English prepositions exhibit stunning frequency and wicked polysemy. In the 450M-word COCA corpus (Davies, 2010), 11 prepositions are more frequent than the most frequent noun.111http://www.wordfrequency.info/free.asp?s=y In the corpus presented in this paper, prepositions account for 8.5% of tokens (the top 11 prepositions comprise >6% of all tokens). Far from being vacuous grammatical formalities, prepositions serve as essential linkers of meaning, and the few extremely frequent ones are exploited for many different functions (figure 1). For all their importance, however, prepositions have received relatively little attention in computational semantics, and the community has not yet arrived at a comprehensive and reliable scheme for annotating the semantics prepositions in context (section 2). We believe that such annotation of preposition functions is needed if preposition sense disambiguation systems are to be useful for downstream tasks—e.g., translation222This work focuses on English, but adposition and case systems vary considerably between languages, challenging second language learners and machine translation systems (Chodorow et al., 2007; Shilon et al., 2012; Hashemi and Hwa, 2014). or semantic parsing (cf. Dahlmeier et al., 2009; Srikumar and Roth, 2011).
This paper describes a new corpus fully annotated with preposition supersenses (hierarchically organized unlexicalized classes). We note that none of the existing English corpora annotated with preposition semantics, on which existing disambiguation models have been trained and evaluated, are both comprehensive (describing all preposition types and tokens) and double-annotated
(to attenuate subjectivity in the annotation scheme and measure inter-annotator agreement). As an alternative to fine-grained sense annotation for individual prepositions—which is difficult and limited by the coverage and quality of a lexicon—we instead train human annotators to labelpreposition supersenses, reporting the first inter-annotator agreement figures for this task. We comprehensively annotate English preposition tokens in a corpus of web reviews and examine the distribution of their supersenses, and improve upon the supersense hierarchy as necessitated by the data encountered during the annotation process. Our annotated corpus will be publicly released at the time of publication.
2 Background and Motivation
Theoretical linguists have puzzled over questions such as how individual prepositions can acquire such a broad range of meanings—and to what extent those meanings are systematically related (e.g., Brugman, 1981; Lakoff, 1987; Tyler and Evans, 2003; O’Dowd, 1998; Saint-Dizier and Ide, 2006; Lindstromberg, 2010).
Prepositional polysemy has also been recognized as a challenge for AI (Herskovits, 1986)
and natural language processing, motivating semantic disambiguation systems(O’Hara and Wiebe, 2003; Ye and Baldwin, 2007; Hovy et al., 2010; Srikumar and Roth, 2013b). Training and evaluating these requires semantically annotated corpus data. Below, we comment briefly on existing resources and why (in our view) a new resource is needed to “road-test” an alternative, hopefully more scalable, semantic representation for prepositions.
2.1 Existing Preposition Corpora
Beginning with the seminal resources from The Preposition Project (TPP; Litkowski and Hargraves, 2005), the computational study of preposition semantics has been fundamentally grounded in corpus-based lexicography centered around individual preposition types. Most previous datasets of preposition semantics at the token level (Litkowski and Hargraves, 2005, 2007; Dahlmeier et al., 2009; Tratz and Hovy, 2009; Srikumar and Roth, 2013a) only cover high-frequency prepositions (the 34 represented in the SemEval-2007 shared task based on TPP, or a subset thereof).333A further limitation of the SemEval-2007 dataset is the way in which it was sampled: illustrative tokens from a corpus were manually selected by a lexicographer. As (Litkowski, 2014) showed, a disambiguation system trained on this dataset will therefore be biased and perform poorly on an ecologically valid sample of tokens.
We sought a scheme that would facilitate comprehensive semantic annotation of all preposition tokens in a corpus: thus, it would have to cover the full range of usages possible for the full range of English preposition types. The recent TPP PDEP corpus (Litkowski, 2014, 2015) comes closer to this goal, as it consists of randomly sampled tokens for over 300 types. However, sentences were sampled separately for each preposition, so there is only one annotated preposition token per sentence. By contrast, we will fully annotate documents for all preposition tokens. No inter-annotator agreement figures have been reported for the PDEP data to indicate its quality, or the overall difficulty of token annotation with TPP senses across a broad range of prepositions.
From the literature on other kinds of supersenses, there is reason to believe that token annotation with preposition supersenses (Schneider et al., 2015) will be more scalable and useful than senses. The term supersense has been applied to lexical semantic classes that label a large number of word types (i.e., they are unlexicalized). The best-known supersense scheme draws on two inventories—one for nouns and one for verbs—which originated as a high-level partitioning of senses in WordNet (Miller et al., 1990). A scheme for adjectives has been proposed as well (Tsvetkov et al., 2014).
One argument advanced in favor of supersenses is that they provide a coarse level of generalization for essential contextual distinctions—such as artifact vs. person for chair, or temporal vs. locative in—without being so fine-grained that systems cannot learn them (Ciaramita and Altun, 2006). A similar argument applies for human learning as pertains to rapid, cost-effective, and open-vocabulary annotation of corpora: an inventory of dozens of categories (with mnemonic names) can be learned and applied to unlimited vocabulary without having to refer to dictionary definitions (Schneider et al., 2012). Like with WordNet for nouns and verbs, the same argument holds for prepositions: TPP-style sense annotation requires familiarity with a different set of (often highly nuanced) distinctions for each preposition type. For example, in has 15 different TPP senses, among them in 10(7a) ‘indicating the key in which a piece of music is written: Mozart’s Piano Concerto in E flat’.
Supersenses have been exploited for a variety of tasks (e.g., Agirre et al., 2008; Tsvetkov et al., 2013, 2015), and full-sentence noun and verb taggers have been built for several languages (Segond et al., 1997; Johannsen et al., 2014; Picca et al., 2008; Martínez Alonso et al., 2015; Schneider et al., 2013, 2016). They are typically implemented as sequence taggers. In the present work, we extend a corpus that has already been hand-annotated with noun and verb supersenses, thus raising the possibility of systems that can learn all three kinds of supersenses jointly (cf. Srikumar and Roth, 2013b).
Schneider et al.’s (2015) preposition supersense scheme is described in detail in a lexical resource, PrepWiki,444http://tiny.cc/prepwiki which records associations between supersenses and preposition types. Hereafter, we adopt the term usage for a pairing of a preposition type and a supersense label—e.g., at/Time. Usages are organized in PrepWiki via (lexicalized) senses from the TPP lexicon. The mapping is many-to-many, as senses and supersenses capture different generalizations. (TPP senses, being lexicalized, are more numerous and generally finer-grained, but in some cases lump together functions that receive different supersenses, as in the sense for 2(2) ‘affecting, with regard to, or in respect of’.) Thus, for a given preposition, a sense may be mapped to multiple usages, and vice versa.
2.4 The Supersense Hierarchy
Of the four supersense schemes mentioned above, Schneider et al.’s (2015) inventory for prepositions (which improved upon the inventory of Srikumar and Roth (2013a)) is unique in being hierarchical. It is an inheritance hierarchy (see figure 2): characteristics of higher-level categories are asserted to apply to their descendants. Multiple inheritance is used for cases of overlap: e.g., Destination inherits from both Location (because a destination is a point in physical space) and Goal (it is the endpoint of a concrete or abstract path).
The structure of the hierarchy was modeled after VerbNet’s hierarchy of thematic roles (Bonial et al., 2011; Hwang, 2014). But there are many additional categories: some are refinements of the VerbNet roles (e.g., subclasses of Time), while others have no VerbNet counterpart because they do not pertain to core roles of verbs. The Configuration subhierarchy, which is used for of and other prepositions when they relate two nominals, is a good example.
3 Corpus Annotation
3.1 Annotating Preposition Supersenses
We fully annotated the Reviews section of the English Web Treebank (Bies et al., 2012), selected because it had previously been annotated for multiword expressions and noun and verb supersenses (Schneider et al., 2014; Schneider and Smith, 2015). The corpus consists of 55,579 tokens organized into 3812 sentences and 723 documents, with gold tokenization and PTB-style POS tags.
Identifying preposition tokens.
TPP, and therefore PrepWiki, contains senses for canonical prepositions, i.e., those used transitively in the [ P NP] construction. Taking inspiration from Pullum and Huddleston (2002), PrepWiki further assigns supersenses to spatiotemporal particle uses of out, up, away, together, etc., and subordinating uses of as, after, in, with, etc. (including infinitival to and infinitival-subject for, as in It took over 1.5 hours for our food to come out).555PrepWiki does not include subordinators/complementizers that cannot take NP complements: that, because, while, if, etc.
These are used where the heuristics fail (sometimes due to a POS tagging error) or where the preposition serves a special syntactic function not captured by the supersense inventory. The most frequent is`i, which applies only to infinitival to tokens that are not Purpose or Function adjuncts.666See figure 1 for examples from the corpus. I want/love/try to eat cookies and To love is to suffer would qualify as `i; a shoulder to cry on would qualify as Function. The label `d applies to discourse expressions; the unqualified backtick (`) applies to miscellaneous cases such as infinitival-subject for and both prepositions in the as-as comparative construction (as wet as water; as much cake as you want).
Figure 3 shows how prepositions can interact with multiword expressions (MWEs). An MWE may function holistically as a preposition: PrepWiki treats these as multiword prepositions. An idiomatic phrase may be headed by a preposition, in which case we assign it a preposition supersense or tag it as a discourse expression (`d). Finally, a preposition may be embedded within an MWE (but not its head): we do not use a preposition supersense in this case, though the MWE as a whole may already be tagged with a verb supersense.
The annotation tool uses heuristics to detect candidate preposition tokens in each sentence given its POS tagging and MWE annotation. A single-word expression is included if:
it is tagged as a verb particle (rp) or infinitival to (to), or
it is tagged as a transitive preposition or subordinator (in) or adverb (rb), and the word is listed in PrepWiki (or the spelling variants list).
A strong MWE instance is included if:
the MWE begins with a word that matches the single-word criteria (idiomatic PP), or
the MWE is listed in PrepWiki (multiword preposition).
Annotators proceeded sentence by sentence, working in a custom web interface (figure 4). For each token matched by the above heuristics, annotators filled in a text box with the contextually appropriate label. A dropdown menu showed the list of preposition supersenses and non-supersense labels, starting with labels known to be associated with the preposition being annotated. Hovering over a menu item would show example sentences to illustrate the usage in question, as well as a brief definition of the supersense. This preposition-specific rendering of the dropdown menu—supported by data from PrepWiki—was crucial to reducing the overhead of annotation (and annotator training) by focusing the annotator’s attention on the relevant categories/usages. New examples were added to PrepWiki as annotators spotted coverage gaps. The tool also showed the multiword expression annotation of the sentence, which could be modified if necessary to fit PrepWiki’s conventions for multiword prepositions.
3.2 Quality Control
Annotators were selected from undergraduate and graduate linguistics students at the University of Colorado at Boulder. All annotators had prior experience with semantic role labeling. Every sentence was independently annotated by two annotators, and disagreements were subsequently adjudicated by a third, “expert” annotator. There were two expert annotators, both authors of this paper.
200 sentences were set aside for training annotators. Annotators were first shown how to use the preposition annotation tool and instructed on the supersense distinctions for this task. Annotators then completed a training set of 100 sentences. An adjudicator evaluated the annotator’s annotations, providing feedback and assigning another 50–100 training instances if necessary.
Inter-annotator agreement (IAA) measures are useful in quantifying annotation “reliability”, i.e., indicating how trustworthy and reproducible the process is (given guidelines, training, tools, etc.). Specifically, IAA scores can be used as a diagnostic for the reliability of (i) individual annotators (to identify those who need additional training/guidance); (ii) the annotation scheme and guidelines (to identify problematic phenomena requiring further documentation or substantive changes to the scheme); (iii) the final dataset (as an indicator of what could reasonably be expected of an automatic system).
The main annotation was divided into 34 batches of 100 sentences. Each batch took on the order of an hour for an annotator to complete. We monitored original annotators’ IAA throughout the annotation process as a diagnostic for when to intervene in giving further guidance. Original IAA for most of these batches fell between 60% and 78%, depending on factors such as the identities of the annotators and when the annotation took place (annotator experience and PrepWiki documentation improved over time).777Specifically, the agreement rate among tokens where both annotators assigned a preposition supersense was between 82% and 87% for 4 batches; 72% and 78% for 11; 60% and 70% for 17; and below 60% for 2. This measure did not award credit for agreement on non-supersense labels and ignored some cases of disagreement on the MWE analysis. These rates show that it was not an easy annotation task, though many of the disagreements were over slight distinctions in the hierarchy (such as Purpose vs. Function).
Though Schneider et al. (2015) conducted pilot annotation in constructing the supersense inventory, our annotators found a few details of the scheme to be confusing. Informed by their difficulties and disagreements, we therefore made several minor improvements to the preposition supersense categories and hierarchy structure. For example, the supersense categories for partitive constructions proved persistently problematic, so we adjusted their boundaries and names. We also improved the high-level organization of the original hierarchy, clarified some supersense descriptions, and removed the miscellaneous Other supersense.
The changes to categories/guidelines noted in the previous paragraph required a small-scale post hoc revision to the annotations, which was performed by the expert annotators. Some additional post hoc revisions were performed to improve consistency; e.g., some anomalous multiword expression annotations involving prepositions were fixed.888In particular, many of the borderline prepositional verbs were revised according to the guidlines outlined at https://github.com/nschneid/nanni/wiki/Prepositional-Verb-Annotation-Guidelines.
Because sentences were adjudicated by one of two expert annotators, we can estimate the dataset’s adjudication reliability—roughly, the expected proportion of tokens that would have been labeled the same way if adjudicated by the other expert—by measuring IAA on a sample independently annotated by both experts.999These sentences were then jointly adjudicated by the experts to arrive at a final version. Applying this procedure to 203 sentences annotated late in the process (using the measure described in footnote 7) gives an agreement rate of .101010For completeness, Cohen’s . It is almost as high as raw agreement because the expected agreement rate is very low—but keep in mind that ’s model of chance agreement does not take into account preposition types or the fact that a relatively small subset of labels were suggested for most prepositions. On the 4 most frequent prepositions in the sample, per-preposition is .84 for for, 1.0 for to, .59 for of, and .73 for in. It is difficult to put an exact quality figure on a dataset that was developed over a period of time and with the involvement of many individuals; however, the fact that the expert-to-expert adjudication estimate approaches 90% despite the large number of labels suggests that the data can serve as a reliable resource for training and benchmarking disambiguation systems.
3.3 Resulting Corpus
4250 tokens have preposition supersenses. Their distribution appears in figure 5. Over 75% of tokens belong to the top 10 preposition types, while the supersense distribution is closer to uniform. 1170 tokens are labeled as Location, Path, or a subtype thereof: these can roughly be described as spatial. 528 come from the Temporal subtree of the hierarchy, and 452 from the Configuration subtree. Thus, fully half the tokens (2100) mark non-spatiotemporal participants and circumstances.
Of the 4250 tokens, 582 are MWEs (multiword prepositions and/or PP idioms).111111For the purpose of counting prepositions by type, we split up supersense-tagged PP idioms such as those shown in (3) and (3) by taking the longest prefix of words that has a PrepWiki entry to be the preposition. A further 588 have non-supersense labels: 484 `i, 83 `d, and 21 `.
To facilitate future experimentation on a standard benchmark,
we partitioned our data into training and test sets.
We randomly sampled 447 sentences
(4,073 total tokens and of preposition instances)
for a held-out test set, leaving 3,888 preposition instances for training.121212Excluding `i and
`other instances, the supersense-labeled prepositions amount to 3,397 training
and 853 test instances.
The sampling was stratified by preposition supersense so as to encourage a
reasonable balance for the rare labels; e.g., supersenses that occur twice are split so that
one instance is assigned to the training set and one to the test set.131313The sampling algorithm
considered supersenses in increasing order of frequency: for each supersense
having instances, enough sentences were assigned to the test
set to fill a minimum quota of tokens for that supersense
(and remaining unassigned sentences
containing that supersense were placed in the training set).
Relative to the training set,
the test set is skewed slightly in favor of rarer supersenses. A small number of annotation errors
were corrected subsequent to determining the splits. Entire sentences were sampled
to facilitate future studies involving joint prediction over the full sentence.
tokens for that supersense (and remaining unassigned sentences containing that supersense were placed in the training set). Relative to the training set, the test set is skewed slightly in favor of rarer supersenses. A small number of annotation errors were corrected subsequent to determining the splits. Entire sentences were sampled to facilitate future studies involving joint prediction over the full sentence.Figure 6 shows, at a type level, the extent of overlap between the training set, test set, and PrepWiki. 61 preposition supersenses are attested in the training data, while 14 are unattested.
We have introduced a new lexical semantics corpus that disambiguates prepositions with hierarchical supersenses. Because it is comprehensively annotated over full documents, it offers insights into the semantic distribution of prepositions. The corpus will further facilitate the development of automatic preposition disambiguation systems.
We thank our annotators—Evan Coles-Harris, Audrey Farber, Nicole Gordiyenko, Megan Hutto, Celeste Smitz, and Tim Watervoort—as well as Ken Litkowski, Michael Ellsworth, Orin Hargraves, and Susan Brown for helpful discussions. This research was supported in part by a Google research grant for Q/A PropBank Annotation.
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