# representing_verbs_as_argument_concepts__b7373e22.pdf

Representing Verbs as Argument Concepts

Yu Gong 1 and Kaiqi Zhao 2 and Kenny Q. Zhu 3 Shanghai Jiao Tong University, Shanghai, China 1gy910210@163.com, 2kaiqi zhao@163.com, 3kzhu@cs.sjtu.edu.cn

Verbs play an important role in the understanding of natural language text. This paper studies the problem of abstracting the subject and object arguments of a verb into a set of noun concepts, known as the argument concepts . This set of concepts, whose size is parameterized, represents the finegrained semantics of a verb. For example, the object of enjoy can be abstracted into time, hobby and event, etc. We present a novel framework to automatically infer human readable and machine computable action concepts with high accuracy.

1 Introduction Verb plays the central role in both syntax and semantics of natural language sentences. The distributional hypothesis (Harris 1954; Miller and Charles 1991) shows that it is possible to represent the meaning of a word by the distributional properties of its context, e.g., its surrounding words in a window. A verb has a unique role in a sentence because it maintains dependency relation with its syntactic arguments such as the subject and the object. Therefore, it is possible to use the distribution of immediate arguments of a verb to represent its meaning, such as Re Verb (Fader, Soderland, and Etzioni 2011). Such an approach is a form of bagof-words (Bo W) approach. The common criticisms of the Bo W approach are i) perceived orthorgonality of all words despite some of them sharing similar or related meanings; ii) its high dimensionality and high cost of computation; and iii) poor readibility to humans. To ameliorate these limitations, a natural solution is to represent the arguments by their abstract types, rather than the words themselves. It is reasonable to assume that a verb represents different meanings, or different senses, if it s used with different types of arguments. To that end, Frame Net (Baker, Fillmore, and Lowe 1998) and Verb Net (Kipper et al. 2000) are examples of human-annotated lexicons that include verbs and their meanings (called frames) and the different types of their arguments (called thematic roles or semantic roles). Due to the excessive cost of constructing such lexicons, as well as their intentional shallow semantic nature, the abstraction of verb arguments

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is very coarse-grained. For example, in Frame Net, the verb eat has just one frame, namely Ingestion , and its direct object has just one role, Ingestibles . Furthermore, the lexical coverage of these resources are very limited. Frame Net, which is the most popular and best maintained among the three, consists of just 3000 verbs and 1200 frames. The Bo W approach is too fine-grained while the semantic role approach is too coarse-grained. In this paper, we seek to strike a balance between these two extremes. Our goal is to automatically infer a tunable set of human-readable and machine-computable abstract concepts for the immediate arguments 1 of each verb from a large text corpus. By tunable , we mean that the granularity of the concepts can be parameterized by the size of the set to be returned. The larger the set, the finer-grained the semantics. The vocabulary of the concepts comes from an existing taxonomy of concepts or terms such as Probase (Wu et al. 2012) or Word Net (Miller and Fellbaum 1998). For instance, the direct object of verb eat may be conceptualized into food , plant and animal . One potential solution toward this goal is selectional preference (SP), originally proposed by Resnik(1996). Classbased SP computes whether a class of terms is a preferred argument to a verb. Together with a taxonomy of concepts, SP can produce a ranked list of classes that are the most appropriate subjects or objects of a verb. However, for the purpose of representing verbs, SP has the following drawback: it doesn t allow the granularity of the concepts to be tuned because it computes a selectional preference score between the verb and every possible concept in the taxonomy. The top k concepts do not necessarily cover all the aspects of that verb because these concepts may semantically overlap each other. Clustering-based SP and LDA-based SP (Ritter, Etzioni, and others 2010) find tunable classes with low overlaps, but the classes are either word clusters or probabilistic distributions of words, which are not abstracted into concepts. Without associating the classes to concepts in taxonomies, the model loses the ability of generalization. For example, if eat Mc Donalds does not appear in the training data, clusteringand LDA-based SP cannot recognize Mc Donalds as a valid argument to eat , since Mc Donalds is

1We only consider subjects and direct objects in this paper, though other arguments may be inferred as well.

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16)

not a member of any inferred clusters or word distributions. In this paper, we first introduce the notion of taxonomy (Section 2) and define the argument conceptualization problem, which asks for k concepts drawn from a taxonomy that generalize as many possible arguments of a verb as possible, and with bounded overlap with each other (Section 3). We present the system to generate tunable argument concepts through a branch-and-bound algorithm (Section 4) and show in experiments that our system can generate high quality human-readable and machine-computable argument concepts (Section 5). Some related work will be discussed (Section 6) before we draw some concluding remarks (Section 7).

We use a taxonomy as the external classification knowledge for conceptualizing the arguments of verbs. A taxonomy is a directed graph (V, E), Here, V is a set of terms, E is a set of binary is A relations

E = {(e, c)|e V, c V, e is A c},

where e is called an entity, c is called a concept, and c is said to cover e. Most terms in V are both concepts and entities; terms with zero outdegree in the graph are entities only. In this paper, we consider two different taxonomies, namely Word Net (Miller and Fellbaum 1998) and Probase (Wu et al. 2012). Word Net organizes words into sets of synonyms (called synsets) along with is A relation between two synsets. Each word may belong to multiple synsets and have multiple hypernyms (concepts) or hyponyms (entities). Probase covers a lot of named entities and multi-word expressions (e.g., Microsoft, Star Wars) which may not be covered by Word Net. This feature allows us to extract more precise arguments.

3 Problem Formulation

We begin with an informal definition of the argument conceptualization problem. Given a collection of argument instances of the same argument type (e.g., object or subject) of a verb, we want to pick k concepts from the taxonomy that subsume as many instances as possible. We would also like these k concepts to have little overlap with each other. The intuition is that each of the k selected concepts represents a unique sense with small semantics overlap and the k concepts collectively cover the majority uses of that verb. We define semantics overlap between two concepts as:

Overlap(c1, c2) = |Ec1 Ec2| min{|Ec1|, |Ec2|},

where Ec is the set of all entities covered by concept c in the taxonomy. Then, we formulate the argument conceptualization problem as a problem of finding maximum weighted k-cliques. Consider a concept graph G = (C, L, W), which has a collection of concepts C in a taxonomy, and a set of edges L in which each edge connects two concepts that have an overlap less than a predefined threshold τ. W stands for weights for

the concepts in the graph. Each weight intuitively represents the quality of the concept with respect to the verb. Figure 1 shows 4 concepts in an illustrative 2-dimensional entity space (a), as well as their corresponding concept graph (b). Each circle ci in (a) represents a set of entities covered by concept ci. Because the overlap between c0 and c3 and between c1 and c3 is high (> τ), (b) is a fully connected graph (clique) minus only two edges: lc0,c3 and lc1,c3.

Figure 1: (a) 4 concepts in the entity space (b) corresponding concept graph

The argument conceptualization problem is then transformed to finding the k-clique with maximum combined weight. A straightforward way to define the weight for each concept is counting the number of argument instances it subsumes according to the is A taxonomy (used as baseline method in Section 5). This assumes that all argument instances of a verb are of equal importance, which is not true in practice. We thus generalize the importance of an argument e to a verb v by a quality function Qv(e), which we will discuss in detail in Section 4.1. Consequently, the weight of concept c for verb v is defined as

e {e|e is A c} Qv(e). (1)

The argument conceptualization problem is to find a kclique (which forms a concept set as Ck) in the graph G which maximizes

c Ck wv(c). (2)

We parameterize the number (k) of argument concepts of a verb because i) different verbs have different number of senses; and ii) even for the same verb, there is no agreement on the exact number of its senses because one meaning can always be divided into a number of finer-grain meanings. For example, in Oxford English Dictionary (Oxford University Press 2015), the transitive verb eat has 4 senses (or definitions), while in Cambridge Dictionary (Cambridge Dictionaries Online 2015) it has just one meaning.

4 Framework Our framework consists of three main steps: argument extraction, argument weight computation and argument conceptualization. In the argument extraction component, we

extract the arguments of the verb from a dependency parsed sentence by several dependency relations ( nsubj , agent for subject extraction and nsubjpass , dobj for object extraction). In the argument weight computation component, we pre-compute the weight for each argument instance (see Section 4.1). In the argument conceptualization, we build the concept graph and use a branch-and-bound algorithm (see Section 4.2) to solve the argument conceptualization problem.

4.1 Argument Weight Computation Since many of the existing dependency parser systems are noisy (Manning et al. 2014). Our observations showed that some errors follow certain patterns. For example, food in food to eat is usually incorrectly labeled as the subject of eat , and the same goes for water to drink , game to play , etc. Similarly, time in play this time and play next time is incorrectly labeled as the object of play . We also discovered that if an argument is incorrect due to parsing, it is often extracted from just a couple of patterns. Conversely, if an argument is correct for the verb, it probably appears under many different patterns. Consider corn as an object of verb eat . It appears in 142 patterns, e.g., eat corn , eat expensive corn , eat not only corn , etc., each of which gives a different dependency structure. However, habit only appears in 42 patterns like eating habit . We follow this observation and assume that correct arguments generally are likely to appear in more patterns than the wrong ones. We define a pattern as a subtree in the dependency tree according to two rules: The argument and one of its children form a pattern:

{POSarg, DEParg, POSchild, DEPchild},

where POS and DEP stand for POS tag and dependency type, respectively. The argument and its siblings form another pattern:

{POSarg, DEParg, POSsib, DEPsib}.

For each argument e of verb v, we collect the set of its patterns Me,v, and use the entropy to measure the correctness, where a higher entropy value means that the argument is more informative w.r.t. the patterns, and hence more likely to be a valid argument. The entropy is defined as:

Entropyv(e) =

m Me,v P(m) log P(m) (3)

Moreover, even if an argument is valid under a verb, it may be less relevant. For example, while fruit is highly relevant to eat , thing is not because it can be the object of many other verbs. To this end, we use a binary version of mutual information to measure the relatedness between two terms. The mutual information MIv(e) is defined as:

1 if p(v, e) log p(v,e) p(v)p(e) > 0, 1 otherwise. (4)

In essence, the entropy measures the correctness of the argument, while mutual information measures its correlation

with the verb. We compute the quality of an argument by combining these two measures:

Qv(e) = Entropyv(e) MIv(e). (5)

4.2 A Branch-and-Bound Algorithm Because the concept space in a general-purpose taxonomy is large, we propose a branch-and-bound algorithm to efficiently search for the solution. The details of our algorithm are shown in Algorithm 1. We model each solution as a binary vector of size |C| (C is the set of all concepts in the taxonomy) in which exactly k elements of the vector are set to 1 while others are set to 0. The search space is represented by a binary decision tree where the nodes at each level indicate the decision to include a concept in the solution or not. The complete search space contains 2|C| nodes. Take the concept graph in Figure 1 as an example. The corresponding search space is shown in Figure 2, in which di = 1 means to include ci in the solution, and di = 0 means otherwise. For k = 3, the concept set {c0, c1, c2} is a valid solution, which is marked by the path (d0 = 1) (d1 = 1) (d2 = 1). The key insight in this algorithm is that, even though the search space is exponential, a subtree can be pruned if its path from the root already contains a valid solution, or if the current path doesn t have the potential to produce a better solution than the current best.

Figure 2: A Snapshot of the Binary Decision Tree with k = 3

Suppose the partial solution of the first i levels in the tree are (d0, d1, ..., di 1) and the current best solution has a score (computed by Eq. (2)). We use dmax and πmax to store the best solution and its score found thus far; and use d and πc to represent the current partial solution and its partial score. Variable ck stands for the number of concepts that have been set to 1 in the current decision path, i.e.,

The main function BB(i) searches through the tree in a depth-first manner. It returns when it reaches the leaf node (Line 11-12) or when it has found a solution (Line 13-16). If the solution is better than the current best, the current best solution is updated. The function traverses one more level to include concept ci (Line 17-19) if it forms a clique with the currently chosen concepts (ISCLIQUE function) and if the maximum possible score with ci is better than the current best score (BOUND function).

Algorithm 1 Argument Conceptualization

1: function AC(W, C, L, k) 2: {c0, ..., c|C| 1} Sort concepts c C in the descending order of wv(c). 3: πmax 0, πc 0, ck 0 4: dmax {0, ..., 0}, d {0, ..., 0} 5: BB(0) 6: if ck = k then 7: return dmax 8: else 9: No solution

10: function BB(i) 11: if i |C| then 12: return 13: if ck = k then 14: if πc > πmax then 15: πmax πc, dmax d 16: return 17: if ISCLIQUE(L, i) = TRUE and BOUND(i)> πmax then 18: ck ck + 1, πc πc + wsv(ci), di 1 19: BB(i + 1) 20: ck ck 1, πc πc wsv(ci), di 0 21: if BOUND(i + 1) > πmax then 22: di 0 23: BB(i + 1) 24: return

25: function ISCLIQUE(L, i) 26: for j from 0 to i 1 do 27: if dj = 1 then 28: if (ci, cj) L and (cj, ci) L then 29: return FALSE 30: return TRUE

31: function BOUND(i) 32: b πc 33: for j from i to min{i + k ck 1, |C| 1} do 34: b b + wsv(cj) 35: return b

A crucial optimization in this algorithm is that we first sort all concepts in C in the descending order of their weighted scores (Line 2). This allows us to quickly compute the bound (Line 33-34) in linear time (against k), i.e., simply compute the total score of the next k ck concepts down the decision tree hierarchy, rather than sorting all the remaining concepts.

5 Experimental Results

In this section, we first show how we prepare the data for argument conceptualization. Then, we use some example concepts generated by our algorithm to show the advantage of our algorithm (AC) against selectional preference (SP), Frame Net (Baker, Fillmore, and Lowe 1998) and Re Verb (Fader, Soderland, and Etzioni 2011), as well as our baseline approach (BL) which considers equal weight for each argument (see Section 3). We also quantitatively evaluate the accuracies of AC, BL and SP on Probase. Finally, we

apply our algorithm to an NLP task known as argument identification (Gildea and Palmer 2002; Abend, Reichart, and Rappoport 2009; Meza-Ruiz and Riedel 2009) and show that concepts generated by AC achieve better accuracy against BL, SP, Reverb and a state-of-the-art semantic role labeling tool (using Frame Net) on both taxonomies.

5.1 Experimental Setup We use our algorithm to conceptualize subjects and objects for 1770 common verbs from Google syntactic Ngram (Goldberg and Orwant 2013; Google 2013) using Probase and Word Net as is A taxonomies. 2 From 1770 verb set, we sample 100 verbs with probability proportional to the frequency of the verb. This set of 100 verbs (Verb-100) is used for quantitative experiments including evaluating the accuracy of argument concepts and the accuracy of argument identification. All argument instances we use in this work come from Verbargs and Triarcs packages of the N-gram data. From the labeled dependency trees, we extract subject-verb dependency pairs (nsubj, agent) and object-verb dependency pairs (dobj, nsubjpass). We expand the subject or object, which is a word, into a phrase recognizable by Probase/Word Net by sliding a window across the subtree rooted at the argument word. For the system parameters, we set the maximum overlap threshold between two concepts to 0.2, and the number of concepts k to {5, 10, 15} to evaluate argument concepts of different granularity. In practice, the number k can be set differently for different verbs, which we view as an advantage of the framework.

5.2 Conceptualization Results We compare the concepts learned by AC with the concepts learned by BL, Frame Net elements, Reverb arguments, and concepts learned by SP. Re Verb is an open information extraction system that discovers binary relations3 from the web without using any predefined lexicon. Re Verb data contains 15 million subject-predicate-object triple instances without any abstraction or generalization. Table 1 shows 3 example verbs and their argument concepts (AC & BL), Frame Net semantic roles (FN), Re Verb argument instances (RV) as well as selectional preference (SP) concepts for the verbs subjects and objects. The number of concepts k is set to 5 for AC & BL, and the top 5 instances/concepts are showed for RV and SP. We can observe that the semantic roles in FN are too general, while RV instances are too specific. Both inevitably lose information: FN is a manually constructed lexicon by experts thus cannot scale up well, while Re Verb is automatically extracted from massive English sentences and hence comes with abundant errors (e.g., ive as a subject of enjoy ). SP does not consider semantic overlaps between argument concepts. BL assumes that all argument instances of a verb are of equal

2All evaluation data sets and results are available at http://adapt. seiee.sjtu.edu.cn/ac. 3Re Verb extracts general relations instead of verb predicates, e.g., XXX heavily depends on YYY.

Table 1: Example subject/object concepts from 5 lexicons Verb AC Concepts BL Concepts Frame Net Re Verb SP Concepts

person, community, institution, player, company

topic, name, group, feature, product

Recipient, Speaker, Interlocutor

Student, an article, the paper, Web browser, Applications

world, group, person, term, safe payment method

document, payment, practice, doctrine, theory

factor, feature, product, activity, person

Theme, Proposal

the program, publication, HTTP cookie, the year, credit card

topic, concept, matter, issue, word

group, community, name, country, sector

name, topic, group, feature, product

Experiencer people, ive, Guests, everyone, someone

world, stakeholder, group, person, actor

benefit, time, hobby, social event, attraction

factor, activity, feature, product, person

Stimulus life, Blog, Breakfirst, their weekend, a drink

benefit, issue, advantage, factor, quality

group, community, name, term, source

topic, name, group, feature, product

no reviews, Project, other destinations, HTML, COMMENTS

large number, number, stakeholder, position, group

document, format, task, procedure, law

factor, feature, activity, product, person

one, review, a profile, text, your visit dates

document, esi online tool, material, nickname, first name

importance, which is not true in practice. It tends to generate uninformative concepts such as factor and feature . Compared to the other methods, AC generates concepts with tunable granularity and low semantic overlap. These concepts are more comprehensive and more accurate. To quantitatively compare our algorithm to BL and SP, we ask three native English speakers to annotate whether the concepts generated by AC, BL and SP are the correct abstraction of the verb s arguments. The majority votes are used as the ground truth. We compute the percentage of correct concepts as accuracy, and report the accuracy of AC, BL and SP in Table 2. AC generates more accurate concepts than BL and SP mainly because AC considers the quality of argument instances extracted from dependency and the semantic overlap between concepts. BL performs worse than SP because the noise caused by parsing error is not considered, but SP considers the association between the verb and arguments which implicitly gives a low rank to the incorrect arguments.

Table 2: Accuracy of AC, BL and SP concepts

k Subject Object AC BL SP AC BL SP

5 0.88 0.49 0.58 0.97 0.63 0.62 10 0.86 0.47 0.56 0.94 0.61 0.65 15 0.85 0.43 0.58 0.91 0.60 0.66

5.3 Argument Identification In the argument identification task, we use the inferred argument concepts to examine whether a term is a correct argument to a verb in a sentence. To evaluate the accuracy of argument identification, for each verb in Verb-100, we first extract and randomly select 100 sentences containing the verb from the Engish Wikipedia corpus. We then extract verb, obj and verb, subj pairs from these 10,000 sentences. Apart from parsing errors, most of these pairs

are correct because Wikipedia articles are of relatively high quality. We roughly swap the subjects/objects from half of these pairs with the subject/object of a different verb, effectively creating incorrect pairs as negative examples. For example, if we exchange clothing in wear clothing with the piano in play piano , we get two negative examples wear piano and play clothing . Finally, we manually label each of the 20,000 pairs to be correct or not, in the context of the original sentences. As a result, we have a test set of 10,000 verb, obj and verb, subj pairs in which roughly 50% are positive and the rest are negative. We compare AC with BL, SP, Re Verb and Semantic Role Labeling (SRL) as follows: AC & BL & SP: Check if the test term belongs to any of the k argument concepts (is A relation) of the target verb. Re Verb: Check if the test term is contained by the verb s list of subjects or objects in Re Verb. SRL: SRL aims at identifying the semantic arguments of a predicate in a sentence, and classifying them into different semantic roles. We use Semafor (Chen et al. 2010), a well-known SRL tool, to label semantic arguments with Frame Net in the sentences, and check if the test term is recognized as a semantic argument of the target verb.

Table 3: Accuracy of argument identification

k Probase Word Net

RV SRL AC BL SP AC BL SP

5 0.81 0.50 0.70 0.55 0.54 0.54

0.48 10 0.78 0.50 0.72 0.57 0.54 0.55 15 0.77 0.49 0.72 0.58 0.54 0.56

5 0.62 0.51 0.58 0.50 0.46 0.50

0.50 10 0.62 0.52 0.58 0.52 0.47 0.52 15 0.62 0.52 0.59 0.53 0.47 0.52

We set k = 5, 10, 15 for AC, BL and SP. The accuracies are shown in Table 3. From Table 3, we observe that

the accuracy of AC is higher than that of BL, SP, Re Verb and SRL. Due to its limited scale, Re Verb cannot recognize many argument instances in the test data, and thus often labels true arguments as negative. SRL, on the opposite side, tends to label everything as positive because the SRL classifier is trained based on features extracted from the context, which remains the same even though we exchange the arguments. Thus, SRL still labels the argument as positive. Comparing with BL and SP, AC considers the coverage of verb arguments, the parsing errors and overlap of concepts to give an optimal solution with different values of k. Consequently, our algorithm generates a set of concepts which cover more precise and diversed verb argument instances. The accuracy decreases when we use Word Net as the taxonomy because Word Net covers 84.82% arguments in the test data while Probase covers 91.69%. Since arguments that are not covered by the taxonomy will be labeled as incorrect by both methods, the advantage of our algorithm is reduced.

6 Related Work We first review previous work on selectional preference, which can be seen as an alternate way of producing abstract concepts for verb arguments, then discuss some known work on semantic representation of verbs.

6.1 Selectional Preference The most related work to our problem (AC) is selectional preferences (SP), which aims at computing preferences over the classes of arguments by a verb, given the fact that some arguments are more suitable for certain verbs than others. For example, drink water is more plausible than drink desk . While our problem defines a controllable level of abstraction for verb arguments, selectional preference often outputs the preference scores for all possible classes of arguments. Moreover, SP doesn t consider the overlap between classes, which results in highly similar classes/concepts. There are several approaches to computing SP. The original class-based approaches generalize arguments extracted from corpus to human readable concepts using a taxonomy such as Word Net. The most representative of such approach was proposed by Resnik (1996), which is used as a comparison in this paper. Instead of Word Net, Pantel et al.(2003) proposed a clustering method (named CBC) to automatically generate semantic classes, which are nonetheless not human readable. Another recent piece of work about SP from Fei et al.(2015) doesn t abstract the arguments, and is thus different from our approach. Other approaches including cutbased SP (Li and Abe 1998), similarity-based SP (Clark and Weir 2001; Erk 2007), and generative model-based SP (Ritter, Etzioni, and others 2010) are less relevant to our problem, because they cannot generate human readable classes.

6.2 Semantic Representation of Verbs From past literature, the semantics of a word (including verbs) can be represented by the context it appears in (Mikolov et al. 2013a; 2013b; Mikolov, Yih, and Zweig 2013; Levy and Goldberg 2014). There are a number of ways to define the context. The simpliest is by the words

from a surrounding window. A slightly different type of context takes advantage of structural information in the corpus, e.g., Wikipedia. The third type of context comes from a knowledge base or lexicon, such as Word Net. For a verb, the gloss, its hypernyms, hyponyms, antonyms and synonyms can be used as its context (Meyer and Gurevych 2012; Yang and Powers ). Finally, most recently, the dependency structure surrounding the verb in a sentence has been used as its context (Levy and Goldberg 2014). This is also the approach adopted in this paper. With different types of context, a common way to represent a verb is by a vector of distributional properties, extracted from the contexts within a large corpus. For example, LSA (Deerwester et al. 1990) uses the window of words as context, while ESA (Gabrilovich and Markovitch 2007) uses the TF-IDF score of the word w.r.t. the article it appears in to form a vector of Wikipedia concepts. Another popular approach is to map the word distribution in the context into another high-dimensional space, which is known as word embedding (Mikolov et al. 2013b). Our approach can be thought of as mapping the words in the context, in this case, the subject and object arguments into a hierarchical concept space.

7 Conclusion

We developed a data-driven approach that automatically infers a set of argument concepts for a verb by abstracting from a large number of argument instances parsed from raw text. These argument concepts are human-readable and machinecomputable, and can be used to represent the meaning of the verb. Our evaluation demonstrates that the concepts inferred are accurate to the human judges and show good potential at identifying correct arguments for verbs even though such arguments have never been seen before. This work can also be seen as mining predicate relations between abstract noun concepts from a taxonomy. As future work, one may consider other important NLP tasks such as word sense disambiguation or term similarity using the argument concept representation of verbs.

Acknowledgments

Kenny Q. Zhu is the corresponding author, and is partially supported by the 2013 Google Faculty Research Award. Zhiyuan Cai and Youer Pu contributed to the earlier version of this paper. We thank Dr. Haixun Wang and the anonymous reviewers for their valuable comments.

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