Verb plays the central role in both syntax and semantics of natural language sentences. The distributional hypothesis [Harris1954, Miller and Charles1991] 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 ReVerb [Fader, Soderland, and Etzioni2011]. Such an approach is a form of “bag-of-words” (BoW) approach. The common criticisms of the BoW 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, FrameNet [Baker, Fillmore, and Lowe1998] and VerbNet [Kipper et al.2000]
are examples of human-annotated lexicons that include verbs and their meanings (calledframes) 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 is very coarse-grained. For example, in FrameNet, 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. FrameNet, which is the most popular and best maintained among the three, consists of just 3000 verbs and 1200 frames.
The BoW 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 111We only consider subjects and direct objects in this paper, though other arguments may be inferred as well. 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 WordNet [Miller and Fellbaum1998]. 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 Resnikresnik1996selectional. Class-based 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 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 others2010] 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 McDonalds” does not appear in the training data, clustering- and LDA-based SP cannot recognize “McDonalds” as a valid argument to “eat”, since “McDonalds” is 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 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 , Here, is a set of terms, is a set of binary “isA” relations
where is called an entity, is called a concept, and is said to cover . Most terms in are both concepts and entities; terms with zero outdegree in the graph are entities only. In this paper, we consider two different taxonomies, namely WordNet [Miller and Fellbaum1998] and Probase [Wu et al.2012]. WordNet organizes words into sets of synonyms (called synsets) along with “isA” 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 WordNet. 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 concepts from the taxonomy that subsume as many instances as possible. We would also like these concepts to have little overlap with each other. The intuition is that each of the selected concepts represents a unique sense with small semantics overlap and the concepts collectively cover the majority uses of that verb.
We define semantics overlap between two concepts as:
where is the set of all entities covered by concept in the taxonomy.
Then, we formulate the argument conceptualization problem as a problem of finding maximum weighted -cliques. Consider a concept graph , which has a collection of concepts in a taxonomy, and a set of edges in which each edge connects two concepts that have an overlap less than a predefined threshold . 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 in (a) represents a set of entities covered by concept . Because the overlap between and and between and is high (), (b) is a fully connected graph (clique) minus only two edges: and .
The argument conceptualization problem is then transformed to finding the -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 isA 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 to a verb by a quality function , which we will discuss in detail in Section 4.1. Consequently, the weight of concept for verb is defined as
The argument conceptualization problem is to find a -clique (which forms a concept set as ) in the graph which maximizes
We parameterize the number () 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 Press2015], the transitive verb “eat” has 4 senses (or definitions), while in Cambridge Dictionary [Cambridge Dictionaries Online2015] it has just one meaning.
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:
where and stand for POS tag and dependency type, respectively.
The argument and its siblings form another pattern:
For each argument of verb , we collect the set of its patterns , 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:
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 is defined as:
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:
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( is the set of all concepts in the taxonomy) in which exactly
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 containsnodes. Take the concept graph in Figure 1 as an example. The corresponding search space is shown in Figure 2, in which means to include in the solution, and means otherwise. For , the concept set is a valid solution, which is marked by the path . 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.
Suppose the partial solution of the first levels in the tree are and the current best solution has a score (computed by Eq. (2)). We use and to store the best solution and its score found thus far; and use and to represent the current partial solution and its partial score. Variable stands for the number of concepts that have been set to 1 in the current decision path, i.e.,
The main function BB() 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 (Line 17-19) if it forms a clique with the currently chosen concepts (ISCLIQUE function) and if the maximum possible score with is better than the current best score (BOUND function).
A crucial optimization in this algorithm is that we first sort all concepts in 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 ), i.e., simply compute the total score of the next 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), FrameNet [Baker, Fillmore, and Lowe1998] and ReVerb [Fader, Soderland, and Etzioni2011], 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 Palmer2002, Abend, Reichart, and Rappoport2009, Meza-Ruiz and Riedel2009] 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 FrameNet) on both taxonomies.
5.1 Experimental Setup
We use our algorithm to conceptualize subjects and objects for 1770 common verbs from Google syntactic N-gram[Goldberg and Orwant2013, Google2013] using Probase and WordNet as isA taxonomies. 222All evaluation data sets and results are available at http://adapt.seiee.sjtu.edu.cn/ac. 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/WordNet 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 to to evaluate argument concepts of different granularity. In practice, the number 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, FrameNet elements, Reverb arguments, and concepts learned by SP. ReVerb is an open information extraction system that discovers binary relations333ReVerb extracts general relations instead of verb predicates, e.g., XXX heavily depends on YYY. from the web without using any predefined lexicon. ReVerb data contains 15 million subject-predicate-object triple instances without any abstraction or generalization.
|Verb||AC Concepts||BL Concepts||FrameNet||ReVerb||SP Concepts|
Table 1 shows 3 example verbs and their argument concepts (AC & BL), FrameNet semantic roles (FN), ReVerb argument instances (RV) as well as selectional preference (SP) concepts for the verbs’ subjects and objects. The number of concepts 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 ReVerb 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 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.
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, ReVerb and Semantic Role Labeling (SRL) as follows:
AC & BL & SP: Check if the test term belongs to any of the argument concepts (isA relation) of the target verb.
ReVerb: Check if the test term is contained by the verb’s list of subjects or objects in ReVerb.
: 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 FrameNet in the sentences, and check if the test term is recognized as a semantic argument of the target verb.
, we observe that the accuracy of AC is higher than that of BL, SP, ReVerb and SRL. Due to its limited scale, ReVerb 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. Consequently, our algorithm generates a set of concepts which cover more precise and diversed verb argument instances. The accuracy decreases when we use WordNet as the taxonomy because WordNet 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 WordNet. The most representative of such approach was proposed by Resnik resnik1996selectional, which is used as a comparison in this paper. Instead of WordNet, Pantel et al.pantel2003clustering 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.fei2015illinois doesn’t abstract the arguments, and is thus different from our approach. Other approaches including cut-based SP [Li and Abe1998], similarity-based SP [Clark and Weir2001, Erk2007], and generative model-based SP [Ritter, Etzioni, and others2010] 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, Mikolov et al.2013b, Mikolov, Yih, and Zweig2013, Levy and Goldberg2014]. 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 WordNet. For a verb, the gloss, its hypernyms, hyponyms, antonyms and synonyms can be used as its context [Meyer and Gurevych2012, Yang and Powers]. Finally, most recently, the dependency structure surrounding the verb in a sentence has been used as its context [Levy and Goldberg2014]. 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 Markovitch2007] 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.
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 machine-computable, 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.
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.
- [Abend, Reichart, and Rappoport2009] Abend, O.; Reichart, R.; and Rappoport, A. 2009. Unsupervised argument identification for semantic role labeling. In ACL/IJCNLP’09, 28–36. Association for Computational Linguistics.
- [Baker, Fillmore, and Lowe1998] Baker, C. F.; Fillmore, C. J.; and Lowe, J. B. 1998. The berkeley framenet project. In Proceedings of the 17th international conference on Computational linguistics-Volume 1, 86–90. Association for Computational Linguistics.
- [Cambridge Dictionaries Online2015] Cambridge Dictionaries Online. 2015. eat definition, meaning - what is eat in the Cambridge British English Dictionary. http://dictionary.cambridge.org/dictionary/british/eat.
- [Chen et al.2010] Chen, D.; Schneider, N.; Das, D.; and Smith, N. A. 2010. SEMAFOR: Frame argument resolution with log-linear models. In Proceedings of the 5th international workshop on semantic evaluation, 264–267. Association for Computational Linguistics.
[Clark and Weir2001]
Clark, S., and Weir, D.
Class-based probability estimation using a semantic hierarchy.In NAACL’01, 1–8. Association for Computational Linguistics.
- [Deerwester et al.1990] Deerwester, S. C.; Dumais, S. T.; Landauer, T. K.; Furnas, G. W.; and Harshman, R. A. 1990. Indexing by latent semantic analysis. JAsIs 41(6):391–407.
- [Erk2007] Erk, K. 2007. A simple, similarity-based model for selectional preferences. In ACL’07, 216–223.
- [Fader, Soderland, and Etzioni2011] Fader, A.; Soderland, S.; and Etzioni, O. 2011. Identifying relations for open information extraction. In EMNLP’11, 1535–1545. Association for Computational Linguistics.
- [Fei et al.2015] Fei, Z.; Khashabi, D.; Peng, H.; Wu, H.; and Roth, D. 2015. Illinois-profiler: Knowledge schemas at scale.
- [Gabrilovich and Markovitch2007] Gabrilovich, E., and Markovitch, S. 2007. Computing semantic relatedness using wikipedia-based explicit semantic analysis. In IJCAI, volume 7, 1606–1611.
- [Gildea and Palmer2002] Gildea, D., and Palmer, M. 2002. The necessity of parsing for predicate argument recognition. In ACL’02, 239–246. Association for Computational Linguistics.
- [Goldberg and Orwant2013] Goldberg, Y., and Orwant, J. 2013. A dataset of syntactic-ngrams over time from a very large corpus of english books. In Second Joint Conference on Lexical and Computational Semantics (* SEM), volume 1, 241–247.
- [Google2013] Google. 2013. Google syntactic n-gram. http://commondatastorage.googleapis.com/books/syntactic-ngrams/index.html.
- [Harris1954] Harris, Z. S. 1954. Distributional structure. Word.
- [Kipper et al.2000] Kipper, K.; Dang, H. T.; Palmer, M.; et al. 2000. Class-based construction of a verb lexicon. In AAAI/IAAI, 691–696.
- [Levy and Goldberg2014] Levy, O., and Goldberg, Y. 2014. Dependencybased word embeddings. In ACL’14, volume 2, 302–308.
- [Li and Abe1998] Li, H., and Abe, N. 1998. Generalizing case frames using a thesaurus and the MDL principle. Computational linguistics 24(2):217–244.
[Manning et al.2014]
Manning, C. D.; Surdeanu, M.; Bauer, J.; Finkel, J.; Bethard, S. J.; and
The Stanford CoreNLP natural language processing toolkit.In ACL’14, 55–60.
- [Meyer and Gurevych2012] Meyer, C. M., and Gurevych, I. 2012. To exhibit is not to loiter: A multilingual, sense-disambiguated wiktionary for measuring verb similarity. In COLING, 1763–1780.
- [Meza-Ruiz and Riedel2009] Meza-Ruiz, I., and Riedel, S. 2009. Jointly identifying predicates, arguments and senses using markov logic. In NAACL’09, 155–163. Association for Computational Linguistics.
- [Mikolov et al.2013a] Mikolov, T.; Chen, K.; Corrado, G.; and Dean, J. 2013a. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
- [Mikolov et al.2013b] Mikolov, T.; Sutskever, I.; Chen, K.; Corrado, G. S.; and Dean, J. 2013b. Distributed representations of words and phrases and their compositionality. In NIPS’13, 3111–3119.
- [Mikolov, Yih, and Zweig2013] Mikolov, T.; Yih, W.-t.; and Zweig, G. 2013. Linguistic regularities in continuous space word representations. In HLT-NAACL, 746–751.
- [Miller and Charles1991] Miller, G. A., and Charles, W. G. 1991. Contextual correlates of semantic similarity. Language and cognitive processes 6(1):1–28.
- [Miller and Fellbaum1998] Miller, G., and Fellbaum, C. 1998. Wordnet: An electronic lexical database.
- [Oxford University Press2015] Oxford University Press. 2015. eat - definition of eat in English from the Oxford dictionary. http://www.oxforddictionaries.com/definition/english/eat.
- [Pantel2003] Pantel, P. A. 2003. Clustering by committee. Ph.D. Dissertation, Citeseer.
- [Resnik1996] Resnik, P. 1996. Selectional constraints: An information-theoretic model and its computational realization. Cognition 61(1):127–159.
- [Ritter, Etzioni, and others2010] Ritter, A.; Etzioni, O.; et al. 2010. A latent dirichlet allocation method for selectional preferences. In ACL’10, 424–434. Association for Computational Linguistics.
- [Wu et al.2012] Wu, W.; Li, H.; Wang, H.; and Zhu, K. Q. 2012. Probase: A probabilistic taxonomy for text understanding. In SIGMOD’12, 481–492. ACM.
- [Yang and Powers] Yang, D., and Powers, D. M. Verb similarity on the taxonomy of WordNet. Citeseer.