没有合适的资源?快使用搜索试试~ 我知道了~
利用文档级信息结合语义空间加强事件检测.docx
1.该资源内容由用户上传,如若侵权请联系客服进行举报
2.虚拟产品一经售出概不退款(资源遇到问题,请及时私信上传者)
2.虚拟产品一经售出概不退款(资源遇到问题,请及时私信上传者)
版权申诉
0 下载量 134 浏览量
2022-12-15
14:20:29
上传
评论
收藏 255KB DOCX 举报
温馨提示
试读
15页
利用文档级信息结合语义空间加强事件检测.docx
资源推荐
资源详情
资源评论
Event detection (ED) is a crucial task of event extraction (EE), which aims to identify event
triggers from text and classify them into corresponding event types. The event trigger is the word
or phrase that can clearly indicate the existence of an event in a sentence. According to the
automatic context extraction (ACE) 2005 dataset, which is widely applied to the ED task, there are
8 event types and 33 subtypes, such as “Attack”, “Transport”, “Meet” etc. Take the following
sentences as examples:
S1: He has died of his wounds after being shot.
S2: An American tank fired on the Palestine hotel.
S3: Another veteran war correspondent is being fired for his controversial conduct in Iraq.
An ideal ED model is expected to recognize two events: a “Die” event triggered by the
trigger word “died” and an “Attack” event triggered by “shot” in S1.
The difficulty of the ED task lies in the diversity and ambiguity of natural language
expression. On the one hand, there are a variety of expressions that belong to the same event type.
In S1, “shot” triggers an “Attack” event, and “fired” also triggers the same event type in S2. On
the other hand, the same trigger can denote different events. In S3, “fired” can trigger an “Attack”
event or an “End-Position” event. Because of the ambiguity, a traditional approach may mislabel
“fired” with “Attack” according to the word “war” with sentence-level information. However, in
the same document, other sentences like “NBC is terminating freelancer reporter Peter Arnett for
statements he made to the Iraqi media.” could provide the clue that “fired” triggers an “End-
Position” event. Up to 57% of the event triggers are ambiguous in the ACE 2005 dataset
[1]
. Thus,
how to solve the ambiguity of event trigger has become an important problem in ED task.
ED is a booming and challenging task in NLP. The dominant approaches for ED adopt deep
neural networks to learn effective features for the input sentences. Most existing methods either
generally focus on sentence-level context, or ignore the correlations between events, such as
semantic correlation information. Many methods
[2-3]
mainly exploit sentence-level features that
lack a summary of the document. Sometimes sentence-level information is insufficient to address
the ambiguity of event trigger, such as the event trigger “fired” in S3. Some document-level
models have been proposed to leverage global context
[4-6]
. However, these methods extract
features of the entire document, which are coarse-grained features for event classification.
Actually, by means of processing context more effectively, the model’s performance can be
improved.
The semantic correlations between different events exist objectively and pervasively, and
they are manifested in several aspects. Initially, different event types have some semantic
relevance. For instance, compared with the “Transport” event, the “Attack” event and the “Injure”
event are semantically closer. Belonging to the same parent event type, different subtypes have
certain semantic correlations. “Be-Born” and “Marry” belong to the same parent event type
“Life”, which can reveal more collective features. They are more likely to co-occur in the same
document. Furthermore, different event triggers have some semantic correlations in the same
document, such as event trigger “shot” and “died” in S1. The events mentioned in the same
document tend to be semantically coherent. As pointed out by Ref. [5], many events usually co-
occur in the same document. According to the ACE 2005 dataset, the top 5 event types that
accompany with “Attack” event in the same sentence are as follows: Attack, Die, Transport, Injure
and Meet. Eventually, there is similar semantics between the event trigger and its corresponding
event type. The event type word indicates the fundamental semantic information and reveals
common features, and the event trigger word has extended semantic information with a more
specific context. Suppose we replace the trigger word with its corresponding event type word, the
semantics of the whole sentence will not change much. Thus, how to model the semantic
correlation information between event types and event triggers becomes a challenge to be
overcome.
Existing methods generally use the one-hot label, which classifies the event type with the 0/1
label. Despite the simplicity, it regards multiple events in the same document as independent ones,
and therefore it is difficult to accurately represent the correlations between different event types.
In this paper, we propose document embedding networks with shared semantics space
(DENSS) to address the aforementioned problems. To learn the event correlations, we use
bidirectional encoder representations from transformers (BERT) to obtain event type
representations and map them into a semantic space, where the more relevant event types are, the
closer they stay. We apply BERT again to acquire the representation of each word with document-
level and sentence-level information via gated attention, project the representation of each event
trigger into the same semantic space, and choose the label of the closest event type.
In summary, the contributions of this paper are as follows: 1) We study the event
correlations problem and propose a novel ED framework, which utilizes BERT for capturing
document-level and sentence-level information. 2) We employ a shared semantic space to
represent event types and event triggers, which minimizes the distance between each event trigger
and its corresponding type. Experiment results on the ACE 2005 dataset verify the effectiveness of
our approach.
1. Approach
1.1 Task Description
The goal of ED consists of identifying event triggers (trigger identification) and classifying
them into corresponding event types (trigger classification). According to the ACE 2005 dataset,
an event is defined as a specific occurrence involving one or more participants. The event trigger
is the main word or phrase that can most clearly express the occurrence of an event. As shown
in Table 1, the ACE 2005 dataset defines 8 event types and 33 subtypes. Each event subtype has
its specific semantic information and different event subtypes have certain semantic correlations.
表 1 Some Event Types and Subtypes of the ACE 2005 Dataset
Table 1. Some Event Types and Subtypes of the ACE 2005 Dataset
Event
Type
Event Subtype
Life
Be-Born、Marry、Divorce、Injure、Die
Movement
Transport
Personnel
Start-Position、End-Position、Elect、
Nominate
Conflict
Demonstrate、Attack
Business
Merge-Org、Start-Org、End-Org、Declare-
Bankruptcy
下载: 导出 CSV
| 显示表格
Formally, given a training set D={d1,d2,⋯D={d1,d2,⋯di,⋯dl}di,⋯dl}, where ll is the
number of documents in training set, and a document d={s1,s2,⋯sj,⋯sm}d={s1,s2,⋯sj,⋯sm},
where mm is the number of sentences in the document dd, the j-th sentence can be represented
as sj={wj1,sj={wj1,wj2,⋯wjk,⋯wjn}wj2,⋯wjk,⋯wjn}, where nn is the number of words in
sentence sjsj.
1.2 Overview
We formalize ED as a multi-label sequence tagging problem. We assign a tag for each word
to indicate whether it triggers the specific event. We adopt the “BIO” tags schema. Tags “B” and
“I” represent the position of the word in a trigger to solve the problem that a trigger contains
multiple words such as “take away”, “go to” and so on.
Figure 1 describes the architecture of DENSS, which primarily involves the following four
components: 1) Event embedding, which learns correlations between event types through BERT;
2) Word embedding, which exploits BERT and gated attention to gain semantic information of
剩余14页未读,继续阅读
资源评论
罗伯特之技术屋
- 粉丝: 3650
- 资源: 1万+
下载权益
C知道特权
VIP文章
课程特权
开通VIP
上传资源 快速赚钱
- 我的内容管理 展开
- 我的资源 快来上传第一个资源
- 我的收益 登录查看自己的收益
- 我的积分 登录查看自己的积分
- 我的C币 登录后查看C币余额
- 我的收藏
- 我的下载
- 下载帮助
安全验证
文档复制为VIP权益,开通VIP直接复制
信息提交成功