Two-level semantic analysis of compounds:

A case study in linguistic engineering^{∗}

Wilco G. ter Stal & Paul E. van der Vet

Knowledge-Based Systems Group Department of Computer Science, University of Twente Enschede, The Netherlands Email: {terstal, vet}@cs.utwente.nl

Abstract

We describe an experiment of a two-level approach for the automated semantic analysis of N-N compounds. The first stage of the interpretation process consists of the translation of compounds into a representational format called Quasi Logical Form (QLF). The second stage consist of a mapping of QLFs onto domain-dependent, conceptual representations. Specifically, in the context of the Plinius project these QLFs are mapped onto relevant, formal expressions in terms of the so-called Plinius ontology KB. We briefly describe the linguistic analysis and then focus on a number of cases of deriving conceptual descriptions from QLFs. Our ultimate goal is to apply the method to a large (> 2000) number of compounds within a specific domain.

1 Introduction

Problems with processing N-N compounds have been described earlier in the literature, for instance by (Wachter and Provoost, 1993; Bouillon et al., 1992; Isabelle, 1984). In languages such as Dutch and German, syntactic processing of compounds, that is identification, segmentation and disambiguation, is already a difficult enterprise. However, the most difficult issue related to compound processing concerns the interpretation task. In general, this task amounts roughly to the derivation of an expression in a suitable meaning representation language, based on the components of the compounds.

We have been studying English compounds in the context of the Plinius project, see § 3, which uses a large text corpus. ¿From this corpus, we (manually) identified about 2200 compounds. In this paper we will focus on the particular module of the NLP engine used within Plinius that is responsible for the automated semantic analysis of these compounds.

∗The authors are indebted to Franciska de Jong for her valuable comments on earlier versions of this paper.

First, we will briefly summarize the general, linguistic perspective on the semantic analysis of compounds. The purpose of this section is to see whether we can employ certain linguistically motivated distinctions within the interpretation task. In section 3 we will describe briefly the Plinius project in which our research is carried out. Specifically, we will discuss the linguistic processes within Plinius. In section 4 we will apply the two-level semantic analysis to Plinius compounds. In order to clarify the approach we will present a number of different cases. Section 5 contains a critical discussion of the introduced approach. Conclusions and suggestions for further research can be found in section 6.

2 Compound analysis: the linguistic perspective

A frequently recurring analysis concerning the semantics of compounds uses the notion of an implicit semantic relation between the components of a compound (Downing, 1977; Isabelle, 1984; Finin, 1986). For instance, compound (1) can be paraphrased as (2) and represented as a predicate-argument structure as in (3).

(1) GM car

(2) Car made by GM

(3) made by(GM, car)

Traditionally, linguists have been trying to identify and to classify these semantic relations. We may distinguish the descriptive approaches, for instance Warren (1978), and studies within the generative framework such as Levi (1978); Selkirk (1982); Grimshaw (1991). The main objective of the generative approaches is to relate compounds systematically with predicate-argument structures. For instance, Levi (1978) proposes a number of so-called Recoverably Deletable Predicates (RDPs) which may be used to characterize the implicit semantic relation within compounds. In table I, we will give some examples of these RDPs.

RDP  Examples
CAUSE cigarette smoke, drug death
HAVE picture book, lemon peel
MAKE daisy chain, coffee machine
USE  steam iron, water pipe
IN  house dog, kitchen table

Table I: Recoverably Deletable Predicates as semantic relations within compounds

A major problem of Levi’s theory is the absence of a procedure to determine what RDP (or for that matter semantic relation) to select given a particular compound. For instance, cigarette smoke may be paraphrased as (4) or (5).

(4) smoke that is caused by a cigarette

(5) smoke that is made by a cigarette

Additionally, some predicates are semantically ambiguous. For instance, the IN-RDP may be used to specify a locative relation as in kitchen table as well as a temporal location as in summer breeze. Even if we introduce more fine-grained predicates, such as loc-IN and temp-IN, the problem remains that there is no syntactic clue, i.e. grammatical information, in the compound itself that suggests the selection of the proper RDP. Therefore, we claim that compounds bear an implicit semantic relation, the nature of which cannot be determined on the basis of grammatical knowledge alone. In fact, it seems that the interpretation of a compound is largely dependent on extra-linguistic or domain dependent knowledge.

Grimshaw (1991); Selkirk (1982); Isabelle (1984) appear to identify some exceptions. They distinguish a class of nouns which subcategorizes for other nouns. An important subclass of these nouns are deverbal nominalizations, that is nouns derived from a verb. In case a nominalization forms the head of a compound, the modifying noun can be semantically interpreted as an argument. In table II we have given some examples of nominalizations, the nominalization as head noun in a compound and a predicate-argument structure for such a compound.

Deverbal nominalization Compound  Predicate-argument structure
giving gift giving give( , gift, )
observing  animal observing observe( , animal)
mixing  powder mixing mix( , powder)

Table II: Deverbal nominalizations in compounds

Compounds containing a deverbal head with the affix -ing, so with an internal structure as in [[N]_{N} [V-ing]_{N} ]_{N}, are referred to as synthetic compounds.^{1} In table II we show that synthetic compounds trigger a different type of predicate-argument structure compared to standard, i.e. containing no nominalization, compounds, as (3). A consequence of this grammatical observation is that we should capture the difference between the two types at the linguistic level.^{2}

In the following section we will briefly describe NLP research in Plinius, in order to sketch the context of our compound approach.

1The term synthetic compound is due to Grimshaw (1991). Note that there are a lot of other compounds containing a head derived from a verb. Examples are truck driver, engine repair, oil pump etc. However, if we consider these compounds, the underlying predicate-argument seems less straightforward, for instance ?DRIVE(driver, truck). In order not to complicate matters we limit the discussion to synthetic compounds.

2There are a number of other linguistic motivations for separating synthetic compounds from standard compounds. For instance, synthetic compounds do not pluralize. For more details, see Grimshaw (1991).

3 Processing compounds in Plinius

The Plinius project aims at developing a system which is capable of semi-automatically extracting domain-specific knowledge from the title and abstract of scientific publications in the field of ceramic materials. The knowledge base resulting from the Plinius project should have economical potential, that is, the benefits of using the knowledge base should outweigh the costs of developing it. In order to obtain this ultimate goal we have made a number of design decisions among which are: (1) use of abstracts,

(2) limitation to sublanguage, operationally defined by a corpus and (3) application of an ontology. For a detailed review of these design decisions we refer to Mars et al. (1993).

The automated semantic analysis of compounds contributes to the overall goal, since our input consists of abstracts containing information in a highly condensed format, which results in a frequent occurrence of compounds. Moreover, since the abstracts describe the results of innovative research, new compounds are used to denote (new) concepts.

In the following subsections we will first clarify the function of the ontology within Plinius. Subsequently, we will briefly explain the general NLP approach we are following. In particular we will focus on two-level semantic analysis, as proposed by van der Sloot and Rentier (1993).

3.1 The Plinius ontology as specification of semantics

In Plinius, a central role is played by a structured concept system (or ontology in current AI terminology). The Plinius ontology (van der Vet and Mars, 1993; van der Vet and Mars, 1991; Mars, 1993) is in first approximation a limitative list of concepts and relations between them. It currently contains concepts for materials, their chemical composition, processes to make materials, and properties of materials. The output of the Plinius process is to be expressed in terms of ontology concepts and relations only. In this sense the Plinius ontology indirectly specifies the semantics^{3} of the texts that are processed. Although the ontology provides a framework for lexical semantic knowledge, it can not be compared directly with the model of a generative lexicon described in Pustejovsky (1991). In particular, Pustejovsky distinguishes four basic levels of semantic description whereas we only employ one.

In a more detailed account, the Plinius ontology is not a flat list of concepts but a structured system. It consists of atomic (primitive) concepts, distributed over several sets for clarity, and rules for making complex concepts. Any complex concept therefore is a particular combination of atomic concepts and thus coincides with its definition. We refer to this way of organising an ontology as the principle of the conceptual construction kit.

The principle of the conceptual construction kit can be illustrated by the concept for a particular chemical, the pure substance aluminium oxide (also known as alumina, chemical formula Al_{2}O_{3}). Concepts for chemicals are defined as sets of tuples. Each tuple has two arguments: a material ingredient and a number giving the proportion. The atomic concepts needed for constructing this concept are chemical elements and natural numbers. For disambiguation, concepts for pure substances do not consist of elements with their proportions but an intermediate level of concepts called groups is defined. For aluminium oxide, there are two groups and each group is a set of one tuple: g_{1} = {⟨Al, 1⟩} and g_{2} = {⟨O, 1⟩}. The concept for aluminium oxide then is the set {⟨g_{1}, 2⟩, ⟨g_{2}, 3⟩}.

3Velardi (1991) uses the notion technical semantics for a similar approach where word senses are defined in terms of technical knowlegde concerning a domain.

In the account below, the level of detail achieved in the ontology is not needed and we will often use abbreviations. For instance, the concept for aluminium oxide just elaborated will be abbreviated as alumina. Further illustrations are provided below.

The particular language chosen to express these concepts is unimportant as long as the expressions are unambiguous. In this paper, we will write complex concepts as feature structures and relations as simple predicate-argument structures for clarity. Examples are given in § 4.3.

3.2 Grammar engineering in Plinius

The overall goal of the language-driven process is to convert natural language constructs into elements suitable for storage in a knowledge base. In order to attain this goal, we are developing and implementing an NLP system which currently consists of the following components:

Preprocess The task of the preprocess, as described in van Raalte et al. (1992), is to segment the abstracts of the Plinius corpus in such a way that the subsequent processes are not hindered by, for example, ambiguous end-of-sentence markers, record information, case conventions, unknown strings of characters representing chemical formulae, or other formulae.

Sublanguage Grammar Currently, our grammar consists of about 80 rules describing linguistic constructions specific for our texts. The formalism we employ is PATR (Shieber, 1986; Gazdar and Mellish, 1989). Thus, grammar rules are context-free phrase structure rules annotated with features. More details concerning the coverage and the organisation of the grammar can be found in Stefanova and ter Stal (1993); van der Vet et al. (1993).

In order to develop, test and debug the Plinius grammar rapidly, we developed a tool with a user-friendly, graphical interface, see Hofman and ter Stal (1994).

Head Corner chart parser The parser which operates on the Plinius grammar is in fact an extension of the Head-Corner (HC) parser described in Sikkel and op den Akker (1993). The main difference stems from the fact that the current parser is allowed to use a context-free grammar annotated with feature structures, i.e., a PATR grammar. More details concerning the HC parser can be found in Verlinden (1993).

The core of the language-dependent process is formed by the sublanguage grammar. In combination with the parser, sentences are transformed into (I) a conventional parse tree representing the syntactic structure of the sentence and (II) a feature structure representing both (detailed) syntactic and semantic information. For instance, the feature structure for sentence (6) amounts to (7).^{4}

(6) The material exhibits elongation

(7) [ \begin{matrix}head & : & [ \begin{matrix}agr & : & [ \begin{matrix}num & : & singular \\ per & : & 3 \end{matrix}] \\ vform & : & fin \\ tense & : & present \end{matrix}] \\ args & : & [ \begin{matrix}subject & : & [ \begin{matrix}head & : & [ agr: [ num: singular]] \\ content & : & [ \begin{matrix}para & : & x1 \\ det & : & the \\ relation & : & material \\ arg0 & : & x1 \end{matrix}] \end{matrix}] \\ object & : & … \end{matrix}] \\ mod & : & null \\ content & : & [ \begin{matrix}para & : & e1 \\ det & : & E \\ relation & : & exhibit \\ arg0 & : & e1 \\ arg1 & : & x1 \\ arg2 & : & x2 \end{matrix}] \end{matrix}]

The language-dependent process is described in more detail in van der Vet et al. (1993).

3.3 Quasi Logical Form in Plinius

The values of the content features in (7) are used to construct a so-called QLF. The idea of QLFs as described in van Eijck and Alshawi (1992); van der Sloot and Rentier (1993); van der Vet et al. (1993) is that they form a suitable data structure for storing grammatical information relevant to further semantic and discourse processing. This means that in the Plinius context QLFs form the input for an additional process which maps expressions in QLF onto the final representation in terms of the Plinius ontology.

We are employing the notation for QLF as described in van der Sloot and Rentier (1993). The general format of a QLF expression amounts to: det(parm, [restr, …], where det functions as a kind of quantifier binding the parameter parm which is restricted by predicates in restr. The QLF for sentence (6) amounts to:

4Due to space limitations we leave out the complete value of the object feature.

(8) E(e1, [exhibit(e1, the(x1, [material(x1)]), zerodet(x2,
     [elongation(x2)]))])

Note that the ’quantifiers’ of the NPs correspond to the normal linguistic determiners, like the, a, all, most, every etc. The quantifier E in (8) is an existential quantifier of events (kind of actions). In van der Sloot and Rentier (1993) it is explained why they employ so called event-semantics. Here it suffices to note that the event style analysis^{5}is to be prefered over the standard first-order fashion, simply because it contains more information.

Another feature, noted by Rich et al. (1987), of QLFs is that they enumerate the entities (referents)^{6} referred to by the sentence as well as the surface functional relationships among those entities. Note that the QLF in (8) in fact is a linear notation of the collected content features of (7). For a further elaboration on the format and the theoretical background of QLF the reader is referred to van der Sloot and Rentier (1993).

In the following section, we will describe how compounds may be represented in QLF and how they relate to conceptual descriptions.

4 Two-level semantic analysis of compounds

In this section, we will explain our method for the two-level semantic analysis of compounds. As explained in § 3.3 the first phase of semantic analysis in Plinius consists of deriving a QLF from natural language input. It is important to note that the lexicon and grammar rules provide the necessary information to construct a QLF.

A QLF constitutes an intermediate, underspecified representation of a NL construct. This means that certain lexical semantic and conceptual aspects are only made explicit during the second phase. The result of this second phase is a representational structure in terms of the Plinius ontology, see § 3.1, which we will call Ontology Knowledge Base Representation (henceforth: OKBR).

First, we will present the QLF format for compounds. Subsequently, we will present a brief conceptual analysis of compounds. Finally, we will sketch the formalization of translating QLFs to OKBRs.

4.1 Representing compounds in QLF

In § 2 we showed that the class of synthetic compounds should be grammatically distinguished from the class of standard compounds. In addition, we explained that QLFs should capture relevant grammatical information. Based on these two observations we propose the following formats for compounds in QLF. Standard compounds, such as diesel engine, are represented as follows:^{7}

5Explained from a general, linguistic point of view in Parsons (1991).

6In case of example (8) these entities are indicated by e1, x2, x3.

7The QLF examples in fact represent compounds functioning as NPs with a ∅-determiner. However, (9) is a count compound noun for which such an analysis is unlikely. Despite this deficiency,

(9) diesel engine ⇒ zerodet(x1, [engine(x1), REL(x1, zerodet(x2,
                                          [diesel(x2)]))])

The QLF in (9) contains the maximum grammatical information for a standard compound. It enumerates the entities involved in the compound and an underspecified semantic relation between them, viz. REL(x, y).

If we follow the proposal of Grimshaw (1991) to treat synthetic compound differently, then we have to translate them into different QLFs. For instance, animal observing can be represented as:

(10) animal observing ⇒ zerodet(e1, [observe(e1, Var, zerodet(x1,
                                          [animal(x1)]))])

QLF (10) can be explained as follows. The noun observing triggers a ternary predicate observe in which the QLF for animal is going fill the object-argument. The subjectargument remains empty, notated with Var.

A negative consequence of distinguishing standard ad synthetic compounds is that both lexicon and grammar become more complex. First, we cannot specify nouns in a uniform way, because event-like nouns such as forming, pressing, observing etc., will receive a different content feature as compared to standard nouns. Moreover, we will have to define (at least) two compound rules in the grammar. In the next section we will try to relate QLFs for compounds to OKBRs.

4.2 OKBRs for compounds: conceptual preliminaries

Until now we implictly assumed that the semantic representation of a compound can be derived in a compositional fashion. However, linguists recognize a large class of compounds with a meaning not directly related to the meaning of its parts. For instance, the compound in (11) is not any ’bird that is black’ but rather a particular kind of bird also known as Turdus merula.^{8}

(11) blackbird

Following standard terminology, for instance Isabelle (1984); Jones (1982), we will refer to compounds of the former class as productive compounds and to (non-compositional) compounds as lexicalized compounds. Note that from an implementational point of view the distinction yields a difference in processing of the compound. A lexicalized compound will be treated as a single unit with a simple QLF as for instance in (12):

(12) blackbird ⇒ zerodet(x1, [blackbird(x1)])

Productive compounds will have to be analysed by means of compound rules in the grammar yielding a QLF as in (9) or (10).

we will accept the ∅ determiner reading of (9) since a proper treatment of the issue is beyond the scope of this paper.

8In Dutch: merel.

In our system, too, a number of compounds are lexicalized. From an engineering point of view, there is no absolute contrast between lexicalized and productive compounds. For the analysis of the lexicalized compound (11) we can still imagine a system able to infer the meaning of (11) from the meanings of black and bird. Such a system will have to be equipped with a lot of background knowledge to have the inferences follow the meaning shifts that have occurred. The majority of this knowledge applies to this particular compound only. If (as will often be the case) this knowledge plays no role in the rest of the system, it is very impractical to store it. What this example makes clear is that for each compound there is a trade-off and we have to choose to lexicalize it or treat it as being productive. For certain compounds it just does not pay to infer their meaning, and therefore we lexicalize them.

4.2.1 Why certain compounds are lexicalized in Plinius

The considerations relevant for deciding whether a particular compound is lexicalized or treated as productive can become quite complicated. They can be illustrated by means of examples from the Plinius corpus. We have chosen to lexicalize the following two compounds.

(13) room temperature

(14) aluminium oxide

In natural science, (13) means preci sely 25 degrees Centigrade. A process able to infer this meaning would have to make deductions involving a concept for room, its more specific interpretation of room in a laboratory, and the subsequent standardisation that has led to the precise meaning given above. All these concepts play no role whatsoever in the rest of the system. That is a high price to pay for the capacity to infer the meaning of (13) from the meanings of room and temperature. Thus, (13) is lexicalized.

We discuss the second example, (14), in more detail. Here, the ontology does contain the concepts needed to infer the meaning of (14) from the meanings of aluminium and oxide. The lexicon would have to include an interpretation in terms of groups of aluminium and oxide. One of the readings of aluminium translates it as a group that consists of one aluminium atom (that is g_{1} of § 3.1). Oxide is translated as a group that consists of one oxygen atom (g_{2} of § 3.1), but in this case (oxide rather than oxygen) it is obvious that we are dealing with an ingredient of a pure substance. Given this information from the lexicon, an inference process has to construct the meaning of (14) by combining the two groups into a concept for the pure substance aluminium oxide. To do that, the process has to calculate the proportions of the two groups as they occur in aluminium oxide. Extra information, either in the lexicon or in the background knowledge base, is needed: the valencies of the groups and valency rules. The outcome is that aluminium groups and oxygen groups constitute aluminium oxide in the proportion 2:3. (The concept for aluminium oxide is also given in § 3.1).

As was the case for (13), a lot of knowledge has to be added to make the inference possible. But in contrast to the former case, the knowledge needed to infer the meaning of (14) applies to many other pure substances and thus is more general. The problem this time is that we cannot make use of this generality because there is no equally general decision procedure to distinguish the regular cases from the many exceptions. We can still mark the case of aluminium oxide as being regular, either in the lexicon itself or in the background knowledge base. But among the exceptions in the domain of ceramics are pure substances involving aluminium, pure substances involving oxygen, and even pure substances involving both aluminium and oxygen besides other elements. To store a mark in the lexicon, (14) has to be an entry. But if we do that, it is more pragmatic to lexicalize (14).

4.2.2 Productive compounds

Due to the contents of our corpus, see § 3, new compounds are very likely to occur. Therefore, it is inevitable to develop a procedure which handles compounds in a compositional fashion. We now turn to three examples of compounds treated as productive compounds in our system:

(15) compression stress

(16) alumina ball

(17) glass forming

Example (15) will serve to explain the two-level approach in general terms. Examples (16) and (17) are instructive because they would have to be treated differently if we distinguish between productive and synthetic compounds. This distinction may be useful for systems poor in domain knowledge. Our approach, however, bears out that the distinction fulfils no purpose if extensive use is made of adequately represented domain knowledge.

4.3 Translating QLF to OKBR: the algorithm

In this section we will sketch the translation procedure from QLF to OKB for the compounds (15), (16) and (17). The corresponding QLFs are given in (18) and (19) and (20).

(18) zerodet(x1, [compression(x1), REL(x1, zerodet(x2,
     [stress(x2)]))])
(19) zerodet(x1, [alumina(x1), REL(x1, zerodet(x2, [ball(x2)]))])
(20) zerodet(e1, [form(e1, Var, zerodet(x1, [glass(x1)]))])

A simple translation algorithm can be formulated as follows:

1. isolate the (non-variable) predicates from the QLF

2. find through a look-up in the QLF-predicate/Concept lexicon a (or more) conceptual description(s) for the predicates.

3. try to unify the concepts found in step 2.

The OKBR for compound (15) can be explained as follows. Stress is a quantity that measures the force applied to a sample. The related concept of strain measures the deformation undergone by the sample as a result of stress. The ontology conceptualises the relation between stress and strain as a property involving tensor quantities. Here, it it sufficient to note that stress can be written as follows:^{9}

(21) stress(x) ⇒ [ \begin{matrix}quantity name & : & stress \\ direction & : & V ar1 \\ magnitude & : & [ \begin{matrix}value & : & … \\ unit & : & … \end{matrix} ] \\ time dependence & : & … \end{matrix}]

Stress can be either static (constant in time) or dynamic; in the latter case, it can be applied at regular intervals with a specified frequency or at irregular intervals. This is expressed in the the time-dependence feature. It can have either the value static or have a feature structure specifying the relevant parameters of a dynamically applied stress.

In the lexicon, all values are empty. They are filled as other sentence or compound constituents are processed. In the present case, the word compression is interpreted in the lexicon as supplying a value for the direction feature, namely inward. Thus,

(22) compression(x) ⇒ [ direction: inward]

It is easy to imagine how the resulting concept for (15) will be equal to (21) except that the feature direction will receive the value inward. The QLF-concept translations for ball and alumina are respectively:

(23) ball(x) ⇒

[ sample: [ \begin{matrix}sample id & : & V ar1 \\ material & : & [ \begin{matrix}chem composition : V ar2 \\ aggregation state : solid \end{matrix}] \end{matrix}]] ∧ shape(V ar1, ball)

(24) alumina(x) ⇒ [ material: [ \begin{matrix}chem composition & : & alumina \\ aggregation state & : & V ar \end{matrix}]]

For Plinius, it has been decided to express the output conceptually as statements about samples and their properties. A sample is a particular and usually unique object that consists of a particular material. Any sample is identified by a unique label attached in the course of the language-dependent process. Often, we know more about a particular sample than just its material composition. For instance, a ball is a sample with a particular shape. This is written as an assertion involving a two-place predicate shape, with the sample identifier as its first argument and a characterisation of the shape as its second argument. Unifying the concepts in (23) and (24) yields:

9For ease of explanation we are using a simple feature structure notation for OKBRs. However, in Speel et al. (1993) we present a small part of the actual formalisation of the Plinius ontology in CLASSIC.

(25) [ sample: [ \begin{matrix}sample id & : & 245.1 \\ material & : & [ \begin{matrix}chem composition & : & alumina \\ aggregation state & : & solid \end{matrix}] \end{matrix}]]∧ shape(245.1, ball)

The concept in (25) represents a specific sample. The sample is labeled with a identifier (245.1). The material used in the sample is alumina with a solid aggregation state. The form of the sample is ball. In the next case will give the OKBR for QLF (17). The concepts for glass and forming are:

(26) form(e, x, y) ⇒

[ \begin{matrix}sample id & : & V ar1 \\ material & : & [ \begin{matrix}chem composition & : & … \\ aggregation state & : & … \end{matrix} ] \end{matrix}] ∧ process(Var1, Var2)

[ \begin{matrix}sample id & : & V ar2 \\ material & : & [ \begin{matrix}chem composition & : & … \\ aggregation state & : & … \end{matrix} ] \end{matrix}]

(27) glass(x) ⇒ [ \begin{matrix}sample id & : & V ar \\ material & : & [ \begin{matrix}chem composition & : & … \\ aggregation state & : & vitreous \end{matrix}] \end{matrix}]

Processes, used in (26), are conceptualised as being relations between two samples: the sample that constituted the starting point of the process, and the sample that is its product. A process can thus be written as a two-place predicate, with the starting sample and product sample as first and second arguments, respectively. The result of integrating the two concepts amounts to:

(28) [ \begin{matrix}sample id & : & 234.1 \\ material & : & [ \begin{matrix}chem composition & : & … \\ aggregation state & : & … \end{matrix} ] \end{matrix}] ∧ process(234.1, 234.2)

[ \begin{matrix}sample id & : & 234.2 \\ material & : & [ \begin{matrix}chem composition & : & … \\ aggregation state & : & vitreous \end{matrix}] \end{matrix}]

5 Discussion

The first issue we would like to address is whether it is essential to maintain the grammatical distinction between standard and synthetic compounds. In § 4.1 we proposed two different formats for compounds. In § 4.3 we discussed the translation from QLF to OKBR. In (28) we gave the conceptual, via QLF, translation for the compound glass forming. The examples illustrate that the actual semantic representation of a compound is generated at the second level. Therefore we would like to claim that the actual format of the QLF for the components is irrelevant as long as they trigger the appropriate conceptual translation. In Wachter and Provoost (1993) a brief discussion of compounds with a deverbal head renders a somewhat weaker, but compatible conclusion.

… predictions (of the meaning depending on syntactic clues provided by the compound (WtS/PV)) are possible to some extent, but very often they exhibit a tentative character. (Wachter and Provoost (1993), pp.19)

Therefore, we associate the word forming with form(x) and analyse all compounds as standard compounds yielding a QLF as in (9). A positive consequence, from an engineering point of view, is that all nouns are treated uniformly in the lexicon and compounds are captured with a single compound rule.

A problem which we did not discuss so far concerns the possibility of a word or QLF having more than one conceptual translation. For instance, if the word compression receives two OKBRs, the algorithm sketched in § 4.3 will become a little more complicated, since the two cases should be tested. This operation may prove to be very costly, so it may be beneficial to incorporate contextual knowledge, based on the previous processed sentences, to disambiguate between the two cases.

A third issue concerns the status of the ontology. In § 2 we argued that the decision whether we should treat a compound as lexicalized versus productive in fact depends on the granularity and scope of the ontology. However, if the amount of lexicalized compounds tends to become too high, an extension of the ontology should be considered. Such an extension, in terms of more fine-grained conceptual distinctions, would provide the means to analyse more Plinius compounds in a compositional fashion. At this moment it remains unclear how one should find an economical balance contents of the ontology and its application for semantic analysis.

6 Conclusions and further work

We discussed a two-level semantic analysis applied to compounds. We demonstrated that actual meaning representations of Plinius compounds are determined by the Plinius ontology. We also showed that in order to arrive at the final representation it is not necessary to capture grammatical information at the intermediate (QLF) level. Moreover, we argued that the distinction between lexicalized and productive compounds is a gradual one. The decision to classify compounds as belonging to the former or latter group is, in our view, motivated by pragmatic (viz. engineering) principles only.

Further work includes investigation of a larger sample of compounds from our corpus. This sample will also contain compounds consisting of three or more elements. Moreover, we will have to implement the second phase of our approach. This means that we have to specify a QLF to OKBR lexicon. Additionally, we will have to formalize and implement the inference and unification operations required to construct concepts from subconcepts.

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