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Partml: Final Paper PartML: Final Paper Tuan Do N. Nick Moran Mikel Mcdaniel May 11, 2013 Abstract 2. member of (senator and senate) Detecting meronymic relationships is member-collection of great use in many natural language ex: I attend Brandeis ... processing domains, such as process- ing biomedical text, question-answering 3. substance of (gold and ring) systems, text summarization, and text substance-whole understanding. (8) (10) We describe, PartML, an annotation specification and ex: Air contains nitrogen ... guideline for annotating part-whole re- lationships and related entity and rela- Our specification captures each of these three tionship attributes that are useful for types with the goal of differentiating the exact applying machine learning. We also type of meronym when one is detected. Our find- provide a brief overview of previous ings include differences in the agreement of anno- works and their approaches for defin- tation for these different types, as well as prelim- ing and automatically finding part-whole inary results from machine learning efforts. We relationships in text, then discuss the describe potential improvements to our specifica- strenghts and weaknesses of our annota- tion and guideline to improve agreement, to bol- tion schemes, including inter-annotator ster future machine learning, and to expand the agreement scores, and finally end with results of machine learning methods ap- scope of the task to related areas. plied to our gold-standard corpus. 2 Previous works 1 Introduction Part{whole or meronymy relations are an im- PartML is an annotation scheme designed to cap- portant binary semantic relation that have been ture meronymic, or part-whole, relationships and worked on from the time of the atomists (Plato, related attributes that are useful for applying Aristotle, and the Scholastics) and further in- machine learning to automatically detect these vestigated in the beginning of the 20th century. relationships in unannotated texts. To cover a Though it is always considered a fundamental large knowledge domain that is both wide and relation, there is no consensus on formal defi- deep, PartML's guidelines are tuned to work nition, representation, and classification and of well with annotated plain-text versions of English meronymy relations in previous works. Studies Wikipedia articles, but should be directly appli- of logic and philosophy of meronymy relations cable or easily translatable to other languages also disagree on its basic properties, especially and types of text. the transitivity property (whether the relation is transitive or not). While different sources have varying views on the classes of part-whole relationships, we use In regards to formal representation and for- the three basic types defined by WordNet: mulization of meronymy relations, there is a simple approach that models the relations as a transitive binary relation in Description Logic. 1. part of (hand and arm) That includes works of Artale et al. (1996) (1), 1 Sattler 1995(2). For example, to say that car has jects in the extent of the concepts, which is fun- wheels and that in turn the wheels have tires as damental in part-whole relationships, it ignores their parts, it could represented as: other important attributes such as the physical existance of these objects (discrete or abstract) Car = 9 (W heel u T ires) that could also provide insight on the differentia- tion of meronymy relation. It also doesn't explic- lead to itly describe the inherently dependent or func- tional relation between involved objects in each Car T ires @ subclass. Winston et al. (1987) (6) described six types Priss, in his discussion on construction of Word- of meronymic relations that are (1) COMPO- Net (3), used a formal definition of meronymy NENT { INTEGRAL,(2) MEMBER { COL- relations, together with other semantic relations LECTION,(3) PORTION { MASS,(4) STUFF (i.e. hypernymy, synonymy) based on Relational { OBJECT,(5) FEATURE { ACTIVITY, and Concept Analysis. The relation is defined among (6) PLACE { AREA. In addition, they proposed synsets, that is differentiated from lexical relation (i.e. antonymy). His work characterizes the re- three attributes of relation called relation ele- lation with three attributes irreflexive, antisym- ments i.e. functional, homeomerous, separable. metric and acyclic. The relation is parameterized For example, PORTION{MASS is homeomerous by quantificational tags, that are simple quanti- because portions are similar to each other, and fiers, such as for all, exactly 1, and some. separable as portion could be disconnected from mass (cut a slice from a pie). They also suggest c Rr[Q1;Q2;]c : () Q1 Q2 : g rg 1 2 g12Ext(c1) g22Ext(c2) 1 2 a hierachical categorization of semantic relations, r 3 4 3 4 giving a distinction between meronymic relation c1R [; Q ;Q ]c2 : () Qg22Ext(c2)Qg12Ext(c1) : g1rg2 and some other `part-of' relations, including spa- c Rr[Q1;Q2; Q3;Q4]c : () c Rr[Q1;Q2;]c 1 2 1 2 tial inclusion, class member, attributes and pos- r 3 4 andc1R [; Q ;Q ]c2 sesion. Keet et al. (2007) distinguishs the use of two There is also no agreement on the classification terms part-whole relation and meronymic rela- of part-whole relations. Cruse (4), using the same tion. They include meronymic relation as a sub- notion as Priss, described four subclasses, consid- class of part-whole relation while the other class ering the cardinality of two word concepts in the is a mereological part-of relation. The latter is a relation. Iris et al.(5) specified four other classes: transitive relation that encompasses a wide range functional component (exactly one whole), Seg- of functional and spatial part-of relations. mented whole/mass nouns/is-substance-of (mul- Most of previous works apply a predefined set tiple homogenous parts - one whole), member- of lexical-syntatic patterns, such as the follow- ship relation (multiple whole), individual con- ing pattern from (8) to construct the meronymic cepts: (one part - one whole). Although this corpus: approach captures the cardinality of involved ob- 2 2 Cluster Patterns Freq. Coverage Examples C1. genitives NPX of NPY eyes of the baby and NPY 's NPX 282 52.71% girl's mouth verb to have NPY have NPX The table has four legs. C2. noun NPXY 86 16.07% door knob compounds NPYX turkey pie C3. preposition NPY PPX 133 24.86% A bird without wings cannot fly NPX PPY A room in the house. C4. other others 34 6.36% The Supreme Court is a branch of the Government. This rule-based approach for extraction of also occur in abstract entities, in which there is meronymy relation suffers from insufficiency be- some sort of underlying pattern structure for the cause many patterns that signify part-whole re- part to participate in. lations are complicated and involve longer struc- The second type of meronym is the substance tures. relationship. This is differentatied from the part- whole type by the fact that the entity in the part role is often homogenous and is the material out 3 Task of which the part is made, rather than playing a specific structural role. It is possible for an en- While many previous works focus on identi- tity to have be made of multiple substances, as fying meronymic relationships via rule-based is often the case for mixtures and conglomerates. and pattern-matching methods which have been Mass nouns are common in the part role for these boot-strapped from known meronymic pairs, our substance-based relationships. goal was to use subtler syntactic clues to the ex- The third type is the member-group relation- istence of these relationships with the hope of ship. This type is similar to the part-whole re- powering a learner which is able to handle a wider lationship, but member entities do not have a variety of structures indicating the sought after direct structural role in the whole. Instead, this relationships. To this end, we frame meronym de- type of relationship is defined by the presence of tection as a classification problem in which pairs an associative relationship, often based on phys- of entities either are or are not in a meronymic ical proximity or social connections. relationship. It is important to note that this relationship is inherently directional, because we In order to keep a focused and well-defined need to distinguish the part from the whole. task, we exclude certain relationships that other authors have included as meronyms, or which are Although our initial machine learning efforts tangentially related to meronyms. This include focus on simple detection of meronymic relation- relationships of classification (often termed is- ships, our specification is set up to also provide a) relationships, the containment relationship (a features for further classification of a detected re- form of has-a), geographic and location-based re- lationship as one of the three types mentioned lationships, mass-portion relationships, activity- earlier. As such, the distinction between the based meronyms (we focus on nouns as entities, three is important to our task, and its effective while these are based on verbs), possession and communication to annotators in the guideline is owernship relationships, and others. These are critical. left open as possible extensions of the specifica- The first type of meronym is the simple part- tion in future work. whole relationship. In this sort of relationship, As an aid to machine learning, we include ex- the entity playing the part role is a distinct com- plicitly negative meronymic relationships in our ponent of the whole, in which it plays some struc- specification. These often show very similar syn- tural role. While this is a very common type tactic structure to positive instances, except for of meronym for physical objects, especially those the presence of a negation word. If these were with mechanical or anatomical structure, it can 3 simply excluded, they would present confusing chine learning.
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