Beyond Possession Existence: Duration and Co-Possession (2020)

Abstract: This paper introduces two tasks: determining (a) the duration of possession relations and (b) co-possessions, i.e., whether multiple possessors possess a possessee at the same time. We present new annotations on top of corpora annotating possession existence and experimental results. Regarding possession duration, we derive the time spans we work with empirically from annotations indicating lower and upper bounds. Regarding co-possessions, we use a binary label. Cohen’s kappa coefficients indicate substantial agreement, and experimental results show that text is more useful than the image for solving these tasks.

## introduction relation extraction is a core problem in natural language processing. extracting relations is generally defined as linking two text chunks with a label. for example, relations such as presi-dent of and married to are common in information extraction @xcite . within computational semantics, relations capture spatial and temporal knowledge @xcite , as well as many other meanings @xcite . approaches to relation extraction usually only determine the right label-often referred to as relation name or type-between two text chunks. relation labels are certainly useful, but there is almost always complementary information that can be extracted. for example, relation labels do not give any hint about for how long the relation holds true or whether the relation is one-to-one or oneto-many. many relations would benefit from having this additional information available, including located at (people have many locations over time) and agent (some events are carried out by only one person but not all; the additional agents may not be explicitly named in a given text). possession relations are ubiquitous and understudied from a computational perspective. possessions are defined as someone (the possessor) possessing something (the possessee), where possessing includes not only ownership but also control, kinship, physical and temporal proximity, and others (section 2). from a computational perspective, previous work on extracting possessions targets possession existence (i.e., whether a possessor x possesses a possessee y) and limited temporal information using anchors, (e.g., at some point of time before or after an event, section 2). in this paper, we complement previous work targeting possession existence with two attributes: duration (for how long does the possession hold true?) and co-possession (are there other possessors possessing the possessee concurrently?). consider the tweet in figure 1 . the possessee is the cup, and from the text we understand that it is reusable. thus the author of the tweet is likely to have the cup for a few weeks or months. if the possessee were a paper cup, however, the author would probably have it for at most one hour. similarly, if the possessee were a personal coffee mug, the author would have it for longer-probably years. on the other hand, if either the text or image indicated that the setting was a restaurant, the author most likely would only have the cup for at most a couple hours, and there would be a copossession-the restaurant and the customer. the main contributions of this paper are: (a) strategy to determine sound intervals for possession durations grounded on lower and upper temporal bounds; (b) corpus of possession relations annotated with durations and copossessions; foot_0 (c) detailed corpus analysis; and (d) experimental results showing that both tasks can be automated. while we work with possessions, a similar approach could be used to determine the duration of any relation and distinguish between one-to-one and one-to-many relations. ## annotating possession duration and co-possession to the best of our knowledge, we are the first to go beyond possession existence and target possession duration and co-possession. more generally, we are the first to determine for how long a semantic relations holds true, and distinguish between oneto-one and one-to-many relations. thus, we create a new corpus to tackle these tasks. source corpora. starting from plain text is a straightforward choice. since existing corpora already annotate possession existence, however, it would be suboptimal. thus we work with the corpora by @xcite , @xcite , and @xcite , and enhance their possession existence annotations with possession duration and co-possession annotations. these source corpora contain 2,257 possession relations, a relatively small amount. we note, however, that the source corpora are diverse (section 2) and include possession relations identified in formal (ontonotes) and informal texts (weblogs, twitter). additionally, we work with possessions identified from not only text (ontonotes and weblogs), but also tweets consisting of text and images. the corpus by chinnappa and blanco (2018) contains 979 sentences, and we select the 358 intra-sentential possessions annotated in those sentences. the corpus by @xcite contains 799 possession relations. the possessor is always the author of a weblog, and the possessee is mentioned in the weblog and can be: (a) a concrete object, e.g., car, notebook; (b) an implicit concrete object associated with an event, e.g., car for driving, cell phone for texting; or (c) an abstract object, e.g., wifi, idea. the corpus by @xcite contains 5,000 tweets (text + image). we select 1,100 tweets in which the author (the possessor) possesses a concrete object mentioned in the tweet (the possessee). ## corpus analysis the distribution of other synsets indicate that possessees are unlikely to have co-possessors, but to a lesser degree. the right-hand side of ## experiments and results in order to predict possession duration and copossession, we experiment with logistic regression and a neural network ensemble including a text component and two image components. each possession relation becomes an instance, and we create stratified training (80%) and test (20%) sets. we also reserve 20% of the training as validation set. more specifically, we build two classifiers: one for possession duration (short, medium, or long) and one for co-possession (yes or no). logistic regression. we use the implementation by scikit-learn @xcite , and use bag-of-words features for the sentence at hand. specifically, we use binary flags indicating word presence, and additional flags to indicate the word corresponding to the possessor and possessee. neural network. the network architecture is similar to the one in our previous work @xcite . it includes a text component and an image component ( the text component is an lstm that takes as input the sentence (or tweet) containing the possessee. words are represented with the concatenation of their 300-dimensional glove embedding @xcite and an additional embedding indicating whether a token is the possessor, possessee, or neither. we train the additional embeddings from scratch with the rest of the network. the image component uses two pretrained neural networks. first, we concatenate to the softmax output layer the weights from the average pooling layer (second to last layer) of inception-net @xcite . second, we obtain the top 5 tags from the google cloud vision api and incorporate them as an additional textual input. ## conclusions standard relation extraction does not provide information about for how long relations hold true or whether relations are one-to-one or one-to-many. in this paper, we tackle both problems and determine possession durations and co-possessions. possessions are ubiquitous yet understudied from a computational perspective. from a theoretical perspective, they include having control over something (e.g. flying a plane, impounding a vehicle, eating ice cream) thus most objects are actually possessees of one or more possessors. additionally, as just exemplified, many possessions can be extracted even if prototypical possession verbs (e.g., have, buy, acquire) are missing. we have presented new annotations on top of existing corpora. regarding durations, we collect lower and upper bounds in order to derive sound duration intervals. the resulting three intervals obtain substantial agreement (0.63 cohen's κ). regarding co-possessions, we obtain slightly better agreement (0.65 cohen's κ). we have also presented baseline models and a neural network architecture to solve both tasks. beyond word embeddings, the lstm benefits from additional embeddings indicating the tokens that are the possessor and possessee. information extracted from the image, however, is not helpful. while the work presented here targets possession relations, we believe that a similar approach could be used to to determine for how long any semantic relation holds true.