acl acl2013 acl2013-266 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Yuanchao Liu ; Ming Liu ; Xiaolong Wang ; Limin Wang ; Jingjing Li
Abstract: In this paper, we propose PAL, a prototype chatterbot for answering non-obstructive psychological domain-specific questions. This system focuses on providing primary suggestions or helping people relieve pressure by extracting knowledge from online forums, based on which the chatterbot system is constructed. The strategies used by PAL, including semantic-extension-based question matching, solution management with personal information consideration, and XML-based knowledge pattern construction, are described and discussed. We also conduct a primary test for the feasibility of our system.
Reference: text
sentIndex sentText sentNum sentScore
1 School of public health, Harbin Medical University, Harbin, China , , { lyc ml iu wangxl , j j l} @ insun . [sent-2, score-0.062]
2 hit Abstract In this paper, we propose PAL, a prototype chatterbot for answering non-obstructive psychological domain-specific questions. [sent-3, score-0.462]
3 This system focuses on providing primary suggestions or helping people relieve pressure by extracting knowledge from online forums, based on which the chatterbot system is constructed. [sent-4, score-0.659]
4 The strategies used by PAL, including semantic-extension-based question matching, solution management with personal information consideration, and XML-based knowledge pattern construction, are described and discussed. [sent-5, score-0.28]
5 We also conduct a primary test for the feasibility of our system. [sent-6, score-0.044]
6 1 Introduction A wide variety of chatterbots and question-and-answer (Q&A;) systems have been proposed over the past decades, each with strengths that make them appropriate for particular applications. [sent-7, score-0.178]
7 With numerous advances in information construction, people increasingly aim to communicate with computers using natural language. [sent-8, score-0.064]
8 For example, chatterbots in some e-commerce Web sites can interact with customers and provide help similar to a real-life secretary (DeeAnna Merz Nagel, 2011; Yvette Colón, 2011). [sent-9, score-0.27]
9 In this paper, we propose PAL (Psychologist of Artificial Language), a chatterbot system for answering non-obstructive psychological questions. [sent-10, score-0.506]
10 Non-obstructive questions refer to problems on family, human relationships, marriage, life pressure, learning, work and so on. [sent-11, score-0.157]
11 In these cases, we expect the chatterbot to play an active role by providing tutoring, solution, support, advice, or even sympathy depending on the help needed by its users. [sent-12, score-0.32]
12 In the following sections, we will briefly discuss related work and then introduce our system and its main features. [sent-17, score-0.044]
13 2 Related Work A number of research work on chatterbots (Rafael E. [sent-18, score-0.178]
14 Several studies on the application of natural language processing technologies for non-obstructive psychological Q&A; systems have also been published (Hai-hu Shi, 2005). [sent-21, score-0.089]
15 Several online psychology counselling Web sites with service provided by human experts have also been established recently (DeeAnna Merz Nagel, 2011; Yvette Colón, 2011). [sent-22, score-0.226]
16 For these Web sites, when the visitors ask similar questions, the expert may provide the same or very similar answers repeatedly. [sent-23, score-0.178]
17 Based on this observation and consideration, we collected a large number of counselling Q&A; pairs to extract common knowledge for the construction of a chatterbot system. [sent-24, score-0.447]
18 Advances in automatic language analysis and processing are used as the bases for the emergence of a complex, task-oriented chatterbot system. [sent-25, score-0.32]
19 The basic control logic strategy is shown in Figure 3. [sent-29, score-0.156]
20 Basic Control Logic of PAL As shown in Figure 3, the initial state is set to welcome mode, and the system can select a sentence from the “sign on” list, which will then provide a response. [sent-31, score-0.107]
21 When users enter a question, the system will conduct the necessary analysis. [sent-32, score-0.128]
22 The system’s knowledge base is indexed by Clucene1 beforehand. [sent-33, score-0.183]
23 Thus, the knowledge index will be used to search the matched records quickly. [sent-34, score-0.182]
24 If the system can find the matched patterns directly and the answer is suitable for the current user, one answer will be randomly selected to generate the response. [sent-35, score-0.359]
25 Historical information and personal information will be analysed when necessary. [sent-36, score-0.113]
26 We mainly adopted the method of ELIZA2, which is an open-source program, to consider the historical information. [sent-37, score-0.086]
27 A “not found” response list is also set to deal with situations when no suitable answers can be identified. [sent-38, score-0.174]
28 Both system utterance and user input will be pushed into the stack as historical information. [sent-39, score-0.238]
29 Given that user questions are at times very simple, the combination with historical input may also be required to determine its meaning. [sent-40, score-0.351]
30 This step can also avoid the duplication of utterances. [sent-41, score-0.031]
31 5 Knowledge Construction Question Matching Method and We design P-XML to store the knowledge base for PAL, as shown in Figure 4. [sent-42, score-0.146]
32 The knowledge base for PAL is mainly derived from the Q&A; pairs in the BAIDU ZHIDAO community3. [sent-43, score-0.146]
33 com 69 An effective method of capturing the user’s meaning accurately is to create an extension for questions in the knowledge base. [sent-50, score-0.268]
34 In this paper, the extension is primarily a synonym expansion of the keywords of questions, with CILIN (Wanxiang Che, 2010) as extension knowledge source. [sent-51, score-0.166]
35 The questions are indexed by Clucene to improve the retrieval efficiency of the search for a matched entry in the knowledge base. [sent-52, score-0.337]
36 During the knowledge base searching step, both the index of the original form and the extension form of the problem are used to find the most possible matched record for the user’s question, as shown in algorithm 1. [sent-53, score-0.379]
37 Algorithm 1is used to examine the similarity between user input and the record returned by Clucene, including traditional and extension similarities. [sent-54, score-0.324]
38 6 Response Management Method One question usually has many corresponding answers in the knowledge base, and these answers differ in explanation quality. [sent-55, score-0.394]
39 Thus, the basic strategy employed by solution management is to select a reliable answer from the matched r ec ord as response, as shown in algorithm 2. [sent-56, score-0.244]
40 Based on these rules, if one answer contains personal information, it will be selected as the candidate answer only when the personal information is consistent with that of the current user. [sent-59, score-0.454]
41 Very concise answers that do not contain personal information can generally be selected as a candidate answer. [sent-60, score-0.248]
42 7 Experiments For the current implementation of PAL, the size of the knowledge base is approximately 1. [sent-61, score-0.146]
43 com, which is one of the largest Chinese online communities. [sent-65, score-0.059]
44 The criterion for choosing these six categories is also because they are the main topics in BAIDU communities about psychological problems. [sent-66, score-0.089]
45 Some information on the knowledge base is given in Table 1, in which “Percent of questions matched” denotes the number of similar questions found when 100 open questions are input (we suppose that if the similarity threshold is bigger than 0. [sent-67, score-0.89]
46 5, then a similar question will be deemed as “hit” in the knowledge base). [sent-68, score-0.124]
47 1, we examine the feasibility of using downloaded dialogue collection for constructing the knowledge base. [sent-70, score-0.21]
48 of unique Percent of questions Size(MB) length Terms in ques. [sent-79, score-0.157]
49 1 System Performance Evaluation Additional questions and their corresponding answers beyond the knowledge base are also used as a test set to evaluate system performance. [sent-96, score-0.482]
50 Concretely, suppose question Q has |A| answers in the test set. [sent-97, score-0.242]
51 Suppose the system output is O, we examine if one best answer exists among |A| answers that are very similar to O (the similarity is greater than threshold T3). [sent-99, score-0.438]
52 If yes, we then assume that one suitable answer has been found. [sent-100, score-0.114]
53 In this way, 70 precision can be calculated as the number of questions that have very similar answers in the system divided by the number of all input questions. [sent-101, score-0.375]
54 The horizon axis denotes the similarity threshold (T1 for sim1 and T2 for sim2) between a user’s input and the questions in the knowledge base. [sent-103, score-0.496]
55 Sim1 is the original similarity, whereas sim2 is the semantic extension similarity. [sent-104, score-0.055]
56 The similarity threshold T3 denotes the similarity between the answer in the test set and system output O. [sent-108, score-0.353]
57 Basically, when T3 is bigger, the system’s performance tends to decrease because a high T3 value denotes a strict evaluation standard. [sent-117, score-0.041]
58 When only index is used and both sim1 and sim2 are below the corresponding threshold T1 or T2, the system can still return record set RS2, but the returned answer may be inconsistent with user’s question. [sent-119, score-0.349]
59 Precision of PAL with different similarity thresholds T3 (The X axis denotes different thresholds for sim1 (T1) and sim2 (T2). [sent-134, score-0.241]
60 The Y axis stands for the precision value of different T1, T2 and T3 values for different areas). [sent-140, score-0.061]
61 2．． Some Interaction Examples Table 2 provides an interaction example of PAL in English. [sent-142, score-0.048]
62 As users input their questions, the system can output the corresponding responses. [sent-143, score-0.117]
63 Basically, when users describe their troubles, PAL can provide some explanation or even try to console the user. [sent-145, score-0.034]
64 Compared to other chatterbots like Eliza, PAL is domain-specific and may provide help to some extent. [sent-146, score-0.178]
65 Thus, the user could input the same question many times, with the system providing suggestions from different angles. [sent-148, score-0.252]
66 PAL can also output voice by using Xiao-kun and Microsoft Anna as the text-to-speech (TTS) engine. [sent-149, score-0.041]
67 The speed, volume and echo properties of the voice can be adjusted. [sent-150, score-0.041]
68 Dialog for Figure 1 (in English) PAL> Hello, you are welcome to communicate with me! [sent-151, score-0.127]
69 You can input from the left side and press enter to get answer! [sent-152, score-0.089]
70 I> How do I deal with the children who like to complain very frequently? [sent-153, score-0.068]
71 Don't’ let him experience the benefits of complaining very frequently. [sent-155, score-0.031]
72 I> Why do parents give children so much pressure PAL> Children have pressure indeed; but parents also have a lot of pressure. [sent-156, score-0.411]
73 I> Why am Iunable to control my emotions, and often quarrel with my parents? [sent-157, score-0.068]
74 Example of an interaction with PAL (in English) 71 8 Conclusions In this paper, we introduce a simple chatterbot for answering non-obstructive psychological questions. [sent-166, score-0.51]
75 The knowledge behind the chatterbot is learned from the Q&A; pairs derived from an online forum using several extraction strategies. [sent-167, score-0.435]
76 The historical and personal information from the dialogues are also incorporated to output an appropriate answer. [sent-168, score-0.199]
77 , enabling the system to ask questions actively and further improving P-XML to form richer patterns for storing Q&A; knowledge. [sent-171, score-0.241]
78 Another interesting aspect would be to add speech input as well as TTS and to transform PAL into a mobile platform for widespread use. [sent-172, score-0.039]
79 2007AA01Z172 Youth Funds of China social & humanity science (10YJCZH099), and Key Laboratory Opening Funding of China MOE—MS Key Laboratory of Natural Language Processing and Speech (HIT. [sent-174, score-0.031]
80 Research on on-line psychology consultation expert system based on man-machine interaction technique. [sent-182, score-0.17]
81 Research of sentiment model based on HMM and its application in psychological consulting expert system. [sent-185, score-0.132]
82 Improving the performance of question answering with semantically equivalent answer patterns. [sent-189, score-0.235]
83 Language, logic and ontology: Uncovering the structure of commonsense knowledge. [sent-201, score-0.119]
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