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<Paper uid="P04-1028">
  <Title>Mining metalinguistic activity in corpora to create lexical resources using Information Extraction techniques: the MOP system</Title>
  <Section position="4" start_page="0" end_page="0" type="metho">
    <SectionTitle>
2 Metalinguistic Operation Processor
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    <Paragraph position="0"> research papers. Section 2 will lay out the theory, methodology and the empirical research grounding the application, while Section 3 will describe the first phase of the MOP tasks: accurate location of good candidate metalinguistic sentences for further processing. We experimented both with manually coded rules and with learning algorithms for this task. Section 4 focuses on the problem of identifying and organizing into a useful database structure the different linguistic constituents of the candidate predications, a phase similar to what are known in the IE literature as Named-Entity recognition, Element and Scenario template fill-up tasks. Finally, Section 5 discusses results and problems of our experiments, as well as future lines of research.</Paragraph>
  </Section>
  <Section position="5" start_page="0" end_page="0" type="metho">
    <SectionTitle>
2 Metalanguage and term evolution in scien-
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    <Section position="1" start_page="0" end_page="0" type="sub_section">
      <SectionTitle>
tific disciplines
2.1 Explicit Metalinguistic Operations
</SectionTitle>
      <Paragraph position="0"> Preliminary empirical work to explore how researchers modify the terminological framework of their highly complex conceptual systems, included manual review of a corpus of 19 sociology articles (138,183 words) published in various British, American and Canadian academic journals with strict peer-review policies. We look at how term manipulation was done as well as how metalinguistic activity was signaled in text, both by lexical and paralinguistic means. Some of the indicators found included verbs and verbal phrases like called, known as, defined as, termed, coined, dubbed, and descriptors such as term and word. Other non-lexical markers included quotation marks, apposition and text formatting.</Paragraph>
      <Paragraph position="1"> A collection of potential metalinguistic patterns identified in the exploratory Sociology corpus was expanded (using other verbal tenses and forms) to 116 queries sent to the scientific and learned domains of the British National Corpus. The resulting 10,937 sentences were manually classified as metalinguistic or otherwise, with 5,407 (49.6% of total) found to be truly metalinguistic sentences.</Paragraph>
      <Paragraph position="2"> The presence of three components described below (autonym, informative segment and markers/operators) was the criteria for classification. Reliability of human subjects for this task has not been reported in the literature, and was not evaluated in our experiments.</Paragraph>
      <Paragraph position="3"> Careful analysis of this extensive corpus presented some interesting facts about what we have termed &amp;quot;Explicit Metalinguistic Operations&amp;quot; (or EMOs) in specialized discourse: A) EMOs usually do not follow the genusdifferentia scheme of aristotelian definitions, nor conform to the rigid and artificial structure of dictionary entries. More often than not, specific information about language use and term definition is provided by sentences such as: (1) This means that they ingest oxygen from the air via fine hollow tubes, known as tracheae, in which the term trachea is linked to the description fine hollow tubes in the context of a globally non-metalinguistic sentence. Partial and heterogeneous information, rather that a complete definition, are much more common.</Paragraph>
      <Paragraph position="4"> B) Introduction of metalinguistic information in discourse is highly regular, regardless of the specific domain. This can be credited to the fact that the writer needs to mark these sentences for special processing by the reader, as they dissect across two different semiotic levels: a metalanguage and its object language, to use the terminology of logic where these concepts originate.3 Its constitutive markedness means that most of the times these sentences will have at least two indicators present, for example a verb and a descriptor, or quotation marks, or even have preceding sentences that announce them in some way. These formal and cognitive properties of EMOs facilitate the task of locating them accurately in text.</Paragraph>
      <Paragraph position="5"> C) EMOs can be further analyzed into 3 distinct components, each with its own properties and linguistic realizations: i) An autonym (see note 3): One or more self-referential lexical items that are the logical or grammatical subject of a predication that needs not be a complete grammatical sentence.</Paragraph>
      <Paragraph position="6"> 3 At a very basic semiotic level natural language has to be split (at least methodologically) into two distinct systems that share the same rules and elements: a metalanguage, which is a language that is used to talk about another one, and an object language, which in turn can refer to and describe objects in the mind or in the physical world. The two are isomorphic and this accounts for reflexivity, the property of referring to itself, as when linguistic items are mentioned instead of being used normally in an utterance. Rey-Debove (1978) and Carnap (1934) call this condition autonymy.</Paragraph>
      <Paragraph position="7"> ii) An informative segment: a contribution of relevant information about the meaning, status, coding or interpretation of a linguistic unit. Informative segments constitute what we state about the autonymical element.</Paragraph>
      <Paragraph position="8"> iii) Markers/Operators: Elements used to mark or made prominent whole discourse operation, on account of its non-referential, metalinguistic nature. They are usually lexical, typographic or pragmatic elements that articulate autonyms and informative segments into a predication.</Paragraph>
      <Paragraph position="9"> Thus, in a sentence such as (2), the [autonym] is marked in square brackets, the {informational segment} in curly brackets and the &lt;marker- null operators&gt; in angular brackets: (2) {The bit sequences representing quanta of knowledge} &lt;will be called &amp;quot;&gt;[Kenes]&lt;&amp;quot;&gt;, {a neologism intentionally similar to 'genes'}.</Paragraph>
      <Paragraph position="10"> 2.2 Defaults, knowledge and knowledge of language  The 5,400 metalinguistic sentences from our BNC-based test corpus (henceforth, the EMO corpus) reflect an important aspect of scientific sublanguages, and of the scientific enterprise in general. Whenever scientists and scholars advance the state of the art of a discipline, the language they use has to evolve and change, and this build-up is carried out under metalinguistic control. Previous knowledge is transformed into new scientific common ground and ontological commitments are introduced and defended when semantic reference is established. That is why when we want to structure and acquire new knowledge we have to go through a resource-costly cognitive process that integrates, within coherent conceptual structures, a considerable amount of new and very complex lexical items and terms.</Paragraph>
      <Paragraph position="11"> It has to be pointed out that non-specialized language is not abundant4 in these kinds of meta-linguistic exchanges because (unless in the context of language acquisition) we usually rely on a lexical competence that, although subsequently modified and enhanced, reaches the plateau of a generalized lexicon relatively early in our adult life. Technical terms can be thought of as semantic anomalies, in the sense that they are ad hoc  constructs strongly bounded to a model, a domain or a context, and are not, by definition, part of the far larger linguistic competence from a first native language. The information provided by EMOs is not usually inferable from previous one available to the speaker's community or expert group, and does not depend on general language competence by itself, but nevertheless is judged important and relevant enough to warrant the additional processing effort involved.</Paragraph>
      <Paragraph position="12"> Conventional resources like lexicons and dictionaries compile established meaning definitions. They can be seen as repositories of the default, core lexical information of words or terms used by a community (that is, the information available to an average, idealized speaker). A Metalinguistic Information Database (MID), on the other hand, compiles the real-time data provided by metalanguage analysis of leading-edge research papers, and can be conceptualized as an anti-dictionary: a listing of exceptions, special contexts and specific usage, of instances where meaning, value or pragmatic conditions have been spotlighted by discourse for cognitive reasons. The non-default and highly relevant information from MIDs could provide the material for new interpretation rules in reasoning applications, when inferences won't succeed because the states of the lexicoconceptual system have changed. When interpreting text, regular lexical information is applied by default under normal conditions, but more specific pragmatic or discursive information can override it if necessary, or if context demands so (Lascarides &amp; Copestake, 1995). A neologism or a word in an unexpected technical sense could stump a NLP system that assumes it will be able to use default information from a machine-readable dictionary. null</Paragraph>
    </Section>
  </Section>
  <Section position="6" start_page="0" end_page="0" type="metho">
    <SectionTitle>
3 Locating metalinguistic information in
</SectionTitle>
    <Paragraph position="0"> text: two approaches When implementingan IE application to mine metalinguistic information from text, the first issue to tackle is how to obtain a reliable set of candidate sentences from free text for input into the next phases of extraction. From our initial corpus analysis we selected 44 patterns that showed the best reliability for being EMO indicators. We start our processing5 by tokenizing text, which then is 5 Our implementation is Python-based, using the run through a cascade of finite-state devices based on identification patterns that extract a candidate set for filtering. Our filtering strategies in effect distinguish between useful results such as (3) from non-metalinguistic instances like (4):  (3) Since the shame that was elicited by the coding procedure was seldom explicitly mentioned by the patient or the therapist, Lewis called it unacknowledged shame.</Paragraph>
    <Paragraph position="1"> (4) It was Lewis (1971;1976) who called attention  to emotional elements in what until then had been construed as a perceptual phenomenon .</Paragraph>
    <Paragraph position="2"> For this task, we experimented with two strategies: First, we used corpus-based collocations to discard non-metalinguistic instances, for example the presence of attention in sentence (4) next to the marker called. Since immediate co-text seems important for this classification task, we also implemented learning algorithms that were trained on a subset from our EMO corpus, using as vectors either POS tags or word forms, at 1, 2, and 3 positions adjacent before and after our markers.</Paragraph>
    <Paragraph position="3"> These approaches are representative of wider paradigmatic approaches to NLP: symbolic and statistic techniques, each with their own advantages and limitations. Our evaluations of the MOP system are based on test runs over 3 document sets: a) our original exploratory corpus of sociology research papers [5581 sentences, 243 EMOs]; b) an online histology textbook [5146 sentences, 69 EMOs] ; and c) a small sample from the MedLine abstract database [1403 sentences, 10 EMOs].</Paragraph>
    <Paragraph position="4"> Using collocational information, our first approach fared very well, presenting good precision numbers, but not so encouraging recall. The sociology corpus, for example, gave 0.94 precision (P) and 0.68 recall (R), while the histology one presented 0.9 P and 0.5 R. These low recall numbers reflect the fact that we only selected a subset of the most reliable and common metalinguistic patterns, and our list is not exhaustive. Example (5) shows one kind of metalinguistic sentence (with a copulative structure) attested in corpora, NLTK toolkit (nltk.sf.net) developed by E. Loper and S. Byrd at the University of Pennsylvania, although we have replaced stochastic POS taggers with an implementation of the Brill algorithm by Hugo Liu at MIT. Our output files follow XML standards to ensure transparency, portability and accessibility but that the system does not attempt to extract or process: (5) &amp;quot;Intercursive&amp;quot; power , on the other hand , is power in Weber's sense of constraint by an actor or group of actors over others.</Paragraph>
    <Paragraph position="5"> In order to better compare our two strategies, we decided to also zoom in on a more limited sub-set of verb forms for extraction (namely, calls, called, call), which presented ratios of metalinguistic relevance in our MOP corpus, ranging from 100% positives (for the pattern so called + quotation marks) to 77% (called, by itself) to 31% (call). Restricted to these verbs, our metrics show precision and recall rates of around 0.97, and an overall F-measure of 0.97.6 Of 5581 sentences (96 of which were metalinguistic sentences signaled by our cluster of verbs), 83 were extracted, with 13 (or 15.6% of candidates) filtered-out by collocations. null For our learning experiments (an approach we have called contextual feature language models), we selected two well-known algorithms that showed promise for this classification task.7 The naive Bayes (NB) algorithm estimates the conditional probability of a set of features given a label, using the product of the probabilities of the individual features given that label. The Maximum Entropy model establishes a probability distribution that favors entropy, or uniformity, subject to the constraints encoded in the feature-label correlation. When training our ME classifiers, Generalized (GISMax) and Improved Iterative Scaling (IIS-Max) algorithms are used to estimate the optimal maximum entropy of a feature set, given a corpus.</Paragraph>
    <Paragraph position="6"> 1,371 training sentences were converted into labeled vectors, for example using 3 positions and POS tags: ('VB WP NNP', 'calls', 'DT NN NN') /'YES'@[102]. The different number of positions considered to the left and right of the markers in our training corpus, as well as the nature of the features selected (there are many more word-types than POS tags) ensured that our 3-part vector introduced a wide range of features against our 2 possible YES-NO labels for processing by our algorithms. Although our test runs using only collocations showed initially that structural regulari6 With a ss factor of 1.0, and within the sociology document set 7 see Ratnaparkhi (1997) and Berger et al. (1996) for a formal description of these algorithms ties would perform well, both with our restricted lemma cluster and with our wider set of verbs and markers, our intuitions about improvement with more features (more positions to the right of left of the markers) or a more controlled and grammatically restricted environment (a finite set of surrounding POS tags), turned out to be overly optimistic. Nevertheless, stochastic approaches that used short range features did perform very well, in line with the hand-coded approach.</Paragraph>
    <Paragraph position="7"> The results of the different algorithms, restricted to the lexeme call, are presented in Table 1, while Figures 1 and 2 present best results in the learning experiments for the complete set of patterns used in the collocation approach, over two of our evaluation corpora.</Paragraph>
    <Paragraph position="9"/>
    <Paragraph position="11"> Figures 1 &amp; 2. Best results for filtering algorithms.8 Both Knowledge-Engineering and supervised learning approaches can be adequate for extraction of metalinguistic sentences, although learning algorithms can be helpful when procedural rules have not been compiled; they also allow easier transport of systems to new thematic domains. We plan further research into stochastic approaches to fine tune them for the task.</Paragraph>
    <Paragraph position="12"> One issue that merits special attention is why some of the algorithms and features work well with one corpus, but not so well with another.</Paragraph>
    <Paragraph position="13"> This fact is in line with observations in Nigam et al. (1999) that naive Bayes and Maximum Entropy do not show fundamental baseline superiorities, but are dependent on other factors. A hybrid approach that combines hand-crafted collocations with classifiers customized to each pattern's behavior and morpho-syntactic contexts in corpora might offer better results in future experiments.</Paragraph>
    <Paragraph position="14"> 4 Processing EMOs to compile metalinguistic information databases Once we have extracted candidate EMOs, the MOP system conforms to a general processing architecture shown in Figure 3. POS tagging is followed by shallow parsing that attempts limited PP-attachment. The resulting chunks are then tagged semantically as Autonyms, Agents, Markers, Anaphoric elements or simply as Noun Chunks, 8 Legend: P: Precision; R: Recall; F: F-Measure. NB: naive Bayes; IIS: Maximum Entropy trained with Improved Iterative Scaling; GIS: Maximum Entropy trained with Generalized Iterative Scaling. (Positions/Feature type) using heuristics based on syntactic, pragmatic and argument structure observation of the extraction patterns.</Paragraph>
    <Paragraph position="15"> Next, a predicate processing phase selects the most likely surface realization of informational segments, autonyms and makers-operators, and proceeds to fill the templates in our databases.</Paragraph>
    <Paragraph position="16"> This was done by following different processing routes customized for each pattern using corpus analysis as well as FrameNet data from Name conferral and Name bearing frames to establish relevant arguments and linguistic realizations.</Paragraph>
    <Paragraph position="17">  As mentioned earlier, informational segments present many realizations that distance them from the clarity, completeness and conciseness of lexicographic entries. In fact, they may show up as full-fledged clauses (6), as inter- or intra-sentential anaphoric elements (7 and 8, the first one a relative clause), supply a categorization descriptor (9), or even (10) restrict themselves se- null mantically to what we could call a sententiallyunrealized &amp;quot;existential variable&amp;quot; (with logical form &gt;x) indicating only that certain discourse entity is being introduced.</Paragraph>
    <Paragraph position="18"> (6) In 1965 the term soliton was coined to describe waves with this remarkable behaviour.</Paragraph>
    <Paragraph position="19"> (7) This leap brings cultural citizenship in line with what has been called the politics of citizenship .</Paragraph>
    <Paragraph position="20"> (8) They are called &amp;quot;endothermic compounds.&amp;quot; (9) One of the most enduring aspects of all social theories are those conceptual entities known as structures or groups.</Paragraph>
    <Paragraph position="21"> (10) A &gt;x so called cell-type-specific TF can be  used by closely related cells, e.g., in erythrocytes and megakaryocytes.</Paragraph>
    <Paragraph position="22"> We have not included an anaphora-resolution module in our present system, so that instances 7, 8 and 10 will only display in the output as unresolved surface element or as existential variable place-holders,9 but these issues will be explored in future versions of the system. Nevertheless, much more common occurrences as in (11) and (12) are enough to create MIDs quite useful for lexicographers and for NLP lexical resources.</Paragraph>
    <Paragraph position="23"> (11) The Jovian magnetic field exerts an influence out to near a surface, called the &amp;quot;magnetopause&amp;quot;.</Paragraph>
    <Paragraph position="24"> (12) Here we report the discovery of a soluble decoy receptor, termed decoy receptor 3 (DcR3)...</Paragraph>
    <Paragraph position="25"> The correct database entry for example 12 is presented in Table 4.</Paragraph>
    <Paragraph position="26"> Reference: MedLine sample # 6 Autonym: decoy receptor 3 (DcR3) Information a soluble decoy receptor  The final processing stage presents metrics shown in Figure 4, using a ss factor of 1.0 to estimate F-measures. To better reflect overall performance in all template slots, we introduced a threshold of similarity of 65% for comparison between a golden standard slot entry and the one provided by the application. Thus, if the autonym or the informational segment is at least 2/3 of the correct response, it is counted as a positive, in many cases leveling the field for the expected errors in the prepositional phrase- or acronymattachment algorithms, but accounting for a (basically) correct selection of superficial sentence segments.</Paragraph>
    <Paragraph position="27"> 9 For sentence (8) the system would retrieve a previous sentence: (&amp;quot;A few have positive enthalpies of formation&amp;quot;). to define &amp;quot;endothermic compounds&amp;quot;.</Paragraph>
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