| { |
| "paper_id": "A97-1021", |
| "header": { |
| "generated_with": "S2ORC 1.0.0", |
| "date_generated": "2023-01-19T02:14:29.299696Z" |
| }, |
| "title": "Large-Scale Acquisition of LCS-Based Lexicons for Foreign Language Tutoring", |
| "authors": [ |
| { |
| "first": "Bonnie", |
| "middle": [ |
| "J" |
| ], |
| "last": "Dorr", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "University of Maryland College Park", |
| "location": { |
| "postCode": "20742", |
| "region": "MD", |
| "country": "USA" |
| } |
| }, |
| "email": "" |
| } |
| ], |
| "year": "", |
| "venue": null, |
| "identifiers": {}, |
| "abstract": "We focus on the probleln of building large repositories of le.rical coJtceplual structure (LCS) representations for verbs in multiple languages. One of the main results of this work is the definition of a relat, ion between broad semantic classes and LCS meaniug components. Our acquisition program-LEXICALL-takes, as input, the result of previous work on verb classification and thematic grid tagging, and outputs LCS representations for different. languages. These representations have been ported into English, Arabic and Spanish lexicons, each containing approximately 9000 verbs. We are currently using these lexicons in an operational foreign language tutoring and machine translation.", |
| "pdf_parse": { |
| "paper_id": "A97-1021", |
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| "abstract": [ |
| { |
| "text": "We focus on the probleln of building large repositories of le.rical coJtceplual structure (LCS) representations for verbs in multiple languages. One of the main results of this work is the definition of a relat, ion between broad semantic classes and LCS meaniug components. Our acquisition program-LEXICALL-takes, as input, the result of previous work on verb classification and thematic grid tagging, and outputs LCS representations for different. languages. These representations have been ported into English, Arabic and Spanish lexicons, each containing approximately 9000 verbs. We are currently using these lexicons in an operational foreign language tutoring and machine translation.", |
| "cite_spans": [], |
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| "eq_spans": [], |
| "section": "Abstract", |
| "sec_num": null |
| } |
| ], |
| "body_text": [ |
| { |
| "text": "A wide range of new capabilities in NLP applications such as foreign language tutoring (FLT) has been made possible by recent advances in lexica.1 semantics (Carrier and Randall, 1993; Dowty, 1991; Fillmore, 1968; Foley and Van Valin, 1984; Grimshaw, 1990; Gruber, 1965; Hale and Keyser, 1993; Jackendoff, 1983; aackendoff, 1990 aackendoff, : Jackendoff, 1996 Levin, 1993; Levin and Rappaport Hovav, To appear; Pesetsky, 1982; Pinker, 1989) . Many of these researchers adopt the hypothesis that verbs can be grouped into broad classes, each of which corresponds to some combination of basic meaning con> ponents. This is the basic premise underlying our approach to multilingual lexicon construction. In particular, we have organized verbs into broad selnantic classes and subsequently designed a set of le,ical conceptual structures (LC, S), for each class. These representations have been ported into English, Arabic, and Spanish lexicons, each containing approximately 9000 verbs.", |
| "cite_spans": [ |
| { |
| "start": 157, |
| "end": 184, |
| "text": "(Carrier and Randall, 1993;", |
| "ref_id": "BIBREF1" |
| }, |
| { |
| "start": 185, |
| "end": 197, |
| "text": "Dowty, 1991;", |
| "ref_id": "BIBREF8" |
| }, |
| { |
| "start": 198, |
| "end": 213, |
| "text": "Fillmore, 1968;", |
| "ref_id": null |
| }, |
| { |
| "start": 214, |
| "end": 240, |
| "text": "Foley and Van Valin, 1984;", |
| "ref_id": "BIBREF10" |
| }, |
| { |
| "start": 241, |
| "end": 256, |
| "text": "Grimshaw, 1990;", |
| "ref_id": "BIBREF11" |
| }, |
| { |
| "start": 257, |
| "end": 270, |
| "text": "Gruber, 1965;", |
| "ref_id": null |
| }, |
| { |
| "start": 271, |
| "end": 293, |
| "text": "Hale and Keyser, 1993;", |
| "ref_id": "BIBREF14" |
| }, |
| { |
| "start": 294, |
| "end": 311, |
| "text": "Jackendoff, 1983;", |
| "ref_id": "BIBREF17" |
| }, |
| { |
| "start": 312, |
| "end": 328, |
| "text": "aackendoff, 1990", |
| "ref_id": null |
| }, |
| { |
| "start": 329, |
| "end": 359, |
| "text": "aackendoff, : Jackendoff, 1996", |
| "ref_id": null |
| }, |
| { |
| "start": 360, |
| "end": 372, |
| "text": "Levin, 1993;", |
| "ref_id": "BIBREF21" |
| }, |
| { |
| "start": 373, |
| "end": 410, |
| "text": "Levin and Rappaport Hovav, To appear;", |
| "ref_id": null |
| }, |
| { |
| "start": 411, |
| "end": 426, |
| "text": "Pesetsky, 1982;", |
| "ref_id": "BIBREF24" |
| }, |
| { |
| "start": 427, |
| "end": 440, |
| "text": "Pinker, 1989)", |
| "ref_id": "BIBREF25" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "An example of a NLP application for which these lexicons are currently in use is an operational foreign language tutoring (FLT) system called Military Language Tutor (MILT). This system provides a wide range of lessons for use in language training. One of the tutoring lessons, the MicroWorld Lesson (see Figure 1 ) requires the capability of the languagelearner to state domain-specific actions in a variety of different ways. For example, the language-learner might connnand the agent (pictured at the left in the graphical interface) to take the following action: Walt\" to the table and pick up the document. The same action should be taken if the user says: Go to the table and remove document, Retrieve the document from the table, etc. The LCS representation provides the capability to execute various forms of the same command without hardcoding them as part, of the graphical interface.", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 305, |
| "end": 313, |
| "text": "Figure 1", |
| "ref_id": null |
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| ], |
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| "section": "Introduction", |
| "sec_num": "1" |
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| "text": "In another tutoring lesson, Question-Answering, the student is asked to answer questions about a foreign language text that they have read. Their answer is converted into an LCS which is matched against a prestored LCS corresponding to an answer typed in by a human instructor (henceforth, called the \"author\"). The prestored LCS is an idealized form of the answer to a question, which can take one of many forms. Suppose, for example, the question posed to the user is: Where did Jack put the book'? (or Addnde paso Jack el libro? in Spanish). The author's answer, e.g., Jack put the book in the trash, has been stored as an LCS by the tutoring system. If the student types Jack threw the book in the trash, or Jack moved the book from the table into the trash, the system is able to nautch against the prestored LCS and determine that all three of these responses are semantically appropriate.", |
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| "section": "Introduction", |
| "sec_num": "1" |
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| { |
| "text": "We have developed an acquisition program--LEXICALL--that allows us to construct LCS-based lexicons for the FLT system. This program is designed to be used for multiple languages, and also for other NLP applications (e.g., machine translation). One of the main results of this work is the definition of a relation between broad semantic classes (based on work by Levin (1993) ) and LCS meaning components. We build on previous work, where verbs were classified automatically (Doff and .Jones, 1996: Dorr, To appear) and tagged with thematic grid information (Dorr, Garman, and Weinberg, 1995) . We use these pre-assigned classes and thematic grids as input to LEXICALL. The output is a set of LCS's corresponding to individual verb entries in our lexicon. Previous research in automatic acquisition focuses primarily on the use of statistical techniques, such as bilingual alignment (Church and Hanks, 1990; Klavans and Tzoukermann, 1995; Wu and Xia, 1995) or extraction of syntactic constructions from online dictionaries and corpora (Brent, 1993) . Others have taken a more knowledge-based (interlingual) approach (Lonsdale, Mitamura, and Nyberg, 1995) . Still others (Copestake et al.. 1995) , use Englishbased grammatical codes for acquisition of lexical representations.", |
| "cite_spans": [ |
| { |
| "start": 362, |
| "end": 374, |
| "text": "Levin (1993)", |
| "ref_id": "BIBREF21" |
| }, |
| { |
| "start": 557, |
| "end": 591, |
| "text": "(Dorr, Garman, and Weinberg, 1995)", |
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| { |
| "start": 882, |
| "end": 906, |
| "text": "(Church and Hanks, 1990;", |
| "ref_id": "BIBREF2" |
| }, |
| { |
| "start": 907, |
| "end": 937, |
| "text": "Klavans and Tzoukermann, 1995;", |
| "ref_id": null |
| }, |
| { |
| "start": 938, |
| "end": 955, |
| "text": "Wu and Xia, 1995)", |
| "ref_id": null |
| }, |
| { |
| "start": 1034, |
| "end": 1047, |
| "text": "(Brent, 1993)", |
| "ref_id": "BIBREF0" |
| }, |
| { |
| "start": 1115, |
| "end": 1153, |
| "text": "(Lonsdale, Mitamura, and Nyberg, 1995)", |
| "ref_id": null |
| }, |
| { |
| "start": 1169, |
| "end": 1193, |
| "text": "(Copestake et al.. 1995)", |
| "ref_id": "BIBREF3" |
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| "text": "Our approach differs from these in that it exploits certain linguistic constraints that govern the relation between a word's surface behavior and its corresponding semantic class. We delnonstrate that-by assigning a LCS representatioll to each semantic class--we can produce verb entries on a broad scale; these, in turn, are ported into multiple languages. We first show how the LCS is used in a FLT system. We then present an overview of the LCS acquisition process. Finally, we describe how LEXICALL constructs entries for specific lexical items.", |
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| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "One of the types of knowledge that must be captured in FLT is linguistic knowledge at the level of the lexicon, which covers a wide range of information types such as verbal subcategorization for events (e.g., that a transitive verb such as hit occurs with an object noun phrase), featural information (e.g., that the direct object of a verb such as frighlen is animate), thematic information (e.g., that Mary is the agent in Mary hie the ball), and lexical-semantic information (e.g., spatial verbs such as throw are conceptually distinct fi'om verbs of possession such as give). By modularizing the lexicon, we treat each information type separately, thus allowing us to vary the degree of dependence on each level so that we can address the question of how much knowledge is necessary for the success of the particular NLP application. This section describes the use of the LCS representation in a question-answering component of the MILT system (Sains, 1993; Weinberg et al., 1995) . As described above, the LCS representation is used as the basis of matching routines for assessing students' answers to free response questions about a short foreign language passage. In order to inform the student whether a question has been answered Jack threw the book in the trash exact match \"That's right\" Jack put the book in the trash Jack threw the book in the trash missing MANNER \"How?\" .Jack threw the book in the trash Jack put the book in the trash extra MANNER \"You're assuming things\" .Jack is friendly Jack put the book in the trash mismatch primitive \"Please reread\" Jack threw the book Jack put the book in the trash missing argument \"Where?\"", |
| "cite_spans": [ |
| { |
| "start": 949, |
| "end": 962, |
| "text": "(Sains, 1993;", |
| "ref_id": null |
| }, |
| { |
| "start": 963, |
| "end": 985, |
| "text": "Weinberg et al., 1995)", |
| "ref_id": "BIBREF6" |
| } |
| ], |
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| "eq_spans": [], |
| "section": "Application of the LCS Representation to FLT", |
| "sec_num": "2" |
| }, |
| { |
| "text": "correctly, the author of the lesson must provide the desired response in advance. The system parses and semantically analyzes the author's response into a corresponding LCS representation which is then prestored in a database of possible responses. Once the question answering lesson is activated, each of the student's responses is parsed and semantically analyzed into a LCS representation which is checked for a match against the corresponding prestored LCS representation. The student is then informed as to whether the question has been answered correctly depending on how closely the student's response LCS matches the author's prestored LCS.", |
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| "section": "Application of the LCS Representation to FLT", |
| "sec_num": "2" |
| }, |
| { |
| "text": "Consider what happens in a lesson if the author has specified that a correct answer to the question Addnde paso Jack el libro? in Spanish is Jack fir6 el libro a la basura ('Jack threw out the book into the trash'). This answer is processed by the system to produce the following LCS:", |
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| "section": "Application of the LCS Representation to FLT", |
| "sec_num": "2" |
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| { |
| "text": "(1) [E,'~nt CAUSE ([Thing JACK], [Ev..t GOLo~ ([Thing BOOK], [P~th TOLo~ ([Position ATLoc ([Thing BOOK], [Thing TRASH])])])], [M ...... THROWINGLY])]", |
| "cite_spans": [], |
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| "section": "Application of the LCS Representation to FLT", |
| "sec_num": "2" |
| }, |
| { |
| "text": "The LCS is stored by the tutor and then later matched against the student's answer. If the student types Jack movio ' el libro de la mesa a la basura ('Jack moved the book froln the table to the trash'), the system must determine if these two match. The student's sentence is processed and the following LCS structure is produced:", |
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| "section": "Application of the LCS Representation to FLT", |
| "sec_num": "2" |
| }, |
| { |
| "text": "(", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Application of the LCS Representation to FLT", |
| "sec_num": "2" |
| }, |
| { |
| "text": "2) [E .... CAUSE ([Thing JACK], [Event GOLoc ([Thing BOOK], [Path ZOLoc ([Position ATLo\u00a2 ([Thing BOOK], [Thing TRASH])])], [Path FROMLo~ ([Position ATLo~ ([Thing BOOK], [Thin~; TABLE])])])])]", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Application of the LCS Representation to FLT", |
| "sec_num": "2" |
| }, |
| { |
| "text": "The matcher compares these two, and produces the following output:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Application of the LCS Representation to FLT", |
| "sec_num": "2" |
| }, |
| { |
| "text": "Missing: MANNER THROWINGLY Extra: FROM LOC", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Application of the LCS Representation to FLT", |
| "sec_num": "2" |
| }, |
| { |
| "text": "The system identifies the student's response as a match with the prestored answer, but it also recognizes that there is one piece of missing information and one piece of extra information. The \"Missing\" and \"Extra\" output is internal to the NLP component of the Tutor, i.e., this is not the final response displayed to the student. The system must convert, this information into meaningful feedback so that the student knows how to repair the answer that was originally given. For example, the instructor can program the tutor to notify the student about the omitted information in the form of a 'How' question, or it can choose to ignore it. The extra information is generally ignored, although it is recorded in case the instructor decides to program the system to notify the student about this as well. The full range of feedback is not presented here. Some possibilities are summarized (in English) in Table 1 (adapted from (Holland, 1994) ). Note that. the main advantage of using the LCS is that it allows the author to type in an answer that is general enough to match any number of additional answers.", |
| "cite_spans": [ |
| { |
| "start": 928, |
| "end": 943, |
| "text": "(Holland, 1994)", |
| "ref_id": "BIBREF16" |
| } |
| ], |
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| { |
| "start": 906, |
| "end": 913, |
| "text": "Table 1", |
| "ref_id": "TABREF0" |
| } |
| ], |
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| "section": "Application of the LCS Representation to FLT", |
| "sec_num": "2" |
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| "text": "We use Levin's publicly available online index (Levin, 1993) as a starting point for building LCSbased verb entries. 1 While this index provides a unique and extensive catalog of verb classes, it does not define the underlying meaning components of each class. One of the main contributions of our work is that it provides a relation between Levin's classes and meaning components as defined in the LCS representation. Table 2 shows three broad semantic categories and example verbs along with their associated LCS representations. We have band-constructed a database containing 191 LCS templates, i.e., one for each verb class in (Levin, 1993) . In addition, we have genera.ted LCS templates for 26 additional classes that are not included in Levin's system. Several of these correspond to verbs that take sentential complements (e.g., coerce).", |
| "cite_spans": [ |
| { |
| "start": 47, |
| "end": 60, |
| "text": "(Levin, 1993)", |
| "ref_id": "BIBREF21" |
| }, |
| { |
| "start": 631, |
| "end": 644, |
| "text": "(Levin, 1993)", |
| "ref_id": "BIBREF21" |
| } |
| ], |
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| { |
| "start": 419, |
| "end": 426, |
| "text": "Table 2", |
| "ref_id": "TABREF1" |
| } |
| ], |
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| "section": "Overview of LCS Acquisition", |
| "sec_num": "3" |
| }, |
| { |
| "text": "1We focus on building entries for verbs; however, we have approximately 30,000 non-verb entries per language. ", |
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| "section": "Overview of LCS Acquisition", |
| "sec_num": "3" |
| }, |
| { |
| "text": "Ko]I [CAUSE (X, [BELo\u00a2 (Y, [ATLo\u00a2 (Y, Z)])], [BY (MANNER)])] [BELo\u00a2 (Y,[ATLo~ (Y, Z)], [BY (MANNER}])] [GOLo~ (Y, [(DIRECTION)Lo\u00a2 (Y, [ATLo\u00a2 (Y, Z)])])] [GOLo\u00a2 (Y, [BY (MANNER)])] [CAUSE (X, [GOIdent (Y, [TOWARDId~t (Y, [ATId\u00a2n~ (Y, [(STATE)Id~nt ([(WITH>po~ (*HEAD*, Z)])])])])])] [CAUSE (X, [GOLo\u00a2 (Y)], [BY (MANNER)])]", |
| "cite_spans": [], |
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| "section": "Overview of LCS Acquisition", |
| "sec_num": "3" |
| }, |
| { |
| "text": "A full entry in the dal:abase includes a semantic class number with a list of possible verbs, a thematic grid, and a LCS template:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Overview of LCS Acquisition", |
| "sec_num": "3" |
| }, |
| { |
| "text": "(3) Class 47.8: adjoin, intersect., meet, touch ....", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Overview of LCS Acquisition", |
| "sec_num": "3" |
| }, |
| { |
| "text": "Thematic Grid: _th_loc LCS Template: (be loc (thing 2) (at loc (thing 2) (thing 11)) ( ! ! -ingly 26) )", |
| "cite_spans": [], |
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| "eq_spans": [], |
| "section": "Overview of LCS Acquisition", |
| "sec_num": "3" |
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| { |
| "text": "The semantic class label 47.8 above is taken from Levin's 1993 book (Verbs of Contiguous Location), i.e., the class to which the verb touch has been assigned. 2 A verb, together with its semantic class uniquely identifies the word sense, or LCS template, to which the verb refers. The thematic grid (_th_loc) indicates that the verb has two obligatory arguments, a theme and a location. 3 The !! in the LCS Template acts as a wildcard; it will be filled by a lexeme (i.e., a root form of the verb). The resulting form is called a constant, i.e., the idiosyncratic part of the meaning that distinguishes among members of a verb class (in the spirit of (Grimshaw, 1993; Levin and Rappaport Hovav, To appear; Pinker, 1989; Talmy, 1985) ). 4 Three inputs are required for acquisition of verb entries: a semantic class, a thematic grid, and a lexeme, which we will henceforth abbreviate as \"class/grid/lexeme.\" The output is a Lisp-like expression corresponding to the LCS representation. An example of input/output for our acquisition procedure is shown here:", |
| "cite_spans": [ |
| { |
| "start": 651, |
| "end": 667, |
| "text": "(Grimshaw, 1993;", |
| "ref_id": null |
| }, |
| { |
| "start": 668, |
| "end": 705, |
| "text": "Levin and Rappaport Hovav, To appear;", |
| "ref_id": null |
| }, |
| { |
| "start": 706, |
| "end": 719, |
| "text": "Pinker, 1989;", |
| "ref_id": "BIBREF25" |
| }, |
| { |
| "start": 720, |
| "end": 732, |
| "text": "Talmy, 1985)", |
| "ref_id": null |
| } |
| ], |
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| "section": "Overview of LCS Acquisition", |
| "sec_num": "3" |
| }, |
| { |
| "text": "(4) Acquisition of LCS for: touch Input: 47.8: _th_loc; \"touch\" 2Verbs not occurring in Levin's book are also assigned to classes using techniques described in {Dorr and Jones, 1996; Dorr, To appear).", |
| "cite_spans": [], |
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| "eq_spans": [], |
| "section": "Overview of LCS Acquisition", |
| "sec_num": "3" |
| }, |
| { |
| "text": "ZAn underscore (_) designates an obligatory role and a comma (,) designates an optional role.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Overview of LCS Acquisition", |
| "sec_num": "3" |
| }, |
| { |
| "text": "4The ! ! in the Lisp representation corresponds to the angle-bracketed constants ill Table 2, e.g., ! !-ingly corresponds to (MANNER}.", |
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| "eq_spans": [], |
| "section": "Overview of LCS Acquisition", |
| "sec_num": "3" |
| }, |
| { |
| "text": "(be loc (* thing 2) (at loc (thing 2) (* thing 11)) (touchingly 26) )", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Output:", |
| "sec_num": null |
| }, |
| { |
| "text": "Language-specific annotations such as the .-,uarker in the LCS Output are added to the templates by processing the components of thematic grid specifications, as we will see in more detail next.", |
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| "eq_spans": [], |
| "section": "Output:", |
| "sec_num": null |
| }, |
| { |
| "text": "In our on-going example (4), the thematic grid _th loc indicates that the theme and the location are both obligatory (in English) and should be annotated as such in the instantiated LCS. This is achieved by inserting a *-marker appropriately. Consider the structural divergence between the following English/Spanish equivalents:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Language-Specific Annotations", |
| "sec_num": null |
| }, |
| { |
| "text": "(5) Structural Divergence: E: John entered the house. S: John entr6 a la casa. 'John entered into the house.'", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Language-Specific Annotations", |
| "sec_num": null |
| }, |
| { |
| "text": "The English sentence differs structurally from the Spanish in that the noun phrase the house corresponds to a prepositional phrase a la casa. This distinction is characterized by different positionings of the *-marker in the lexical entries produced by LEXICALL:", |
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| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Language-Specific Annotations", |
| "sec_num": null |
| }, |
| { |
| "text": "(6) Lexical Entries: enter: (go loc (* thing 2) (toward loc (thing 2) (in loc (thing 2) (* thing 6))) (enteringly 26) ) entrar: (go loc (* thing 2) ((* toward 5) loc (thing 2) (in loc (thing 2) (thing 6))) (enteringly 26) )", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Language-Specific Annotations", |
| "sec_num": null |
| }, |
| { |
| "text": "The lexicon entries for enter and entrar both mean \"X (= Thing 2) goes into location Y (= Thing 6).\" Variable positions (designated by numbers, such as 2, 5 and 6) are used in place of the ultimate fillers such as john and house. The structural divergence of (,5) is a.ccomnaodated as follows: the *-marked leaf node, i.e., (thing 6) in the enter definition, is filled directly, whereas the .-marked non-leaf node, i.e., ((toward 5) loc ...) in the en\u00a2rar definition, is filled in through unification at the internal toward node.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Language-Specific Annotations", |
| "sec_num": null |
| }, |
| { |
| "text": "C.onsider the construction of a lexical entry for the verb adorn. The LC, S for this verb is in the class of Fill Verbs (9.8): s (7) (cause (thing 1) (go ident (thing 2) (toward ident (thing 2) (at ident (thing 2) (!!-ed 9)))) (with poss (*head*) (thing 16)))", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Construction of Lexical Entries", |
| "sec_num": null |
| }, |
| { |
| "text": "This list structure recursively associates logical heads with their arguments and modifiers. The logical head is represented as a primitive/field Colnbination, e.g., GOIdent is represented as (go ident ...).", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Construction of Lexical Entries", |
| "sec_num": null |
| }, |
| { |
| "text": "The arguments for CAUSE are (thing 1) and (go ident ...). The substructure GO itself has two arguments (thing 2) and (toward ident ...) and a modifier (with poss ...).6 The ! !-ed constant refers to a resulting state, e.g., adorned for the verb adorn. The LC.S produced by our program for this verb is: The variables in the representation map between LCS positions and their corresponding thematic roles. In the LCS framework, thematic roles provide semantic information about properties of the argument and modifier structures. In (7) and (8) above, the numbers 1, 2, 9, and 16 correspond to the roles agent (ag), theme (th), predicate (pred), and possessional modifier (mod-poss), respectively. These numbers enter into the construction of LCS entries: they correspond to argument positions in the LCS template (extracted using the class/grid/lexeme specification), hfformatiou is filled into the LCS template using these numbers, coupled with the thematic grid tag for the particular word being defined.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Construction of Lexical Entries", |
| "sec_num": null |
| }, |
| { |
| "text": "LEXICALL locates the appropriate template in the LCS database using the class/grid pairing as an in-5Some of the other 9.8 verbs are: anoint, bandage. flood, frame, garland, stud, s~@~se, surround, veil. 6The *head* symbol--used for modifiers--is a placeholder that points to the root (cause) of the overall lex-icaJ entry. dex, and then determines the language-specifc annotations to instantiate for that template. The default position of the .-marker is the left-most occurrence of the LCS node corresponding to a particula.r thematic role. However, if a preposition occurs in the grid, the .-marker may be placed differently. In such a. case, a. primitive representation (e.g., (to loc (at loc))) is extracted from a set of predefined mappings. If this representation corresponds to a subcomponent of the LCS template, the program recognizes this as a match against the grid, and the .-marker is placed in the template at the level where this match occurs (as in the entry for entrar given in (6) above).", |
| "cite_spans": [ |
| { |
| "start": 142, |
| "end": 203, |
| "text": "bandage. flood, frame, garland, stud, s~@~se, surround, veil.", |
| "ref_id": null |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Pundmnentals", |
| "sec_num": "5.1" |
| }, |
| { |
| "text": "If a preposition occurs in the grid but there is no matching primitive representation, the preposition is considered to be a. collocation, and it is placed in a special slot--:collocations--which indicates that the LCS already covers the semantics of the verb and the preposition is an idiosyncratic variation (as in learn about, know of, etc.).", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Pundmnentals", |
| "sec_num": "5.1" |
| }, |
| { |
| "text": "If a preposition is required but it is not specified (i.e., empty parentheses 0), then the .-marker is positioned at the level dominating the node that corresponds to that role--which indicates that several different prepositions might apply (as in put on, put under, put through, etc.).", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Pundmnentals", |
| "sec_num": "5.1" |
| }, |
| { |
| "text": "The input to LEXICALL is a class/grid/lexeme specification, where each piece of information is separated by a hash sign (#):", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Examples", |
| "sec_num": "5.2" |
| }, |
| { |
| "text": "<class>#<grid>#<lexeme># <other semantic information>", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Examples", |
| "sec_num": "5.2" |
| }, |
| { |
| "text": "For example, the input specification for the verb replant (a word not classified by Levin) is: 9.7#_ag_th,mod-poss(with)#replant# !!-ed = planted (manner = again)", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Examples", |
| "sec_num": "5.2" |
| }, |
| { |
| "text": "This input indicates that the class assigned to replant is 9.7 (Levin's Spray/Load verbs) and its grid has a.n obligatory agent (ag), theme (tit), and all optional possessional modifer with preposition with (mod-poss (with) ). The information following the final # is optional; this information was previously hand-added to the assigned thematic grids. In the current example, the !!-ed designates the form of the constant planted which, in this case, is a morphological variant of the lexeme replant, r Also, the rThe constant takes one of several forms, including: ! !-ingly for a manner, ! !-er for an instrument, and !!-ed for resulting states. If this information has not been hand-added to the class/grid/lexeme specification (as is the case with most of the verbs), a default morphological process produces the appropriate form from tile lexeme. manner again is specified as an additional semantic coin ponent.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Examples", |
| "sec_num": "5.2" |
| }, |
| { |
| "text": "For presentational purposes, the remainder of this section uses English examples. However, as we saw in Section 4, the representations used here carry over to other languages a.s well. In fact, we have used the same acquisition program, without modification, for building our Spanish and Arabic LCS-based lexicons, each of size comparable to our English LCSbased lexicon.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Examples", |
| "sec_num": "5.2" |
| }, |
| { |
| "text": "(9) Example: The flower decorated the room.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "I. Thematic Roles without Prepositions", |
| "sec_num": null |
| }, |
| { |
| "text": "Input: 9.8#_mod-poss_th#decorate# Template:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "I. Thematic Roles without Prepositions", |
| "sec_num": null |
| }, |
| { |
| "text": "(be ident (thing 2) (at ident (thing 2) (!!-ed 9)) (with poss (*head*) (thing 16)))", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "I. Thematic Roles without Prepositions", |
| "sec_num": null |
| }, |
| { |
| "text": "Two thematic roles, th and mod-poss, are specified for the above sense of the English verb decorate. The thematic code numbers--2 and 16, respectively--are .-marked and the constant decorated replaces the wildcard:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "I. Thematic Roles without Prepositions", |
| "sec_num": null |
| }, |
| { |
| "text": "(10) Output: (be ident (* thing 2) (at ident (thing 2) (decorated 9)) (with poss (*head*) (* thing 16)))", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "I. Thematic Roles without Prepositions", |
| "sec_num": null |
| }, |
| { |
| "text": "II. Thematic Roles with Unspecified Prepositions (11) Example: We parked the car near the store. We parked the car in the garage. Input: 9. l#_ag_th_goal ( ) #park# Template:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "I. Thematic Roles without Prepositions", |
| "sec_num": null |
| }, |
| { |
| "text": "(cause (thing 1) (go loc (thing 2) (toward loc (thing 2) ([at] loc (thing 2) (thing 6)))) ( ! ! -ingly 26) )", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "I. Thematic Roles without Prepositions", |
| "sec_num": null |
| }, |
| { |
| "text": "The input for this example indicates that the goal is headed by an unspecifed preposition. The thematic roles ag, th, and goal() correspond to code numbers 1, 2, and 6, respectively. The variable positions for ag and th are .-marked just as in the previous case, whereas goal() requires a different treatment. (go ident (thing 2) (toward ident (thing 2) (at ident (thing 2) (!!-ed 9)))) (with poss (*head*) (thing 16)))", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "I. Thematic Roles without Prepositions", |
| "sec_num": null |
| }, |
| { |
| "text": "Here, the mod-poss role requires the preposition 'w~th in the modifier position:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "I. Thematic Roles without Prepositions", |
| "sec_num": null |
| }, |
| { |
| "text": "(14) Output:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "I. Thematic Roles without Prepositions", |
| "sec_num": null |
| }, |
| { |
| "text": "(cause (* thing 1) (go ident (* thing 2) (toward ident (thing 2) (at ident (thing 2) (decorated 9)))) ((* with 15) poss (*head*) (thing 16)))", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "I. Thematic Roles without Prepositions", |
| "sec_num": null |
| }, |
| { |
| "text": "In order to determine the position of the .-marker for a thematic role with a required preposition, LEXICALL consults a set of predefined mappings between prepositions (or postpositions, in a language like Korean) and their corresponding primitive representations, s In the current case, the preposition with is mapped to the following primitive representation: (with poss). Since this matches a sub-component of the LCS template, the program recognizes this as a match against the grid, and the .-marker is placed in the template at the level of with.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "I. Thematic Roles without Prepositions", |
| "sec_num": null |
| }, |
| { |
| "text": "Limitations and Conclusions", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "6", |
| "sec_num": null |
| }, |
| { |
| "text": "We have described techniques for automatic construction of dictionaries for use in large-scale FLT. The dictionaries are based on a languageindependent representation called lexical conceptual structure (LCS). Significant enhancements to LCSbased tutoring could be achieved by combining this representation with a mechanism for handling issues related to discourse and pragmatics. For example, Mthough the LCS processor is capable of determining that the phrase in the trash partially matches the answer to Where did John put the book?, a pragmatic component would be required to determine that this answer is (perhaps) more appropriate than the full answer, He put the book in the trash. Representing conversational context and dynamic context updating (Traum et al., 1996; Haller, 1996; DiEugenio and Webber, 1996) would provide a fl'amework for this type of response \"relaxation.\" Along SWe have defined approximately 100 such mappings per language.", |
| "cite_spans": [ |
| { |
| "start": 754, |
| "end": 774, |
| "text": "(Traum et al., 1996;", |
| "ref_id": null |
| }, |
| { |
| "start": 775, |
| "end": 788, |
| "text": "Haller, 1996;", |
| "ref_id": "BIBREF15" |
| }, |
| { |
| "start": 789, |
| "end": 816, |
| "text": "DiEugenio and Webber, 1996)", |
| "ref_id": null |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "6", |
| "sec_num": null |
| }, |
| { |
| "text": "For example, the mapping produces the following primitive representations for the English word to: (to loc (at loc)), (to poss (at poss)), (to temp (at temp)), (toward loc (at loc)), (toward poss (at poss)). We have similar mappings defined in Arabic and Spanish. For example, the following primitive representations are produced for the Spanish word a: (at loc), (to loc (at loc)), (to poss (at poss)), (toward loc (at lot)). these same lines, a pragmatic component could provide a mechanism for det, ermining that certain fully matched responses (e.g., John hurled the book inlo the trash) are not. as \"realistic sounding\" as partially matched alternatives.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "6", |
| "sec_num": null |
| }, |
| { |
| "text": "Initially, LEXICALL was designed to support the development of LCS's for English only; however, the same techniques can be used for nmltilingual acquisition. As the lexicon coverage for other languages expands, it, is expected that our acquisition techniques will help further in the cross-linguistic investigation of the relationship between Levin's verb classes and the basic meaning components in the LCS represent, ation. In addition, it is expected that verbs in the same Levin class may have finer distinctions than what we have specified in the current LCS templates.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "6", |
| "sec_num": null |
| }, |
| { |
| "text": "We view the importation of LCS's from the English LCS database into Arabic and Spanish as a first, approxin~ation to the development of complete lexicons for these languages. The results have been hand-checked by native speakers using the class/grid/lexeme format (which is much easier to check than the flfily expanded LCS's). The lexical verification process took only two weeks by the native speakers. We estimate that, it would take at least 6 months to build such a lexicon from scratch (by human recall and data. entry alone), and in such a case, the potential for error would be a.t least twice as high.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "6", |
| "sec_num": null |
| }, |
| { |
| "text": "One important benefit of using the Levin classification as the basis of our program is that, once the mapping between verb classes and LCS representations has been established, we can acquire the LCS representation for a new verb (i.e., one not in Levin) simply by associating it. with one of the 191 classes. We see our approach as a first step toward compression of lexical entries in that it allows lexicons to be stored in terms of the more condensed class/grid/lexeme specifications; these can expanded online, as needed, during sentence processing in the NLP application.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "6", |
| "sec_num": null |
| }, |
| { |
| "text": "We conclude that, while human intervention is necessary for the acquisition of class/grid information, this intervention is virtually eliminated fi'om the LCS construction process because of our provision of a lnapping between semantic classes and primitive meaning components.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "6", |
| "sec_num": null |
| } |
| ], |
| "back_matter": [ |
| { |
| "text": "gent Computer-Aided Training and Virtual Envi-ronmeT~t Technology, NASA: Houston, TX. Tahny, Leonard. 1985 ", |
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| "text": "Computer-Aided Training and Virtual Envi-ronmeT~t Technology, NASA: Houston, TX. Tahny, Leonard. 1985", |
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| "section": "annex", |
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