=Paper=
{{Paper
|id=Vol-2036/T2-9
|storemode=property
|title=A Hybrid Model For Information Retrieval From Microblogs During Disaster
|pdfUrl=https://ceur-ws.org/Vol-2036/T2-9.pdf
|volume=Vol-2036
|authors=Du Xin,Wang Xiaoyu,Zhuang Ziyao,Qi Limin
|dblpUrl=https://dblp.org/rec/conf/fire/DuWZQ17
}}
==A Hybrid Model For Information Retrieval From Microblogs During Disaster==
https://ceur-ws.org/Vol-2036/T2-9.pdf
A Hybrid Model For Information Retrieval From
Microblogs During Disaster
Du Xin Wang Xiaoyu
School of Computer Science and School of Computer Science and
Technology, Heilongjiang Institute of Technology, Heilongjiang Institute of
Technology, Harbin, China Technology, Harbin, China
duxin111@outlook.com wongxiaoyu1946@gmail.com
Zhuang Ziyao Qi Limin
Faculty of Science, Agriculture and School of Physical Sciences, Harbin
Engineering, University of Newcastle upon Normal University, Harbin, China
Tyne, UK
qilimin111@outlook.com
zhuangziyao1@outlook.com
Abstract tweets from other tweets such as useless tweets, and
repeat forwarding tweets.
When the disaster occurs, the social network site
such as Twitter is increasingly being used for For Task 2, Using the results identified by Task 1,
helping direct rescue operations. This article we implement the matching task. We should match
describes the methods we used in the Fire2017. need-tweets with availability-tweets. Also, a need-
tweet and a matching availability-tweets can be in
We regarded the distinction of need-tweets and
different language. we deem this task as an information
availability-tweets as classification tasks, and the retrieval problem. For each of the need-tweets in the
logistic regression and Support Vector Machine query collection, retrieve the most matching
are used to decide the type of the tweets. In the availability-tweets. In the match between need-tweets
need and availability matching, we regard it as an and availability-tweets.
information retrieval task, using the retrieval
model to complete the task. 2. Task 1
2.1 Problem Description
KEYWORDS
In Task 1, we judge that the text is need-tweets,
information retrieval, microblog, identify need- availability-tweets, or other using the correlation
tweets and availability-tweets, matching need- between text and category. Based on the description of
tweets and availability-tweets classification problem, the classification of text content
includes the expression of the text, the selection and
1. Introduction training of the classifier, text preprocessing, feature
Now users on the social networking site to publish extraction and so on. We use the training data (π₯1 , π¦1 ),
real-time content, become the most extensive (π₯2 , π¦2 ,, , (π₯π , π¦π ) to learn a classifier Y=f(x,, so
information sharing, one of the fastest ways to spread that the classification system can use the classifier for
information. The published material can contain new data to mark. π₯π represents the characteristic
specific locations, specific needs, especially when the vector of data. π¦π represents the classification system
disaster comes, can provide accurate rescue information output. π¦π labels as {0,1}.
to guide the rescue efficiently.
2.2 Classifier Models
For Task 1, we need to judge the need-tweets, In this working note, two groups adopt SVM, which
availability-tweets and others. We deem it as a is a binary model, which is defined as the most
classification problem. We use the classifiers to significant liner classifier in the feature space. The
distinguish the useful need-tweets and the availability- learning strategy is the interval maximization. Solving
the problem into Convex Quadratic Programming Remove URL β β β
Problem. We chose the RBF kernel, which can map the
samples non-linearly to a higher dimension. The RBF ID 2_1 2_2 2_3
core has fewer numerical complexity characteristics.
Ultimately two groups completed Task 1 with LibSVM. Remove stop word β β Γ
The third group uses LR model. Each data point of Remove @user name β β β
the LR model has an impact on the classification plane.
Remove punctuation β β β
Its influence is far from its distance to the classification
plane, and if the data dimension is high, the LR model Remove URL β β β
will match the parameters regularization method. In
classification, the computation is minimal, and the Note:
speed is breakneck, so the storage resources are low, so
it is convenient to observe the probability scores of (1, HLJIT2017-IRMIDIS_1_task1_1 = 1_1
samples.
(2, HLJIT2017-IRMIDIS_1_task1_2 = 1_2
2.3 Feature Selection (3, HLJIT2017-IRMIDIS_1_task1_3 = 1_3
For feature selection, we conduct experiments with
words and n-gram (n = 2, 3, 4, 5, respectively. The (4, HLJIT2017-IRMIDIS_1_task2_1 = 2_1
results show that we have the best effect of using words
(5, HLJIT2017-IRMIDIS_1_task2_2 = 2_2
as features. Table 1 shows the comparison of the test
results of each feature. For the preprocessing of tweet (6, HLJIT2017-IRMIDIS_1_task2_3 = 2_3
text, each team adopts different methods to preprocess
the data. Table 2 specifically describes the different 3. Task 2
teams in the pre-treatment differences exist. (β
indicates that this method is used, and Γ is not used, 3.1 Problem Description
Task 2 requires that the need-tweets match in Task
Note:
1 be searched by Task 1. Need-tweets is used as a query
HLJIT2017-IRMIDIS_1_task1_1 and HLJIT2017- set Q. Availability-tweets can be used as a collection of
IRMIDIS_1_task1_2 is different in stop words list. documents D. We use statistical language models to
solve the problem of Task 2. Language models are used
to assess what kind of word sequences are more typical
Table 1: The Result of various characteristic according to language usages, and inject the right bias
accordingly into the system to prefer an output
Feather Need Availability sequence of words with high probability according to
Pre Re Pre Re the language model. If a document language model
gives the query a high probability, the query words
2-gram 0.281 0.245 0.477 0.474 must have top opportunities according to the document
language model, which further means that the query
3-gram 0.482 0.254 0.671 0.597 words frequently occur in the document.
4-gram 0.428 0.245 0.641 0.606 3.2 Relation
5-gram 0.385 0.2 0.604 0.556 The correlation calculation can be expressed briefly
as shown in Figure 1, using the Need-Tweets (N, as the
word 0.517 0.281 0.709 0.644 query set Q, the Availability-tweets (A, as the
document set D, and then the correlation calculation to
obtain the correlation R (Q, D,.
Table 2: Preprocess tweet text Figure 1: Relevance calculation
ID 1_1 1_2 1_3
N Q D A
Remove stop word β β Γ
Remove @username β β β
Remove punctuation β β β R (Q, D)
Table 3 describes the parameters of the two tools
selected in this article.
3.3 Language Model
We use the indri open-source retrieval tool and use Table 3: Parameter setting
the Dirichlet smoothing language model. The formula
SVM svm_type=c_svc,kernel_type=rbf,
is as follows:
π(π€; π) + ππ(π€|πΆ) Gamma=0.1οΌnr_class=2οΌ
ππ (π€|π) =
βπ€ π(π€; π) + π total_sv=8660οΌRho=0.313597
π
ππ = LR studyRate=0.01οΌtheta = 0.05
βπ€ π(π€; π) + π
iterNum=1000
We give a discount to the probability of the word
appearing in the document and provide extra value to
the likelihood of the word that does not appear in the
document. 4.4 Experimental results
π ππππ(π, π) = πππ π(π|π) Table 4 shows the experimental results of Task 1,
the average Map of LR algorithm is higher than that of
ππ (ππ |π)
= β πππ two groups of using LibSVM. The Availability-Tweet
ππ π(ππ |π) and Need-Tweets Map values of the two groups using
π:π(π:π(ππ :π)>0)
LibSVM showed a contrast. And they all were much
+ β ππππ(ππ |π) + ππππππ higher than the other one, which led to the low Average
π Map value. The preliminary analysis may be due to
π (π€|π) π€βπ improper selection of stop words caused by the
π(π€|π) = { π πππ occurrence of the above phenomenon.
ππ π(π€|πΆ) ππ‘πππ€ππ π
4. Experimental Result Table 5 shows the experimental results of Task 2.
Task 2 of the input is based on task 1 is output. The
4.1 Data Set precision of the three sets of values was almost the same,
but the first group of Recall was much lower than the
At the start of the track, about 20,000 tweets other two teams. Probably the result of the submission
released (training set,, along with a sample of need- in Task 1 is too much, and there is a mistake on the
tweets and availability-tweets in these 20 K tweets. threshold.
Later, a new set of 50,000 tweets released (test set,.
Table 4: Task 1 Experimental Result
4.2 Evaluation index
Precision = number of correct messages extracted / ID 1_1 1_2 1_3
number of extracted messages
Avail- Precision 0.550 0.100 0.760
Recall = the number of correct messages extracted /
the number of messages in the sample ability @100
(π½2 + 1)ππ
Recall 0.100 0.017 0.159
πΉ β π ππππ =
π½2 π + π
@1000
Ξ² is the parameter, π is the precision, and π
is the
recall. Map 0.760 0.001 0.112
MapοΌ Need Precision 0.060 0.630 0.610
@100
πΜππΏ = ππππππ₯π π(π₯|π)
Recall 0.021 0.217 0.173
π(π₯|π)π(π) @1000
πΜππ΄π = ππππππ₯π
β«β π(π₯|π β² )π(π β² )ππ β² MAP 0.001 0.140 0.128
4.3 Parameter setting Average MAP 0.031 0.071 0.120
Table 5: Task 2 Experimental Result
Precision@5 Recall F-score
2_1 0.088 0.021 0.034
2_2 0.088 0.217 0.125
2_3 0.082 0.147 0.105
5. Conclusion
Through the above experimental results, we found
that the classification and sorting of text content for
some informal occasions are very regarding words. In
the future experiments, we will deepen the study of
machine learning and try to select more different
features, to filter and choose text content of informal
occasions.
Acknowledgments
This work is supported by Philosophy and Social
Science Planning Project of Heilongjiang
Province, China (No. 16EDD05)
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