emnlp emnlp2010 emnlp2010-24 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Amit Goyal ; Ellen Riloff ; Hal Daume III
Abstract: In the 1980s, plot units were proposed as a conceptual knowledge structure for representing and summarizing narrative stories. Our research explores whether current NLP technology can be used to automatically produce plot unit representations for narrative text. We create a system called AESOP that exploits a variety of existing resources to identify affect states and applies “projection rules” to map the affect states onto the characters in a story. We also use corpus-based techniques to generate a new type of affect knowledge base: verbs that impart positive or negative states onto their patients (e.g., being eaten is an undesirable state, but being fed is a desirable state). We harvest these “patient polarity verbs” from a Web corpus using two techniques: co-occurrence with Evil/Kind Agent patterns, and bootstrapping over conjunctions of verbs. We evaluate the plot unit representations produced by our system on a small collection of Aesop’s fables.
Reference: text
sentIndex sentText sentNum sentScore
1 Abstract In the 1980s, plot units were proposed as a conceptual knowledge structure for representing and summarizing narrative stories. [sent-4, score-0.404]
2 Our research explores whether current NLP technology can be used to automatically produce plot unit representations for narrative text. [sent-5, score-0.56]
3 We create a system called AESOP that exploits a variety of existing resources to identify affect states and applies “projection rules” to map the affect states onto the characters in a story. [sent-6, score-1.549]
4 We also use corpus-based techniques to generate a new type of affect knowledge base: verbs that impart positive or negative states onto their patients (e. [sent-7, score-1.132]
5 We evaluate the plot unit representations produced by our system on a small collection of Aesop’s fables. [sent-11, score-0.388]
6 1 Introduction In the 1980s, plot units (Lehnert, 1981) were proposed as a knowledge structure for representing narrative stories and generating summaries. [sent-12, score-0.432]
7 Plot units are fundamentally different from the story representations that preceded them because they focus on the affect states of characters and the tensions between them as the driving force behind interesting and cohesive stories. [sent-13, score-0.882]
8 The last few decades have seen tremendous advances in NLP and the emergence of many resources that could be useful for plot unit analysis. [sent-21, score-0.382]
9 So we embarked on a project to see whether plot unit representations can be generated automatically using current NLP technology. [sent-22, score-0.388]
10 We created a system called AESOP that uses a variety of resources to identify words that correspond to positive, negative, and mental affect states. [sent-23, score-0.503]
11 AESOP uses affect projection rules to map the affect states onto the characters in the story based on verb argument structure. [sent-24, score-1.415]
12 Additionally, affect states are inferred based on syntactic properties, and causal and cross-character links are created using simple heuristics. [sent-25, score-0.952]
13 Affect states often arise from actions that produce good or bad states for the character that is acted upon. [sent-26, score-0.798]
14 For example, “the cat ate the mouse ” produces a negative state for the mouse because being eaten is bad. [sent-27, score-0.363]
15 Similarly, “the man fed the dog ” produces a positive state for the dog because being fed is generally good. [sent-28, score-0.367]
16 We create a new type of lexicon consisting of patient polarity verbs (PPVs) that impart positive or negative states on their patients. [sent-32, score-0.975]
17 These verbs reflect world knowledge about desirable/undesirable states for animate beings; for example, being fed, paid or adopted are generally desirable states, while being eaten, chased or hospitalized are generally undesirable states. [sent-33, score-0.504]
18 We evaluate the plot unit representations produced by our system on a small collection of fables. [sent-37, score-0.388]
19 2 Overview of Plot Units Plot unit structures consist of affect states for each character, and links defining the relationships between them. [sent-38, score-0.913]
20 Plot units include three types of affect states: positive (+), negative (-), and mental (M). [sent-39, score-0.75]
21 Affect states can be connected by causal links and cross-character links, which explain how the narrative hangs together. [sent-40, score-0.648]
22 Causal links exist between affect states for the same character and have four types: motivation (m), actualization (a), termination (t) and equivalence (e). [sent-41, score-0.933]
23 To see a concrete example of a plot unit representation, a short fable, “The Father and His Sons,” is shown in Figure 1(a) and our annotation of its plot unit structure is shown in Figure 1(b). [sent-44, score-0.712]
24 In this fable, there are two characters, the “Father” and (collectively) the “Sons”, who go through a series of affect states depicted chronologically in the two columns. [sent-45, score-0.706]
25 The first affect state (a1) is produced from sentence #1 (s1) and is a negative state for the sons because they are quarreling. [sent-46, score-0.821]
26 This state is shared by the 78 father (via a cross-character link) who has a negative annoyance state (a2). [sent-47, score-0.416]
27 This latter structure (the second gray region) is an HONORED REQUEST plot unit structure. [sent-61, score-0.394]
28 We briefly overview the variety of situations that can be represented by affect states in plot units. [sent-64, score-0.901]
29 For example, “Max was disappointed” produces a negative affect state for Max, and “Max was pleased” produces a positive affect state for Max. [sent-66, score-1.296]
30 Situational Affect States: Positive and negative affect states can represent good and bad situational states that characters find themselves in. [sent-67, score-1.259]
31 These states do not represent emotion, but indicate whether a situation (state) is good or bad for a character based on world knowledge. [sent-68, score-0.439]
32 aFnor example, “atyhe b wolf asked an eagle to extract the bone ” is a directive speech act that indicates the wolf’s plan to resolve its negative state (having a bone stuck). [sent-80, score-0.508]
33 This example illustrates how a negative state (bone stuck) can motivate a mental state (plan). [sent-81, score-0.395]
34 Fucoer example, if the eagle successfully extracts the bone from the wolf’s throat, then both the wolf and the eagle will have positive affect states because both were successful in their respective goals. [sent-89, score-0.978]
35 When a character is 79 acted upon (the patient of a verb), then the character may be in a positive or negative state depending upon whether the action was good or bad for them based on world knowledge. [sent-91, score-0.789]
36 Consequently, we decided to create a lexicon of patient polarity verbs that produce positive or negative states for their patients. [sent-93, score-0.993]
37 4 AESOP: Automatically Generating Plot Unit Representations Our system, AESOP, automatically creates plot unit representations for narrative text. [sent-96, score-0.518]
38 AESOP has four main steps: affect state recognition, character identification, affect state projection, and link creation. [sent-97, score-1.143]
39 During affect state recognition, AESOP identifies words that may be associated with positive, negative, and mental states. [sent-98, score-0.574]
40 AESOP then identifies the main characters in the story and applies affect projection rules to map the affect states onto these characters. [sent-99, score-1.352]
41 During this process, some additional affect states are inferred based on verb argument structure. [sent-100, score-0.785]
42 Finally, AESOP creates cross-character links and causal links between affect states. [sent-101, score-0.718]
43 We also present two corpus-based methods to automatically produce a new resource for affect state recognition: a patient polarity verb lexicon. [sent-102, score-0.909]
44 1 Recognizing Affect States The basic building blocks of plot units are affect states which come in three flavors: positive, negative, and mental. [sent-106, score-0.956]
45 We use the verbs listed for these classes to produce M, +, and - affect states. [sent-117, score-0.522]
46 , 2005b): We used the• w MoPrdQs Alis Lteedx as having positive or negative es uesnteidment polarity to produce +/- states, when they occur with the designated part-of-speech. [sent-119, score-0.378]
47 , 2005): Wanet cus Oedri ethntea wioonrd Lse xliicstoend as having positive or negative polarity to produce +/- affect states, when they occur with the designated part-of-speech. [sent-126, score-0.772]
48 3 Mapping Affect States onto Characters Plot unit representations are not just a set of affect states, but they are structures that capture the 1We only selected fables that had two main characters. [sent-143, score-0.753]
49 80 chronological ordering of states for each character as the narrative progresses. [sent-144, score-0.514]
50 Consequently, every affect state needs to be attributed to a character. [sent-145, score-0.491]
51 Since most plots revolve around events, we use verb argument structure as the primary means for projecting affect states onto characters. [sent-146, score-0.825]
52 We developed four affect projection rules that orchestrate how affect states are assigned to the characters. [sent-147, score-1.159]
53 2 The rules only project affect states onto AGENTS and PATIENTS that refer to a character in the story. [sent-151, score-0.884]
54 All affect tags assigned to the VP are projected onto the AGENT. [sent-155, score-0.474]
55 Example: “Mary laughed (+) ” projects a + affect state onto Mary. [sent-156, score-0.571]
56 All affect tags assigned to the VP are projected onto the PATIENT. [sent-159, score-0.474]
57 Example: “John was rewarded (+), projects a + affect state onto John. [sent-160, score-0.571]
58 If the PATIENT is a character, then all affect tags associated with the VP are projected onto the PATIENT. [sent-163, score-0.474]
59 Finally, if an adverb or adjectival phrase has affect, then that affect is mapped onto the preceding VP and the rules above are applied. [sent-171, score-0.5]
60 However, we identified two cases where affect states often can be inferred based on syntactic properties. [sent-177, score-0.722]
61 Consequently, this action should produce a positive affect state for John. [sent-182, score-0.665]
62 To capture this intuition, in rule #4 if VERB 1 does not already have an affect state assigned to it then we produce an inferred mental state for the AGENT. [sent-191, score-0.779]
63 5 Causal and Cross-Character Links Our research is focused primarily on creating affect states for characters, but plot unit structures also include cross-character links to connect states that are shared across characters and causal links between states for a single character. [sent-194, score-2.017]
64 A crosscharacter link is created when two characters in a clause have affect states that originated from the same word. [sent-196, score-0.826]
65 A causal link is created between each pair of (chronologically) consecutive affect states for the same character. [sent-197, score-0.883]
66 Currently, AESOP only produces forward causal links (motivation (m), actualization (a)) and does not produce backward causal links (equivalence (e), termination (t)). [sent-198, score-0.611]
67 two affect states, the order and types ofthe two states uniquely determine which label it gets (m or a). [sent-201, score-0.682]
68 Our intuition was that an “evil” agent will typically perform actions that are bad for the patient, while a “kind” agent will typically perform actions that are good for the patient. [sent-210, score-0.493]
69 We manually identified 40 stereotypically evil agent words, such as monster, villain, terrorist, and murderer, and 40 stereotypically kind agent words, such as hero, angel, benefactor, and rescuer. [sent-211, score-0.629]
70 5 Evaluation Plot unit analysis of narrative text is enormously complex the idea of creating gold standard plot unit annotations seemed like a monumental task. [sent-241, score-0.651]
71 We collected 34 Aesop’s fables from a web site4, choosing fables that have a true plot (some only contain quotes) and exactly two characters. [sent-245, score-0.439]
72 In our gold standard, each affect state is annotated with the set of clauses that could legitimately produce it. [sent-252, score-0.533]
73 During evaluation, the systemproduced affect states must be generated from the correct clause. [sent-254, score-0.682]
74 However, for affect states that could be ascribed to multiple clauses in a sentence, the evaluation was done at the sentence level. [sent-255, score-0.682]
75 In this case, the system-produced affect state must come from the sentence that contains one of those clauses. [sent-256, score-0.491]
76 2 Evaluation of Affect States using External Resources Our first set of experiments evaluates the quality of the affect states produced by AESOP using only the external resources. [sent-262, score-0.682]
77 Note that M and + states are also generated from the negative PPVs because they are inferred during affect projection (Section 4. [sent-274, score-0.897]
78 The precision drop is likely due to redundancy, which creates spurious affect states. [sent-280, score-0.394]
79 If two different words have negative polarity but refer to the same event, then only one negative affect state should be generated. [sent-281, score-0.845]
80 But AE83 SOP will generate two affect states, so one will be spurious. [sent-282, score-0.394]
81 The positive PPVs did generate several correct affect states (including a - state when a positive PPV was negated), but also many spurious states. [sent-287, score-0.979]
82 Evaluating the impact of PPVs on plot unit struc- × tures is an indirect way of assessing their quality because creating plot units involves many steps. [sent-293, score-0.63]
83 The Kind Agent 5The top-ranked Evil/Kind Agent PPV lists (θ > 1) which yields 1203 kind PPVs, and 477 evil PPVs, the top 164 positive Basilisk verbs, and the 678 (unique) negative Basilisk verbs. [sent-315, score-0.393]
84 The second column of Table 3 shows the perfor- mance of AESOP when using gold standard affect states. [sent-329, score-0.394]
85 Our simple heuristics for creating links work surprisingly well for xchar and a links when given perfect affect states. [sent-330, score-0.619]
86 The third column ofTable 3 shows the results when using systemgenerated affect states. [sent-333, score-0.394]
87 First, we created a Baseline system that is identical to AESOP except that it does not use the affect projection rules. [sent-339, score-0.451]
88 Instead, it naively projects every affect state in a clause onto every character in that clause. [sent-340, score-0.667]
89 This illustrates the importance of the projection rules for mapping affect states onto characters. [sent-342, score-0.845]
90 3F 4531 Our gold standard includes pure inference affect states that are critical to the plot unit structure but come from world knowledge outside the story itself. [sent-346, score-1.072]
91 Of 157 affect states in our test set, 14 were pure inference states. [sent-347, score-0.682]
92 Consequently, AESOP generates more spurious affect states from the quotations when using the gold standard annotations. [sent-358, score-0.682]
93 Other preliminary work has begun to look at plot unit modelling for single character stories (Appling and Riedl, 2009). [sent-360, score-0.48]
94 , (Elson and McKeown, 2009)), automatic affect state analysis (Alm, 2009), and automated learning of scripts (Schank and Abelson, 1977) and other con85 ceptual knowledge structures (e. [sent-363, score-0.491]
95 We showed that affect projection rules can effectively assign affect states to characters. [sent-374, score-1.159]
96 Some aspects of affect state identification are closely related to Hopper and Thompson’s (1980) theory oftransitivity. [sent-376, score-0.491]
97 AESOP produces affect states with an F score of 45%. [sent-381, score-0.73]
98 Identifying positive states appears to be more difficult than negative or mental states. [sent-382, score-0.589]
99 This includes the M affect states that initiate plans, the +/- completion states, as well as their corresponding links. [sent-384, score-0.682]
100 We suspect that the relatively low recall on positive affect states is due to our inability to accurately identify successful plan completions. [sent-385, score-0.816]
wordName wordTfidf (topN-words)
[('affect', 0.394), ('ppvs', 0.354), ('states', 0.288), ('aesop', 0.268), ('plot', 0.219), ('patient', 0.195), ('agent', 0.19), ('ppv', 0.159), ('basilisk', 0.157), ('unit', 0.137), ('evil', 0.136), ('causal', 0.136), ('narrative', 0.13), ('negative', 0.118), ('polarity', 0.118), ('sons', 0.115), ('fables', 0.11), ('father', 0.104), ('positive', 0.1), ('state', 0.097), ('character', 0.096), ('links', 0.094), ('verbs', 0.086), ('mental', 0.083), ('onto', 0.08), ('characters', 0.079), ('plans', 0.075), ('fable', 0.073), ('vp', 0.069), ('link', 0.065), ('verb', 0.063), ('actualization', 0.061), ('bone', 0.061), ('wolf', 0.061), ('fed', 0.061), ('coreference', 0.059), ('projection', 0.057), ('framenet', 0.057), ('units', 0.055), ('bad', 0.055), ('eaten', 0.052), ('exhortations', 0.049), ('subgoal', 0.049), ('produces', 0.048), ('lexicon', 0.046), ('riloff', 0.045), ('agents', 0.043), ('produce', 0.042), ('rescued', 0.042), ('patients', 0.042), ('bootstrapping', 0.042), ('inferred', 0.04), ('kind', 0.039), ('act', 0.039), ('wilson', 0.038), ('gray', 0.038), ('goals', 0.037), ('eagle', 0.037), ('quarreling', 0.037), ('situational', 0.037), ('stereotypically', 0.037), ('teach', 0.037), ('xchar', 0.037), ('neutral', 0.036), ('chambers', 0.035), ('story', 0.034), ('plan', 0.034), ('representations', 0.032), ('desirable', 0.032), ('action', 0.032), ('killed', 0.031), ('resolver', 0.031), ('orientation', 0.031), ('actions', 0.029), ('choi', 0.029), ('conjunctions', 0.029), ('takamura', 0.028), ('harm', 0.028), ('lehnert', 0.028), ('seemed', 0.028), ('stuck', 0.028), ('stories', 0.028), ('sentiment', 0.028), ('rule', 0.026), ('resources', 0.026), ('voice', 0.026), ('baker', 0.026), ('undesirable', 0.026), ('rules', 0.026), ('adopted', 0.024), ('bundle', 0.024), ('chased', 0.024), ('chronologically', 0.024), ('emotion', 0.024), ('fujiki', 0.024), ('hopper', 0.024), ('hospitalized', 0.024), ('impart', 0.024), ('kasch', 0.024), ('mouse', 0.024)]
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