Base_JSON/prefixA/json/A94/A94-1019.json

{
    "paper_id": "A94-1019",
    "header": {
        "generated_with": "S2ORC 1.0.0",
        "date_generated": "2023-01-19T01:13:40.762577Z"
    },
    "title": "Recycling Terms into a Partial Parser",
    "authors": [
        {
            "first": "Christian",
            "middle": [],
            "last": "Jacquemin",
            "suffix": "",
            "affiliation": {},
            "email": "[email protected]"
        }
    ],
    "year": "",
    "venue": null,
    "identifiers": {},
    "abstract": "Both full-text information retrieval and large scale parsing require text preprocessing to identify strong lexical associations in textual databases. In order to associate linguistic felicity with computational efficiency, we have conceived FASTR a unification-based parser supporting large textual and grammatical databases. The grammar is composed of term rules obtained by tagging and lemmatizing term lists with an online dictionary. Through FASTR, large terminological data can be recycled for text processing purposes. Great stress is placed on the handling of term variations through metarules which relate basic terms to their semantically close morphosyntactic variants. The quality of terminological extraction and the computational efficiency of FASTR are evaluated through a joint experiment with an industrial documentation center. The processing of two large technical corpora shows that the application is scalable to such industrial data and that accounting for term variants results in an increase of recall by 20%. Although automatic indexing is the most straightforward application of FASTR, it can be extended fruitfully to terminological acquisition and compound interpretation.",
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        "paper_id": "A94-1019",
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        "abstract": [
            {
                "text": "Both full-text information retrieval and large scale parsing require text preprocessing to identify strong lexical associations in textual databases. In order to associate linguistic felicity with computational efficiency, we have conceived FASTR a unification-based parser supporting large textual and grammatical databases. The grammar is composed of term rules obtained by tagging and lemmatizing term lists with an online dictionary. Through FASTR, large terminological data can be recycled for text processing purposes. Great stress is placed on the handling of term variations through metarules which relate basic terms to their semantically close morphosyntactic variants. The quality of terminological extraction and the computational efficiency of FASTR are evaluated through a joint experiment with an industrial documentation center. The processing of two large technical corpora shows that the application is scalable to such industrial data and that accounting for term variants results in an increase of recall by 20%. Although automatic indexing is the most straightforward application of FASTR, it can be extended fruitfully to terminological acquisition and compound interpretation.",
                "cite_spans": [],
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                "eq_spans": [],
                "section": "Abstract",
                "sec_num": null
            }
        ],
        "body_text": [
            {
                "text": "Large terminological databases are now available and can be used as lexicons in Natural Language Processing (NLP) systems aimed at terminology extraction. In FASTR term lists are transformed into large lexicalized grammars and are parsed with a robust and computationally tractable unification-based parser. Our method contrasts with pattern-matching techniques by offering an expressive and convenient descriptive framework. It also differs from a general multipurpose parser by an ability to recycle linguistic knowledge embodied in terminological data. Higher quality in terminological extraction is achieved thanks to a description of term variations.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Introduction",
                "sec_num": null
            },
            {
                "text": "Areas of application using such a tool for terminology extraction include automatic indexing through an assignment of text pointers to thesaurus entries, knowledge acquisition form textual databases, noun phrase structural disambiguation and machine translation with a specific concern for the translation of idioms, compounds and terms.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Introduction",
                "sec_num": null
            },
            {
                "text": "When designing any NLP system with large linguistic resources, there is tension between tractability and descriptive power. Finite state automata are efficient tools for lexical extraction. But their lack of convenience for information description makes the testing of different methodological choices difficult. Such a limitation is specifically problematic during the development stage. Symmetrically, unification-based parsers offer rich and conceptually tractable formalisms, but their computational cost is very high. The approach taken in FASTR is to use a convenient grammatical description stemming from PATR-H (Shieber 1986 ) associated with an optimized computational engine. Efficiency and constraint-based grammar formalism have motivated the acronym of the application (FAST + PATR-H) that stands for FAST TERM RECOGNIZER.",
                "cite_spans": [
                    {
                        "start": 619,
                        "end": 632,
                        "text": "(Shieber 1986",
                        "ref_id": "BIBREF12"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Introduction",
                "sec_num": null
            },
            {
                "text": "When terminology extraction is applied to automatic indexing, two measures are important: recall and precision. Precision is crucial for applications using acquisition methods which are subject to an excessive recall, blurring terminological entries with syntactic recurrences or semantic preferences. Conversely, in a knowledge-based method like FASTR, recall is a decisive evaluation of the coverage of the extraction. The recall rate mainly depends on the ability of the processor to extract term occurrences which differ from their description in the terminological base. With the purpose of enhancing the recall rate, FASTR includes a metagrammar used to generate term variant rules from term rules. Such an addition of robustness does not entail a degradation of precision because variations are restricted to a \"safe\" window bordered by the term components.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Introduction",
                "sec_num": null
            },
            {
                "text": "The formalism of FASTR is organized into three levels : a single word lexicon, a terminological grammar and a metagrammar for term variations. The initialization of FASTR consists of the description of the inflectional system of the language under study, the generation of a lexicon and a grammar from a list of terms with an on-line lexicon and the handcrafted creation of a set of paradigmatic metarules (about a hundred) which are refmed according to the experimental results.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Introduction",
                "sec_num": null
            },
            {
                "text": "Processing in FASTR starts with segmentation and stemming. During stemming, a few term rules are activated through a bottom-up filtering. Then, metarules are applied to these rules and yield transformed rules used to extract terms and their variants. For example, from the preceding sentence and from a grammar including term variant, the sequence terms and their variants would be extracted as a variation of term variant. The data required by FASTR consist of a declension file, an initial terminological database and an on-line dictionary to transform terms into compilable linguistic data. As far as human time is concerned, only a slight experimental tuning of the metarules is necessary.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Introduction",
                "sec_num": null
            },
            {
                "text": "The formalism of FASTR stems from PATR-H (Shieber 1986) . Rules are composed of a context-free portion describing the concatenation of the constituents and a set of equations constraining information of these constituents.",
                "cite_spans": [
                    {
                        "start": 41,
                        "end": 55,
                        "text": "(Shieber 1986)",
                        "ref_id": "BIBREF12"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "A Three-tier Formalism",
                "sec_num": null
            },
            {
                "text": "The description of a single word includes minimally the string of the word stem, a part-of-speech category and its inflection number. These three values are used to dynamically rebuild the different inflections of the word. They are automatically extracted from an on-line dictionary with morphological information. We currently use the DELAS dictionary of LADL laboratory (University of Paris 7). For example, rule (1) describes the noun ray, plural rays.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "A Three-tier Formalism",
                "sec_num": null
            },
            {
                "text": "(1) Word : 'ray' <cat> = 'N' <inflection> = 1.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "A Three-tier Formalism",
                "sec_num": null
            },
            {
                "text": "Terms are described by grammar rules. The formalism of PATR-H has been extended to support such additional facilities as rules with an extended domain of locality, structure disjunction and negative atomic values. Rule (2) represents the term IX ray] diffraction. This rule localizes the embedded structure X ray. Lexical anchors, indicated by the value of the feature lexicallzation, are used prior to the parsing phase for a selective bottom-up activation of the rules. For example, rule (2) is anchored to diffraction and is activated when this word is encountered in the input sentence.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "A Three-tier Formalism",
                "sec_num": null
            },
            {
                "text": "(2)",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "A Three-tier Formalism",
                "sec_num": null
            },
            {
                "text": "Rule : NI ---> (N2 ---> N3 N4) N 5 <N I label> = 'XRD' <N I metaLabel> = 'XX' <N I lexicalization> = 'Ns' <N 3 lenuna> = 'X' <N 3 inflection> = 7 <N 4 lemma> = 'ray' <N 4 inflection> = I <Ns lemma> = 'diffraction' <N5 inflection> = I.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "A Three-tier Formalism",
                "sec_num": null
            },
            {
                "text": "The third level of the formalism consists of a metagrammar. Metarules are composed of two contextfree descriptions : the source and the target and a set of equations constraining them. Information shared by the source and the target is embodied by identical symbols. For example, metarule (3) describes a coordination of a two-constituent term inserting a conjunction (except but) and a word (except a definite or indefinite determiner) between both constituents. When applied to rule (2), it outputs a novel rule which accepts X ray or neutron diffraction as a variant of X ray diffraction.",
                "cite_spans": [],
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                "eq_spans": [],
                "section": "A Three-tier Formalism",
                "sec_num": null
            },
            {
                "text": "( 3)Metarule : Coor{X 1 --> X 2 X 3)",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "A Three-tier Formalism",
                "sec_num": null
            },
            {
                "text": "= X I --~ X 2 C 3 X 4 X 3",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "A Three-tier Formalism",
                "sec_num": null
            },
            {
                "text": "\"'C' = conjunction \" <X 1 metaLabel> = 'XX'\" <Ca lemma> 1 'but .... I denotes inequality\"' <X 4 cat> I 'Dd .... Dd'= definite determiner\" <X4 cat> I 'Di'.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "A Three-tier Formalism",
                "sec_num": null
            },
            {
                "text": "\"'Di'= indefinite determiner\"",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "A Three-tier Formalism",
                "sec_num": null
            },
            {
                "text": "FASTR has been applied to the French and the English languages and can be easily extended to any language without word agglutination thanks to an external description of morphology. The suffix stripping operation precedes syntactic analysis and requires a dictionary of lemmas and a declension file (Savoy 1993) . Each entry in the dictionary has a basic stem and words with an irregular inflectional morphology such as mouse/mice have one or more auxiliary stems. Derivational links such as synapselsynaptic can also be accounted for through multi-valued part-of-speech categories such as noun-adjective. The declension file is illustrated by formulae (4) and (5). A set of features is provided for each inflectional case of each inflected category (e.g. (4) for nouns). A list of suffixes corresponds to each declension class (e.g. (5) for the fn'st two classes of nouns). ? 1 indicates the first auxiliary stem. The inflection class of a word is denoted by the value of the feature inflection in word rule (1) and term rule (2).",
                "cite_spans": [
                    {
                        "start": 299,
                        "end": 311,
                        "text": "(Savoy 1993)",
                        "ref_id": "BIBREF10"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Parsing Morphology",
                "sec_num": null
            },
            {
                "text": "\" The two cases of nouns \"",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Parsing Morphology",
                "sec_num": null
            },
            {
                "text": "(4) N[ 1 1 <number> = 'singular'. N[ 2 1 <number> = 'plural'. \" dog/dog-s (stem dog) \" (5) N[ 1 ] 0 s \" mouse/mice (stem mouse, aux. stem mice) \" N! 2 ] 0 ?1",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Parsing Morphology",
                "sec_num": null
            },
            {
                "text": "In order to prepare suffix stripping, a generalized lexicographic tree is built form the whole set of the reversed suffixes of the current language. Each inflected word is also reversed and all its endings corresponding to an actual suffix are removed. The corresponding stems are looked for in the dictionary. If one of their inflections is equal to the current inflected word, the features associated with the declension case are unified with the features of the lemma and attached to the inflected word. Thus, the morphological stemmer associates all its homographic inflections to an inflected word.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Parsing Morphology",
                "sec_num": null
            },
            {
                "text": "The term rules whose lexical anchor is equal to one of the lemmas in the input are activated and processed by a top-down algorithm. In order to ensure short parsing times, unification is delayed until rewriting is achieved. Whenever a rule fails to be parsed, it is repeatedly tried again on its variants generated by metarules.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Parsing Morphology",
                "sec_num": null
            },
            {
                "text": "Metarules can be straightforwardly described by embedding the formalism into a logical framework where rule generation by metarules is calculated through unification. With this aim in mind, the definitions of term rules and metarules given in the preceding part can be transformed into logical (in)equations by using the formulae of Kasper and Rounds (1986) . As in (Vijay-Shanker 1992), type variables whose denotations are sets of structures derived from non-terminals can be replaced by monoadic predicates. Individual variables that stand for individual feature structures are used to capture reentrance. For example, rule (2) is translated into formula (6). A monoadic predicate arity is added to restrict the application of metarules. 6XRD",
                "cite_spans": [
                    {
                        "start": 333,
                        "end": 357,
                        "text": "Kasper and Rounds (1986)",
                        "ref_id": "BIBREF6"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Term syntax and local syntax",
                "sec_num": null
            },
            {
                "text": "(x) ca cat(x) = 'N' ^ arlty(x) = 2 ^ lexicallzation(x) = x4 ^ metaLabel(x) = 'XX' ^ l (x) = x I ^ cat(xl) = 'N' ^ arity(xl) ---2 ^ 1 (x I) ---x 2 A 2(XI) -----X 3 ^ cat(x2) = 'N' ^ lemma(x 2) = 'X' ^ inflection(x2) = I ^ cat(x 3) = 'N'A lemma(x3) = 'ray' ^ inflection(x 3) = I ^ 2(x) = x 4 ^ cat(x4) = 'N' ^ lemma(x 4) ---'diffraction' ^ inflectlon(x4) = I",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Term syntax and local syntax",
                "sec_num": null
            },
            {
                "text": "Standard fixed-point semantics is associated to this syntax which is used to calculate the interpretation of such formulae. The denotation of a formula is an automaton calculated through an inductive interpretation of the terms it contains (Rounds and Manaster- Ramer 1987) . As a consequence of this mathematical formulation, the metarules are expressed as couples of monoadic predicates with shared variables, For example, the metanfle of coordination (3) is described by formula (7). The syntax of both sides of the metarule is identical to the syntax of rules except for the monoadic rule predicate p which is a variable. -, stands for negation.",
                "cite_spans": [
                    {
                        "start": 262,
                        "end": 273,
                        "text": "Ramer 1987)",
                        "ref_id": null
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Term syntax and local syntax",
                "sec_num": null
            },
            {
                "text": "Coor(p(y) ca arity(y)-2 ^ I (y) = Yl ^ 2(y) = Y2) = (Coot(p) (y) ca arity(y) = 4 ^ l(y) = Yl ^ 2(y) ~-Y3 A 3(y) = Y4 ^ 4(y) = Y2 ^ cat(y 3) = 'C' ^ -~(lemma(y4) --'but') ^ -~(cat(y4) = 'Di') ^ -,(cat(y 4) = 'Dd') )",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Term syntax and local syntax",
                "sec_num": null
            },
            {
                "text": "The result of the application of a metarule to a rule is calculated in two steps. Firstly, the left-hand-side of the metarule is unified with the rule. If unification falls, no output rule is generated. Otherwise, let \u00a2~ be the substitution providing the unification. Then, the formula of the transformed rule is equal to the right-hand-side of the metarule, where the variables are substituted according to s. The computational implementation is straightforwardly derived from this calculus. For example, metarule (7) applies to rule (6) with the substitution ct (8) and yields the transformed rule (9) whose PATR-H expression is (10). The mapping performed by the metarules in FASTR differs from the definition of metarules in GPSG (Gazdar et al. 1985) on the following points :",
                "cite_spans": [
                    {
                        "start": 734,
                        "end": 754,
                        "text": "(Gazdar et al. 1985)",
                        "ref_id": null
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Term syntax and local syntax",
                "sec_num": null
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            {
                "text": "\u2022 The matching of the input rule and the source is replaced by their unification. The correspondence between source and target is achieved by identical variables shared by both sides of the metarule. \u2022 In GPSG, when input rule and target disagree about the value of some feature, the target always wins. In FASTR, the target wins if its value for this feature is independent of its source. Conversely, ff source and target share this value, the unification of the source and the rule falls and no output is provided. \u2022 The metavariable W used in GPSG and standing for a set of categories is not available in FASTR. However, an empty category in the context-free skeleton can stand for any subtree of the original rule. Thus, variable Yl from metarule (7), associated to X 2 in formula (3), stands for the subterm X ray when applied to rule (6). When implementing metarules in a grammar parser, there are two possibilities for the time to apply the metarules to a rule. The compile-time application calculates all the images of all the rules in the grammar prior to parsing. In the run-time approach, metarules are dynamically applied to the active rules during parsing. Weisweber and Preu6 (1992) demonstrate that there is no difference in complexity between both approaches. Moreover, in the compile-time approach, metarules generate a huge set of transformed rules which may make the parsing process totally inefficient. Due to the very large size of our grammar, we have opted for the dynamic approach. The computational performances of the application reported in (Jacquemin 1994a) indicate that the parser only spends 10% of its time in generating metarules and fully justify the run-time approach.",
                "cite_spans": [
                    {
                        "start": 1170,
                        "end": 1196,
                        "text": "Weisweber and Preu6 (1992)",
                        "ref_id": null
                    },
                    {
                        "start": 1568,
                        "end": 1585,
                        "text": "(Jacquemin 1994a)",
                        "ref_id": "BIBREF5"
                    }
                ],
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                "eq_spans": [],
                "section": "Term syntax and local syntax",
                "sec_num": null
            },
            {
                "text": "The keystone of the computational tractability is lexicalization which allows for a bottom-up filtering of the rules before parsing. It is completed by fast mechanisms for data access such as a B-Tree (for the disk resident lexicon of single words) and a Hash-Code table (for the memory resident stop words).",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Computational Lexicalization",
                "sec_num": null
            },
            {
                "text": "The formalism of FASTR is lexicalized in the sense of Schabes and Joshi (1990) because it is composed of rules associated with each lexical item which is the anchor of the corresponding rules. The parsing algorithm for lexicalized grammars takes advantage of lexicalization through a two-step strategy. The first step is a selection of the rules linked to the lexical items in the input. The second step parses the input with a grammar restricted to the filtered rules. In case of rules with multiple lexical items such as the rules representing multi-word terms, the anchor can be any of the lexical items. For example, the term aortic disease can be anchored either to aortic or to disease. In Jacquemin (1994b) , an algorithm for optimizing the determination of computational anchors is described. It yields a uniform distribution of the rules on to the lexical items with respect to a given weighting function. A comparison between the \"natural\" lexicalization on the head nouns and the optimized one has been made with FASTR. It shows that the rules filtered by the optimized lexicalization represent only 57% of the rules selected by the natural lexicalization and ensure a 2.6-time higher parsing speed.",
                "cite_spans": [
                    {
                        "start": 54,
                        "end": 78,
                        "text": "Schabes and Joshi (1990)",
                        "ref_id": "BIBREF11"
                    },
                    {
                        "start": 696,
                        "end": 713,
                        "text": "Jacquemin (1994b)",
                        "ref_id": null
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                ],
                "ref_spans": [],
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                "section": "Computational Lexicalization",
                "sec_num": null
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            {
                "text": "The computational performances of parsing with FASTR mainly depend on the size of the grammar (see Figure 1 ). The parsing speed with a 71,623-rule terminological grammar, a 38,536-word lexicon and 110 metarules is 2,562 words/minute on a Sparc 2 workstation (real time), As 71,623 terms is a reasonable size for a real-word multi-domain list of terms (for example WordNet currently includes 35,155 synonyms sets), a workstation is well-suited for processing large corpora with such terminological databases. documentation center INIST/CNRS : a 118,563-word corpus on metallurgy [METAL] and a 1.5-million word medical corpus [MEDIC] . The laboratory of INIST/CNRS has achieved tagging and lemmatization of terms and has evaluated the results of the indexing provided by FASTR.",
                "cite_spans": [
                    {
                        "start": 625,
                        "end": 632,
                        "text": "[MEDIC]",
                        "ref_id": null
                    }
                ],
                "ref_spans": [
                    {
                        "start": 99,
                        "end": 107,
                        "text": "Figure 1",
                        "ref_id": "FIGREF2"
                    }
                ],
                "eq_spans": [],
                "section": "Computational Lexicalization",
                "sec_num": null
            },
            {
                "text": "In this experiment, the metagrammar consists of positive paradigmatic metarules (e.g. (11)) and filtering negative metarules rejecting the spurious variations extracted by the positive ones (e.g. 12 (Jacquemin and Royaut6 1994) . An evaluation of terminology extraction on corpus [METAL] indicates that term variations represent 16.7% of multi-word term occurrences extracted by FASTR (an account for term variants increases recall by 20%). The three kinds of variants retrieved through metarules are coordinations (2%), modifier insertions (8.3%) and permutations (6.4%). See Figure 3 for examples. Elisions such as Kerrr ma~netoootical effect ---> Kerr effect are not accounted for because our local approach to variation is not appropriate to elliptic references. In this framework, FASTR retrieves 74.9% of the term variants with a precision of 86.7%. These results confirm the substantial gain in recall obtained by accounting for term variants in automatic indexing. A better precision could be reached through a more accurate description of permutation. An improvement in term variant recall requires the handling of elision.",
                "cite_spans": [
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                        "start": 199,
                        "end": 227,
                        "text": "(Jacquemin and Royaut6 1994)",
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                        "start": 280,
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                        "text": "[METAL]",
                        "ref_id": null
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                "ref_spans": [
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                        "start": 577,
                        "end": 585,
                        "text": "Figure 3",
                        "ref_id": null
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                "section": "Computational Lexicalization",
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            {
                "text": "Firstly, our formalism is inspired by two fields of lexicalized and logical tree formalisms. The f'n'st one is the general framework of Lexicalized Tree Adjoining Grammar (LTAG) which has shown to be fruitful for the description of idioms (Abeill6 and Schabes 1989) . The second one is the important extension of Tree Adjoining Grammar (TAG) to a logical framework (Vijay-Shanker 1992) which contrasts with the traditional approach that operations in a TAG combine trees. From these works, we have adopted the constraint of LTAG which states that rules must have at least one lexical frontier node together with the logical representation of Vijay-Shanker (1992) where rules are not restricted to immediate dependency. The lexicalized tree grammar is motivated by the domain to be described : terms mainly consist of compounds with an internal structure and lexical constituents. The logical formalism provides us with a straightforward extension to metandes. Secondly, our approach to text processing is a form of partial parsing. A current trend in large scale NLP system (Jacobs 1992) refuses to consider parsing as an exhaustive derivation of a very large grammar which would process any encountered sentence. To alleviate these problems parsing should be planned as the cooperation of several methods such as text preprocessing, parsing by chunks, multiple-step partial parsing, shallow parsing.., etc. The scope of the preprocessing task is \"abstract [ing] idiosyncrasies, highlight[ing] regularities, and, in general feed[ing] digested text into the unification parser\" (Zernik 1992) . With this aim in mind FASTR brings forth occurrences of complex lexical entries and their local variations. It is adapted to integration in a multi-step parsing strategy. It takes as input a raw corpus and yields chunks corresponding to partial parses. This output can be fed into a following module or reprocessed with more precise metarules.",
                "cite_spans": [
                    {
                        "start": 239,
                        "end": 265,
                        "text": "(Abeill6 and Schabes 1989)",
                        "ref_id": "BIBREF0"
                    },
                    {
                        "start": 1074,
                        "end": 1087,
                        "text": "(Jacobs 1992)",
                        "ref_id": "BIBREF3"
                    },
                    {
                        "start": 1457,
                        "end": 1533,
                        "text": "[ing] idiosyncrasies, highlight[ing] regularities, and, in general feed[ing]",
                        "ref_id": null
                    },
                    {
                        "start": 1577,
                        "end": 1590,
                        "text": "(Zernik 1992)",
                        "ref_id": "BIBREF16"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Related Work",
                "sec_num": null
            },
            {
                "text": "Thirdly, our research on term extraction places great stress on term variations. The most direct precursors of the use of term variation in information retrieval are Sparck Jones and Tait (1984) . These authors advocate the systematic generation of syntactic term variants in query processing. Their approach, however, makes the assumption that only semantic equivalent variant should be generated and that each of the words in a variant should be given instead of allowing paradignmtic places. They only account for restricted associations such as information retrieval/retrieval of information. Strzalkowski and Vauthey (1992) follow the way suggested by Sparck Jones and Tait (1984) at the end of their paper. Instead of generating term variants in a query, they look for different term occurrences in text documents analyzed by a general multipurpose parser. Their parse trees are composed of head/modifier relations of four categories. These four classes account for most of the syntactic variants of two-word terms into pairs with compatible semantic content such as information retrieval/information retrieval system~retrieval of information from databases... We think however that most of these variants can be extracted without parsing the whole sentence. They can be detected safely through a local parse with a noun-phrase micro-syntax.",
                "cite_spans": [
                    {
                        "start": 173,
                        "end": 194,
                        "text": "Jones and Tait (1984)",
                        "ref_id": null
                    },
                    {
                        "start": 597,
                        "end": 628,
                        "text": "Strzalkowski and Vauthey (1992)",
                        "ref_id": "BIBREF13"
                    },
                    {
                        "start": 664,
                        "end": 685,
                        "text": "Jones and Tait (1984)",
                        "ref_id": null
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Related Work",
                "sec_num": null
            },
            {
                "text": "Although applied straightforwardly to automatic indexing, FASTR can be extended to terminology acquisition through a bootstrapping method where new terms are acquired by observing the variations of controlled terms in corpora. Figure 3 reports four occurrences of term variants retrieved through three metarules belonging to three different families. Each of these occurrences yields a novel candidate term which either already belongs to the terminology or can be added after validation.",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 227,
                        "end": 235,
                        "text": "Figure 3",
                        "ref_id": null
                    }
                ],
                "eq_spans": [],
                "section": "Extensions and Conclusion",
                "sec_num": null
            },
            {
                "text": "A second extension of FASTR concerns acquisition of noun phrase interpretation from a corpus. Observation of variation is an opportunity to find objective linguistic clues which denote the semantic relation between both words of a binominal compound. For example, cell into a metastatic tumor is a permutation of tumor cell involving the preposition into. Figure 4 lists four N cell terms for which more than four permutations cell Prep X N have been encountered in corpus [MEDIC] .",
                "cite_spans": [
                    {
                        "start": 473,
                        "end": 480,
                        "text": "[MEDIC]",
                        "ref_id": null
                    }
                ],
                "ref_spans": [
                    {
                        "start": 356,
                        "end": 364,
                        "text": "Figure 4",
                        "ref_id": "FIGREF3"
                    }
                ],
                "eq_spans": [],
                "section": "Extensions and Conclusion",
                "sec_num": null
            },
            {
                "text": "The prepositions found in more than one permutation are followed by their number of occurrences. For example, the prepositions encountered in the permutations of blood cell are from, in, into and on. These four prepositions denote a relation of spatial inclusion of a trajector cell into a landmark blood (Langacker 1987) . FASTR can play a crucial role in other text-based intelligent tasks. This part has sketched out a picture of incremental terminological acquisition and noun-phrase understanding through the analysis of term variants. As Resnik (1993) points out, large-scale knowledge sources can be used as a source of lexical information. Similarly, our approach to corpus linguistics makes a extensive use of terminological data and investigates systematically and precisely the variations of terms in technical corpora. The next natural step in term and compound processing is to provide FASTR with a learning ability. With this aim in mind, we are currently investigating two novel research directions : firstly, a hybridisation of FASTR with a connectionist model dedicated to nominal composition (Jacquemin 1993) and, secondly, a cooperation between FASTR and LEXTER (Bourigault 1993 ) a tool for term acquisition through the filtering of part-of-speech patterns.",
                "cite_spans": [
                    {
                        "start": 305,
                        "end": 321,
                        "text": "(Langacker 1987)",
                        "ref_id": null
                    },
                    {
                        "start": 544,
                        "end": 557,
                        "text": "Resnik (1993)",
                        "ref_id": "BIBREF8"
                    },
                    {
                        "start": 1110,
                        "end": 1126,
                        "text": "(Jacquemin 1993)",
                        "ref_id": "BIBREF4"
                    },
                    {
                        "start": 1181,
                        "end": 1197,
                        "text": "(Bourigault 1993",
                        "ref_id": "BIBREF1"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Extensions and Conclusion",
                "sec_num": null
            }
        ],
        "back_matter": [
            {
                "text": "I would like to thank Jean Royaut6 from INIST/CNRS for his helpful and friendly collaboration on this project. Many thanks also to Benoit Habert from ENS Fontenay for numerous constructive discussions.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Acknowledgement",
                "sec_num": null
            }
        ],
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        "ref_entries": {
            "FIGREF0": {
                "uris": null,
                "type_str": "figure",
                "num": null,
                "text": "[y = x, XRD / p, x I = Yl, x4 = Y2I   (9)   Coor(XRD)(x) ca cat(x) ---'N' ^ arlty(x) ---4 A lexlcaIlzation(x) = x 4 A metaLabel(x) ---'XX' ^ l(x) = x I ^ cat(xl) = 'N' ^ arlty(x I) = 2 ^ l (Xl) = x2 ^ 2(Xl) = x3 ^ cat(x2) = 'N' ^ lemma(x 2) = 'X' ^inflection(x2) = I ^ cat(x3} = 'N'^ lemma(x3) = 'ray' A inflection(x3) = I ^ 4(x) = x4 ^ cat(x4) = 'N' ^ lemma(x4) = 'diffraction' ^ Inflection(x4) -I ^ 2(y) = Y3 ^ 3(y) = Y4 ^ cat(y3) = 'C' (I0) Rule : NI ---> (N2 ---> N3 N4) C6 N7 N s <NI label> = 'Coor(XRD)' <N l metaLabel> = 'XX' <N I lexicallzation> = 'N 5' <N 3 lernma> = 'X' <N 3 inflection> = l <N4 lemma> = 'ray' <N 4 inflection> = I <N s lemma> = 'diffraction' <N 5 inflection> = I."
            },
            "FIGREF1": {
                "uris": null,
                "type_str": "figure",
                "num": null,
                "text": "). Examples of variations from [MEDIC] accepted by (11) or rejected by (12) are shown in Figure 2. (11) Metarule Coor( Xl ---> X2 X3 ) =x, -~x~. c~ x4 x3 <X l metaLabel> = 'XX'. (12) Metarule NegCoor( Xi ---> X2 X3 ) = x, ~ x2 c3 x4 xs <Xi metaLabel> = 'XX' <X4 cat> = 'P .... P' = preposition\" <X 4 cat> = 'Dd' <X 4 cat> = 'Di'."
            },
            "FIGREF2": {
                "uris": null,
                "type_str": "figure",
                "num": null,
                "text": "Parsing speed of FASTR (Sparc 2, real time) Application to Automatic Indexing A list of 71,623 multi-domain terms and two corpora of scientific abstracts have been provided by the Negative metarules are used instead of negative constraints such as the ones stated in (3) to keep a trace of the rejected variations. More details about this description are reported in"
            },
            "FIGREF3": {
                "uris": null,
                "type_str": "figure",
                "num": null,
                "text": "Noun phrase interpretation through variationAlthough initially devised for automatic indexing,"
            },
            "TABREF0": {
                "type_str": "table",
                "html": null,
                "text": "], in [13], into, on Tumor cell in [3], from [4], into, with, of",
                "content": "<table><tr><td>Term</td><td>Prepositions</td><td/></tr><tr><td>Membrane cell</td><td>in [4], into, to</td><td/></tr><tr><td>Myeloid cell</td><td>of [3], from</td><td/></tr><tr><td>Blood cell</td><td>from [8</td><td/></tr><tr><td/><td>Variation</td><td>Candidate term</td></tr><tr><td>water absorption</td><td>water and sodium absorption (coordination)</td><td>sodium absorption</td></tr><tr><td>CentraI Africa</td><td>Central and West Africa (coordination)</td><td>WestAfrica</td></tr><tr><td>controlled delivery</td><td>controlled drug delivery (insertion)</td><td>drug delivery</td></tr><tr><td>magnetic coupling</td><td>magnetic transcutaneous coupling (insertion)</td><td>transcutaneous coupling</td></tr><tr><td>information access</td><td>access to lexical information (permutation)</td><td>lexical information</td></tr><tr><td>wave effect</td><td>effect of short wave (permutation)</td><td>short wave</td></tr><tr><td/><td colspan=\"2\">Figure 3. Acquisition of candidate terms through variation</td></tr></table>",
                "num": null
            }
        }
    }
}