexander (ed.), Computational linguistics and intelligent text
    processing. 13th international conference, CICLing 2012, New Delhi,
    India, March 11‒17, 2012. Proceedings, Part I. Berlin: Springer (ISBN
    978-3-642-28603-2/pbk). Lecture Notes in Computer Science 7181, 513-525
    (2012).
py: 2012
pu: Berlin: Springer
la: EN
cc: 
ut: event actor; sentiment holder; TempEval-2; semantic role labeling (SRL);
    name entity recognizer (NER)
ci: 
li: doi:10.1007/978-3-642-28604-9_42
ab: Summary: In this paper, we study the roles of event actors and sentiment
    holders from the perspective of event sentiment relations within the
    TimeML framework. The proposed algorithm is bootstrapping in nature
    that identifies the association between the event and sentiment
    expressions. There are two basic steps of the algorithm and they deal
    with lexical keyword spotting and co-reference resolution. We consider
    the associations between the event and sentiment expressions that are
    in the same or different text segments. Guided by the classical
    definitions of events in the TempEval-2 shared task, a manual
    evaluation is attempted to distinguish the sentiment events from the
    factual events and the agreement was satisfactory. In order to
    computationally estimate the different sentiments associated with
    different events, the knowledge of event actors and sentiment holders
    is introduced. To identify the roles between the event actors and
    sentiment holders, appropriate method is proposed. From the
    experiments, it is observed that the lexical equivalence between event
    and sentiment expressions easily identifies the similar entities that
    are both responsible for the event actors and sentiment holders. If the
    event and sentiment expressions occupy different text segments, the
    identification of their corresponding event actors and sentiment
    holders needs the knowledge of parsed-dependency relations, named
    entities along with the anaphors. The manual evaluation produces
    satisfactory results on the test documents of the TempEval-2 shared
    task in case of identifying the many to many associations between the
    event actors and sentiment holders for a specific event.
rv: