=Paper=
{{Paper
|id=Vol-1976/paper04
|storemode=property
|title=Incorporating Part-of-Speech Feature and Entity Embedding for Question Entity Discovery and Linking
|pdfUrl=https://ceur-ws.org/Vol-1976/paper04.pdf
|volume=Vol-1976
|authors=Shijia E,Li Yang,Shiyao Xu,Shengbin Jia,Yang Xiang
}}
==Incorporating Part-of-Speech Feature and Entity Embedding for Question Entity Discovery and Linking==
https://ceur-ws.org/Vol-1976/paper04.pdf
Incorporating Part-of-Speech Feature and Entity
Embedding for Question Entity Discovery and
Linking
Shijia E, Li Yang, Shiyao Xu, Shengbin Jia, and Yang Xiang
Tongji University, Shanghai 201804, P.R. China,
{436_eshijia,1452238,1452221,shengbinjia,shxiangyang}@tongji.edu.cn
Abstract. Question entity discovery and linking (QEDL), which aims
to extract the named entities associated with the given question. Typi-
cally, the name of an entity has a certain ambiguity, i.e. the same entity
name may refer to multiple entities. In this paper, we propose a model
which based on the part-of-speech (POS) feature and entity embedding
to solve the QEDL task. The proposed model does not depend on much
feature engineering. Our experiments show that the entity embedding
can make full use of semantic information involved in an entity and its
context word. In the evaluation of the CCKS 2017 shared task, our model
achieves 0.5960 in the F1 score of mentions, and 0.3694 in the F1 score
of entities for the opening test data.
Keywords: entity linking, entity discovery, entity embedding
1 Introduction
Named entity linking (NEL) is to link a given mention to an entity in the knowl-
edge base. It has been pay attention with the development of natural language
processing (NLP) and knowledge graph (KG). In traditional NEL tasks, the text
corpus is long text. Therefore, the solutions can use a lot of context features with
the mentions to accomplish the entity linking. However, in this shared task, ques-
tion entity discovery and linking (QEDL) introduced by CCKS 2017, we need
to find the mentions and link them to the entities in an existing knowledge base
with the short question text. Compared with the traditional NEL tasks, the re-
sources that we can use in QEDL task are just the word in the question and the
entity attributes in the knowledge base. Due to the short length of the text in
this task, we do not have much context feature of the mentions in the question.
Thus, we cannot apply tradition NEL methods directly to this task.
In this paper, we propose a method for the QEDL shared task in CCKS
2017. For the discovery of mention, we use the part of speech features and a
variety of combinatorial strategies to effectively identify possible mentions. As
for the entity linking, we utilize the entity embedding that is generated based
on the entity attributes to do the entity disambiguation. Also, the results of
entity linking can help us do the post-processing to improve the performance of
mention discovery.
The rest of this paper is structured as follows: Section 2 contains related
work. In Section 3, we describe the overall framework of our proposed model in
this task. Experimental results and discussions are presented in Section 4, and
finally, we give some concluding remarks in Section 5.
2 Related Work
In recent years, many researchers have begun to focus on NEL in short texts, in
particular for the Chinese language. Shen et al. [6] propose a method to model
Twitter users’ data. It can make the candidate entities with similar user interest
have a high weight. Guo et al. [1] model the micro-blogs with similar themes
to disambiguate candidate entities. To utilize context features, Liu et al. [3] use
the similarity between the mention context and entity to accomplish the entity
linking with micro-blog data. Jiang et al. [2] use Twitter’s forward, reply, and
other messages of the same user to expand the context of sentiment classifica-
tion. The core of the methods mentioned above is trying to use the contextual
information needed by the entity linking so that the overall performance can be
improved. However, it adds to the cost of data pre-processing and not all data
sources have enough contextual information.
As a result, for this QEDL task, we have tried several methods just based on
the knowledge base to generate the entity embedding. It contains the semantic
information embodied in entity attributes and can be used to do the entity
disambiguation with the limited contextual information of mentions.
3 Model Description
In this section, we describe the proposed method to solve the QEDL task. Figure
1 shows the overall framework of our method. We will provide details for each
module in the following sections.
3.1 Word segmentation for the question text
Word segmentation is the first step of our system. We use Jieba1 , the Chinese
word segmentation tool, to help us get words from each question. Other segmen-
tation tools such as Ansj2 and Thulac3 are attempted, but both of them perform
weaker than Jieba. We adopt accurate mode instead of all mode to segment the
questions. Thus each word is a substring of a question and not overlapped by
others. However, Jieba cannot recognize some relatively long and complicated
words accurately. For example, we expect the word “注册会计师” (certified pub-
lic accountant) could be cut correctly from the question “注册会计师的审计责任
包括哪些?” (what are the audit responsibilities of certified public accountants?),
1
https://github.com/fxsjy/jieba
2
https://github.com/NLPchina/ansj_seg
3
http://thulac.thunlp.org
question get candidate entities
word segmentation entity list
API requests
merge tokens based on entity disambiguation
POS with entity embedding
Mention Entity
Discovery candidate candidate Linking
mentions entities
post-processing
target mentions target entities
Fig. 1. The overall framework of our proposed method.
but unfortunately, it will be further divided into two words (“注册” (register),
“会计师” (accountant)). This problem can be solved by specifying our custom
dictionary to be included in the Jieba default dictionary. The custom dictionary
contains named entities, such as “小米手机” (Mi phones), “韵达快递” (Yunda
Express), from various domains. Each word cut from a question is considered
as a candidate mention or a possible part of a candidate mention. We tag the
part-of-speech (POS) of each word, which will be used to judge whether the
word is a part of a candidate mention.
3.2 Candidate Mention Generation
Merge adjacent words After word segmentation, there are two lists, Seg =
{a1 , a2 , . . . , an } and P os = {p1 , p2 , . . . , pn } (n=the number of words). In most
case, Jieba splits one mention into several words (such as “百度知道企业平台”
(Baidu Zhidao enterprise platform) becomes “百度知道, 企业, 平台”(Baidu
Zhidao, Enterprise, Platform)). We merge all 2, 3, . . . , n-1 and n adjacent words
in Seg into one string. These words and new strings are added to initial mention
list so that the correct mentions are sure to be preserved. The initial candidate
mention set is:
M entioninit = Seg∪{mij | mij = ai . . . aj (i = 1, . . . , n−1; j = i+1, . . . , n)} (1)
Filter based on the POS In our experiments, we can find out that the ma-
jority of the mentions of questions are nouns. The parts of speech that represent
nouns can be picked out with Jieba. The noun POS set is:
N oun_P OS = {an, ng, n, nr, ns, nt, nz, un, nz, eng, nrt, l, i, j, x} (2)
There is also a stop word list to reduce noise. Only the candidate mentions in
the initial list, do not appear in the stop word list and contain at least one word
whose POS is in N oun_P OS list are retained. The filtered candidate mention
set is:
M ention = M entioninit − {mij | mij = ai . . . aj (∀pk ∈
/ N oun_P OS, k = i, . . . , j)}
−{mij | mij ∈ Stopwords}
(3)
3.3 Get Candidate Entities and Prepare the Entity Corpus
In this task, we must use the CN-DBpedia [7] as the standard knowledge base
system. With the API provided by the system, we can get the entity list corre-
sponding to a mention. The target entity will be selected from that list.
Also, the API provided by the knowledge base system allows us to obtain
the relevant attributes of entities, such as the description and category of an
entity. We can use an entity name and its related attributes to form a line of
text. Therefore, we can get a large entity corpus with the candidate entities. This
corpus can be used to train the word embedding with word2vec [4]. In this task,
we use Gensim [5] to train the word embedding. The embedding size is 300.
3.4 Entity Embedding
For the word segmentation of the entity corpus, we do not cut the entity name
so that the entity name will be reserved as a single word. To generate the entity
embedding, we first load the word embedding produced by Gensim. For each line
in the entity corpus, the first word is the entity name, and the other words are
entity attribute values. As a result, an entity embedding is just the average vector
of the distributed representations of its attribute values, and the embedding size
of an entity is still 300. It is important to emphasize that the embedding produced
by Gensim will then be used to generate vector representations of the words in
a question.
3.5 Entity Disambiguation
When we get the entity embedding, we can do the entity disambiguation with
the candidate entities. The word segmentation results of the question text can
be denoted as qwords = {q1 , q2 , . . . , qx } (x=the number of words) which are the
only contextual information we can use. Therefore, we use the following entity
score to represent the similarity between a candidate entity (denoted as ent)and
the question:
∑x
k=1 Cos(Emb(qk ), Emb(ent))
Entity_Score = (4)
x
where Cos(a, b) means the cosine similarity of a and b, and Emb(·) means to get
the vector representation of the word (from raw word embedding) or entity (from
entity embedding). For all the candidate entities of a mention, we select the one
that has the maximum Entity_Score as the entity linked to the mention.
3.6 Post-processing
After the above processing steps, we have got the mentions and entities that
meet the requirements. To further improve the performance of the proposed
method, we need to do the necessary post-processing. First of all, in the can-
didate mention list, if M ention_A is included in M ention_C, e.g. “百度知道”
(Baidu Zhidao) ∈ “百度知道企业平台” (Baidu Zhidao enterprise platform), then
M ention_A and its corresponding entity will be removed from the final results.
In addition, if M ention_B does not have a linked entity in the knowledge base,
and M ention_B is not in the dictionary, then it will be removed as well.
4 Experiments
4.1 Datasets and Implementation
We apply the proposed method directly to the QEDL task. The train set provided
by CCKS 2017 shared task contains 1,400 questions, and the test set contains
749 questions. We use the train set to improve the stability of the model and
extend the dictionary. For the embedding size, we tried 50, 100, 300 and 500,
and the size of 300 performed best on the train set. Therefore, we use the best
parameter configuration on the train set for the final evaluation.
For the entity disambiguation, we also tried to use a similarity measure based
on the longest common subsequence (LCS). Specifically, we calculate the length
of the LCS between the question and the entity description retrieved by the API
and then select the entity with the maximum length of LCS as the target entity.
4.2 Results
Table 1 shows the experimental results on the train set and test set in this task.
As the results shown in Table 1, the proposed method with the entity em-
bedding achieves a good performance with the F1-Entity of 0.3694 on the test
set. It yields a significant improvement compared to the method with LCS. It
proves that the generated entity embedding contains valid semantic information,
which can be used to compare the similarity with the context of mentions, so as
to complete the entity disambiguation task effectively.
Table 1. The experimental results for the train set (top) and test set (bottom).
Method F1- Precision- Recall- F1- Precision- Recall-
Mention Mention Mention Entity Entity Entity
POS + LCS 0.5153 0.4347 0.6327 0.3353 0.2828 0.4117
POS + Entity 0.6839 0.6120 0.7750 0.4942 0.4422 0.5600
embedding
POS + LCS 0.5114 0.4295 0.6320 0.2805 0.2284 0.3635
POS + Entity 0.5960 0.5103 0.6681 0.3694 0.3189 0.4388
embedding
5 Conclusion
In this paper, we describe the method which incorporates the POS feature and
entity embedding for the QEDL task. Our method can achieve good performance
with few feature engineering. For future work, we need to study more effective
evaluation metrics to test whether the results of the mention discovery and entity
linking can meet the requirement of practical applications.
Acknowledgments This work was supported by the National Basic Research
Program of China (2014CB340404), the National Natural Science Foundation
of China (71571136), and the Project of Science and Technology Commission of
Shanghai Municipality (16JC1403000, 14511108002).
References
1. Guo, Y., Qin, B., Liu, T., Li, S.: Microblog entity linking by leveraging extra posts.
In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language
Processing. pp. 863–868 (2013)
2. Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment
classification. In: ACL (1). pp. 151–160. Association for Computational Linguistics
(2011)
3. Liu, X., Li, Y., Wu, H., Zhou, M., Wei, F., Lu, Y.: Entity linking for tweets. In:
ACL (1). pp. 1304–1311 (2013)
4. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word repre-
sentations in vector space. arXiv preprint arXiv:1301.3781 (2013)
5. Rehurek, R., Sojka, P.: Software framework for topic modelling with large corpora.
In: In Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frame-
works. Citeseer (2010)
6. Shen, W., Wang, J., Luo, P., Wang, M.: Linking named entities in tweets with knowl-
edge base via user interest modeling. In: Proceedings of the 19th ACM SIGKDD
international conference on Knowledge discovery and data mining. pp. 68–76. ACM
(2013)
7. Xu, B., Xu, Y., Liang, J., Xie, C., Liang, B., Cui, W., Xiao, Y.: Cn-dbpedia: A
never-ending chinese knowledge extraction system. In: International Conference on
Industrial, Engineering and Other Applications of Applied Intelligent Systems. pp.
428–438. Springer (2017)