{ "paper_id": "A88-1033", "header": { "generated_with": "S2ORC 1.0.0", "date_generated": "2023-01-19T02:04:03.732543Z" }, "title": "COMBINATORIAL DISAMBIGUATION", "authors": [ { "first": "P", "middle": [ "S" ], "last": "Newman", "suffix": "", "affiliation": { "laboratory": "", "institution": "IBM Los Angeles Scientific Center", "location": { "addrLine": "11601 Wilshire Boulevard", "postCode": "90025-1738", "settlement": "Los Angeles", "region": "CA" } }, "email": "" } ], "year": "", "venue": null, "identifiers": {}, "abstract": "The disambiguation of sentences is a combinatorial problem. This paper describes a method for treating it as such, directly, by adapting standard combinatorial search optimizations. Traditional disambiguation heuristics are applied but, instead of being embedded in individual decision procedures for specific types of ambiguities, they contribute to numerical weights that are considered by a single global optimizer. The result is increased power and simpler code. The method is being implemented for a machine translation projecl, but could be adapted to any natural language system.", "pdf_parse": { "paper_id": "A88-1033", "_pdf_hash": "", "abstract": [ { "text": "The disambiguation of sentences is a combinatorial problem. This paper describes a method for treating it as such, directly, by adapting standard combinatorial search optimizations. Traditional disambiguation heuristics are applied but, instead of being embedded in individual decision procedures for specific types of ambiguities, they contribute to numerical weights that are considered by a single global optimizer. The result is increased power and simpler code. The method is being implemented for a machine translation projecl, but could be adapted to any natural language system.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Abstract", "sec_num": null } ], "body_text": [ { "text": "The disambiguation of sentences is a combinatorial problem. Identification of one word sense interacts with the identification of other word senses, (I) lie addressed the chair and with constituent attachment,", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1." }, { "text": "(2) He shot some bucks with a rifle Moreover, the attachment of one constituent interacts with the attachment of other constituents:", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1." }, { "text": "(", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1." }, { "text": "This paper describes a method of addressing the problem directly, by adapting standard searctl optimization techniques, in the first section we describe the core of the method, which applies a version of best-.first search to a uniform representation of the set of possibilities. In the second section we relate the work to other approaches to preference-based disambiguation. \"l\"he final sections describe how the representation may he obtained from a lexicon.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "3) She put the vase on the table in the living room", "sec_num": null }, { "text": "In the machine translation project for which this technique is being developed, disambiguation begins after a parser, specifically the PLNI.P English Grammar by Jensen (1986) , has identified one or more parses based primarily on syntactic considerations (including subcategorization). Words are disambiguated up to part-of-speech, but word senses are not identified. Individual parses may indicate alternative attachments of many kinds of constituents including prepositional phrases and relative clauses.", "cite_spans": [ { "start": 161, "end": 174, "text": "Jensen (1986)", "ref_id": "BIBREF7" } ], "ref_spans": [], "eq_spans": [], "section": "The Search Method", "sec_num": "2." }, { "text": "Beginning disambiguation only after a general parse has the advantage of making clear what all the possibilities are, thus allowing their investigation in an efficient order. Perlbrming a full parse before disamblguatlon need not consume an inordinate amount of space or time; techniques such as those used by Jensen (default rightmost prepositional phrase attachment), and \"l'omita (1985) (parse forests) adequately control resource requirements.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The Search Method", "sec_num": "2." }, { "text": "The parser output is first transfi)rmed so that it is represented as a set of of semantic choice points. Each choice point generally represents a constituent, it conrains a group of weighted semantic alternatives that represent the different ways in which word-senses of tile constituent head can he associated semantically with word-senses of a higher level head. This allows word-sense and attachment alternatives to be treated uniformly.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The Search Method", "sec_num": "2." }, { "text": "Combinatorial disamhiguation then selects the consistent combination of alternatives, one from cat'h choice point, that yields tile highest total weight. \"1,~ illustrate the method, we use tile exlension of the classic example mentioned above:", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The Search Method", "sec_num": "2." }, { "text": "(2) lie shot some bucks with a rifle A decomposition of tile sentence into choice points el, c2, and c3 is shown in Figure I . (The illustration assumes that \"shot\" and \"bucks\" have two meanings each, and ignores determiners.) Choice point \"cl\" gives the alternative syntactic and semantic possibilities for the attachment of the constituent \"he\" to its only possible head \"shot'. Alternative ell is that \"he\" is the subject of %hootl', with the semantic function \"agent', and is given (for reasons which will be discussed later) the weight \"3\". Alternative c12 is similar, but the meaning used for \"shoot\" is \"shoot2\". Similar alternatives are used for the attachment (c2) of the object \"some bucks\" to its only possible head, \"shoot'. Alternative c23 represents the unlikely combinations.", "cite_spans": [], "ref_spans": [ { "start": 116, "end": 124, "text": "Figure I", "ref_id": null } ], "eq_spans": [], "section": "The Search Method", "sec_num": "2." }, { "text": "Choice point c3 reflects the different possible attachments of'with a rifle'; the highest v, eight (3) is given to its attachment as an instrumental modifier of \"shootl'. The other possibilities range from barely plausible to implausible and are weighted accordingly.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The Search Method", "sec_num": "2." }, { "text": "I laving obtained this repesentation (whose construction is described in later sections), the next step is to establish the single-alternative choice points as given and to propagate any associated word-sense constraints to narrow or eliminate other alternatives. (l'his does not occur in our example.)", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The Search Method", "sec_num": "2." }, { "text": "Then combinations of the remaining choices are searched using a variation of the A* best-first search method. See Nilsson (1980) for a thorough description of A*. Briefly, A* views combinatorial search as a problem of finding an optimal path from the root to a leaf of a tree whose nodes are the weighted alternatives to be combined. At any point in the process, the next node n to be added is one for which the potential F(n) = G(n) + If(n) is maximal over all possible additions. G(n) represents the value of the path up to and including n. I I(n) represents an estimated upper bound for the value of the path below n (i.e., for the additional value which can be obtained) ~. When a complete path is found by this method, it must be optimal. The efficiency of the method, i.e., the speed of finding an optimal path, depends on the accuracy of tile estimates ll(n).", "cite_spans": [ { "start": 114, "end": 128, "text": "Nilsson (1980)", "ref_id": "BIBREF11" } ], "ref_spans": [], "eq_spans": [], "section": "The Search Method", "sec_num": "2." }, { "text": "To apply the method in our context, tile search tree is defined in terms of levels, with each level corresponding to a choice point. Choice points are assigned to levels so that those which would probably be responsible for the greatest difference between estimated and actual l l(n) in an arhitrary assignment are examined first. Looked at in another way, the assignment of choice points to levels is made so that those which will best differenliale among polential path scores are examined firsl. This is done by (partially) ordering the choice points in descending order of their difference potential De, the difference in weight between their highest weighted alternative and the next allernalive. If the highest weight is represented by two different alternatives, Dc = 0. Within this ordering the choice points are further ordered by weight differences between tile 2rid and 3rd highest weighted alternatives, etc. For our example this results in choice point c3 ('with a rifle') being assigned to the highest level in the tree, followed by choice points c2 and then el. ti.e., fired-at ) ti.e., ~rasted )", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The Search Method", "sec_num": "2." }, { "text": "li.e., male deer) ti.e., dollar) 0 ( i .e., together-~ith) ( i.e., ~ccompanied-by) We also associate with each level=choice point the value lie, which is the sum of the maximum weights that can be added by contributions below that choice point. This is needed by the algorithm to estimate potential path scores below a given node. For this example, the lie values are:", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The Search Method", "sec_num": "2." }, { "text": "HO: ~op=9", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The Search Method", "sec_num": "2." }, { "text": "H3:rifle=6 HZ: buck=3 HI: he=0", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The Search Method", "sec_num": "2." }, { "text": "Then the best-first search is carried out. At each point in the search, the next node to be added is that which (a) is consistent in word-sense selection with previous choices, and (b) has the highest potential. The potential is calculated as the accumulated weight down to (and including) that node plus tic for that level. A diagram of the procedure, as applied to the example, is shown in Figure 2 . The first node to be added is \"with rifle shootl', which has the highest potential. At that point, the highest weighted consistent alternative is c21, etc.", "cite_spans": [], "ref_spans": [ { "start": 392, "end": 400, "text": "Figure 2", "ref_id": "FIGREF3" } ], "eq_spans": [], "section": "The Search Method", "sec_num": "2." }, { "text": "While the set of choice points implies that there are (4 x 3 x 2) = 24 paths to be searched, only one is pursued to any distance. Thus while the approach takes a combinatorial view of the problem, it does so without loss of efficiency.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The Search Method", "sec_num": "2." }, { "text": "When a full path is found, it is examined for semantic consistency (beyond word-sense consistency). The checks made include: (a) ensuring that the interpretation includes all required semantic functions for a word-sense (specified in the lexicon), and (b) ensuring that non-repeatable functions (e.g., the goal of an action) are not duplicated.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The Search Method", "sec_num": "2." }, { "text": "Even if the full path is found to be consistent, the search does not terminate immediately, but continues until it is certain that no other path can have aft equal score. This will be true whenever the maximum potential for open nodes is less than the score for an already-found path. A more precise description of the algorithm is given in the Appendix.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The Search Method", "sec_num": "2." }, { "text": "When more than one path is found with the same high score, additional tests are applied. These tests include comparisons of surface proximity and, as this work is situated within a multi-target translation system, user queries in the source language, as outlined by Tomita (1985) .", "cite_spans": [ { "start": 266, "end": 279, "text": "Tomita (1985)", "ref_id": "BIBREF16" } ], "ref_spans": [], "eq_spans": [], "section": "The Search Method", "sec_num": "2." }, { "text": "An extended version of the method is used in comparing alternate parses which differ in constltucnt composition, and thus are more easily analyzed as different parse trees, each with its own set of choice points. An example is the classic: (where the main verb can he arty one of tile first three words). In such cases, one set of choice points is constructed per parse tree.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The Search Method", "sec_num": "2." }, { "text": "In general, the search alternates among trees, with the next node to be added being that with tile greatest potential across trees. If such trees always had Ihe same numhe,-of choice points, this would he the only revision needcd, llowever, the number of choice poinls may differ, for one thing because the parser may have detected and condensed non-compositional compounds (e.g., \"all the same ~) in one parse but not in another. For this reason the algorithm changes to evaluate paths not by total scores, but by average scores (i.e., the total scores divided by the number of choice points in the particular parse).", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The Search Method", "sec_num": "2." }, { "text": "\"rifle\" c31", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "\"kop", "sec_num": null }, { "text": "\"buck\" cZl cZZ ( inconsistent )", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "\"kop", "sec_num": null }, { "text": "\"he\" cll The basic A* algorithm is usually described as *expanding\" (i.e., adding all immediate successors ol) the most promising node. The variant described here, which is more appropriate to our situation (and also mentioned by Nilsson), adds a single node at each step.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "\"kop", "sec_num": null }, { "text": "There seems to be little work which directly addresses the combinatorial problem. First, there is considerable work in preference-related disambiguation that assumes, at least for purposes of discussion, that individual disambiguation problems can be addressed in isolation. For example, treatments of prepositional phrase attachment by by Dahigren and McDowell (1986) and Wilks el. al. (1985) propose methods of finding the \"best ~ resolution of a single attachment problem by finding the first preference which is satisfied in some recommended order ofrule application. Other types of ambiguity, and other instances of the same type, are assumed to have been resolved. This type of work contributes interesting insights, but cannot be used directly.", "cite_spans": [ { "start": 340, "end": 368, "text": "Dahigren and McDowell (1986)", "ref_id": null }, { "start": 373, "end": 393, "text": "Wilks el. al. (1985)", "ref_id": null } ], "ref_spans": [], "eq_spans": [], "section": "Related Work", "sec_num": "3." }, { "text": "One type of more realistic treatment, which might be called the deferred decision approach, is exemplified by ilirst (19831. When, in the course of a parse, an immediate decision about a word sense or attachment cannot be made, a set of alternative possibilities is developed. The possibility sets are gradually narrowed by propagating the implications of both decisions and narrowings of other possibility sets.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Related Work", "sec_num": "3." }, { "text": "This approach has a number of problems. First, it is susceptible to error in semantic \"garden path\" situalions, as early decisions may not be justifiable in the context. For example, in processing (5) He shot a hundred bucks with one rifle.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Related Work", "sec_num": "3." }, { "text": "a particular expert might decide on \"dollars\" for bucks, because of the modifier \"hundred', before the prepositional phrase is processed. Also, it is difficult to see how versions of this method could be extended to deal with comparing alternate parses where the alternatives are not just ones of attaching constituents, but of deciding what the constituents are in the first place.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Related Work", "sec_num": "3." }, { "text": "A full-scale deferred-decision approach also has the potential of significant design complexity (the ilirst version is explicitly limited), as each type of decision procedure (for words and for different kinds of attachments) must be able to understand and process the implications of the results of other kinds of decision procedures.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Related Work", "sec_num": "3." }, { "text": "Underlying these problems is the lack of quantification of alternatives, which allows for comparison of combinations.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Related Work", "sec_num": "3." }, { "text": "There are, however, early and more recent approaches which do apply numeric weights to sentence analysis. Early approaches using weights applied them primarily to judge alternative parses. Syntactically-oriented approaches in this vein attached weights to phrase structure grammar rules (Robinson 1975 (Robinson , 1980 or ATN arcs (Bates 1976) . Some approaches of this period focussing on semantic expectations were those of Wilks (19751 and Maxwell and Tuggle (1975) , which employed scores expressing the number of expected dependents present in an interpretation. An ambitious approach combining different kinds of weights and cumulative scores, described by Walker and Paxton et. al. (19771, included heuristics to select the most promising sublrees for earlier development, to avoid running out of space before a \"best\" tree can be found. llowever, except for tiffs type of provision, none of the early approaches using weights seem to address the combinatorial problem. ~", "cite_spans": [ { "start": 287, "end": 301, "text": "(Robinson 1975", "ref_id": null }, { "start": 302, "end": 318, "text": "(Robinson , 1980", "ref_id": null }, { "start": 331, "end": 343, "text": "(Bates 1976)", "ref_id": "BIBREF0" }, { "start": 426, "end": 468, "text": "Wilks (19751 and Maxwell and Tuggle (1975)", "ref_id": null }, { "start": 663, "end": 705, "text": "Walker and Paxton et. al. (19771, included", "ref_id": null } ], "ref_spans": [], "eq_spans": [], "section": "Related Work", "sec_num": "3." }, { "text": "A contemporary approach for thorough syntactic and semantic disambigualion using weights is described by L. . During a left-to-right parse, individual attachments are weighted based on a list of considerations including preference, relative sentential position, frames/scripL~, and salience in the current context. The multiple evolving parse trees are rated by summing their contained weights, and the combinatorial problem is controlled by retaining only the two highest scoring parses of any complete pltrases.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Related Work", "sec_num": "3." }, { "text": "This approach is interesting, although some details are vague 3. l[owevcr, the post-parse application of A* described in this paper obtains the benefits of such a within-parse approach without its deficiences in that: (a) combinatorial computations of weighCs and wordsense consistencies are avoided except when warranted by total sentence informalion, and (b) there is no possibility of early erroneous discarding of allerrmtives. Heidorn (1982) provides a good summary of early work in weight-based analysis, as well as a weight-oriented approach to attachment decisions based on syntactic considerations only.", "cite_spans": [ { "start": 432, "end": 446, "text": "Heidorn (1982)", "ref_id": "BIBREF5" } ], "ref_spans": [], "eq_spans": [], "section": "Related Work", "sec_num": "3." }, { "text": "No examples are given, so it is unclear whether a parse for a phrase or part thereof represents only one interpretation, or all interpretations having the same structure, scored by the most likely interpretation. The former is obviously inadequate (c.g., for highly ambiguous subject NPs like *The stands'), while the latter seems to require either the calculation of all alternative cumulative scores, or recalculation of scores if an interpretation fails. One other parser-based work should be noted, that of Wittenburg (1986) , as it is explicitly based on A*. The intent and content of the method is quite different from that described here. It is situated within a chart-parser for a categorial grammar, and the intent is to limit parsing expense by selecting that rule for next application which has the least likelihood of leading to an incomplete tree. While selectional preference is mentioned in passing as a possible heuristic, the heuristics discussed in any depth are grammar oriented, and operate on the immediate facts of the situation, rather than on informed estimates of total parse scores.", "cite_spans": [ { "start": 511, "end": 528, "text": "Wittenburg (1986)", "ref_id": "BIBREF21" } ], "ref_spans": [], "eq_spans": [], "section": "Related Work", "sec_num": "3." }, { "text": "It should be also be mentioned that the representation of alternatives in schemes which combine syntactic and semantic disambiguation is rarely discussed, although maintaining a consistent representation of the relationships among word-sense and attachment alternatives is fundamental to a systematic treatment of the problem. An exception is the discussion by K. Schubert 0986), who describes a representation for alternatives with some affinities to that described here 4.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Related Work", "sec_num": "3." }, { "text": "parsing are not found in spreading-activation approaches, exemplified by Charniak 0986), Cottrell and Small 0984), and Waltz and Pollack (1985) . These approaches are still in the experimental stage, and are primarily intended for parallel hardware, while the A* algorithm used in this paper is designed for conventional serial hardware. But, in a sense, these approaches reinforce the main point of this paper: they argue for a single global technique for optimized combinatorial disambiguation based on all available information.", "cite_spans": [ { "start": 119, "end": 143, "text": "Waltz and Pollack (1985)", "ref_id": "BIBREF18" } ], "ref_spans": [], "eq_spans": [], "section": "The information limitations of disambiguation during", "sec_num": null }, { "text": "llaving described how the choice points are used, we address their development. Two steps are involved:", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Preparing Semantic Choices", "sec_num": "4." }, { "text": "(1) the development of syntactic choice points, and (2) the development of semantic choice points. The first step transforms the parse-level syntactic functions into a form appropriate to the second step, which is the application of the lexicon to those fimctions to obtain the semantic alternatives.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Preparing Semantic Choices", "sec_num": "4." }, { "text": "In our example, the first step is a simple one. Syntactic relationships among conslituents are transformed into syntactic relationships among head words, and the syntactic relationships arc refined, so that \"ppmod\" is replaced by the actual preposilions used. The result of this step is shown in Figure 3 . The development of syntactic choice poinls for some other types of constituents is more complex. Before discussing these situations, we discuss slep 2: application of the lexicon to the syntactic choice points Io obtain Ihe semantic choice points, i.e., those shown in Figure I ", "cite_spans": [], "ref_spans": [ { "start": 296, "end": 304, "text": "Figure 3", "ref_id": "FIGREF4" }, { "start": 576, "end": 584, "text": "Figure I", "ref_id": null } ], "eq_spans": [], "section": "Preparing Semantic Choices", "sec_num": "4." }, { "text": "The lexicon conlains entries for word stems (distinguished by part-of-speech), linked to word-sense entries, which are lhe lowest level \"conccptsL Concept entries are linked Io superordinale concept entries, forming a lattice. Concepl entries include a set of concept features (e.g. a list of snperordinate concepts), and one or more rules for each syntactic function associated with the concept. The more relevant parts of the lexicon entries fbr the concepts used in the ongoing example are shown in Figure 4 Weights are actually assigned symbolically, to allow experimentation. Current settings are as follows:", "cite_spans": [], "ref_spans": [ { "start": 502, "end": 510, "text": "Figure 4", "ref_id": "FIGREF2" } ], "eq_spans": [], "section": "The Lexicon", "sec_num": "4.1" }, { "text": "\u2022 Rules for idioms (e.g., kick the bucket), 4.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The Lexicon", "sec_num": "4.1" }, { "text": "\u2022 RUles for more general selectional preferences, 3.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The Lexicon", "sec_num": "4.1" }, { "text": "\u2022 Rules for acceptable but not preferred alternatives (e.g., locative prepositional phrases attached to arbitrary actions), 2. \u2022 Very general attachments (e.g., \"nounmod noun l noun2), 0. These allow for uninterpreted metaphoric usage. 5", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The Lexicon", "sec_num": "4.1" }, { "text": "One major objective in assigning weights to ensure that combinations of very high (idiom) weights together with very low weights do not ouLscore more balanced combinations. Thus, for: Lexicon entries also cot|lain additional information. First, a list of required syntactic functions is generally associated with word-senses. Also, syntactic function rules may contain additional conditions limiting their applicability. For example, a combination \"nounl IN noun2* might be limited to apply only if the second concept denotes an object larger than the first.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "The Lexicon", "sec_num": "4.1" }, { "text": "Given these lexicon entries, the set of semantic alternatives corresponding to each syntactic alternative \"synfun wordl word2\" may be derived. The goal of the derivation process is to account for all possible combinations of word-senses for wordl and word2 related by tile syntactic fimction \"synfim\". To do this, all concept entries containing potentially applicable rules are searched. For each satisfied rule fotmd, an alternative is created of the form:", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Lexicon Application", "sec_num": "4.2" }, { "text": "senran{ic-rela~ion sensepairs weigh{", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Lexicon Application", "sec_num": "4.2" }, { "text": "where \"sensepairs\" is a list of pairs. F.ach pair is in the form ((di,dj,...) (hnl,hn,...)), where the di's are senses of the dependenl participant of the function,", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Lexicon Application", "sec_num": "4.2" }, { "text": "Obtaining the necessary lexicon information is of course a major problem. But there is significant contemporary work in the automatic or semi-automatic derivation of that information. For example, the aproached described by Jcnsen and Binot (19~,6) obtains attachment preference information by parsing dictionary definitions.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Lexicon Application", "sec_num": "4.2" }, { "text": "and tile hi's are senses of the headword. The semantic relationship is stated to apply to all combinations of word-senses in the cross-products of those lists. For the example sentence, this process would obtain essentially the alternatives shown in Figure 1 , except that alternative c23 would first be expressed as:", "cite_spans": [], "ref_spans": [ { "start": 250, "end": 258, "text": "Figure 1", "ref_id": "FIGREF1" } ], "eq_spans": [], "section": "Lexicon Application", "sec_num": "4.2" }, { "text": "obj I t buekl ,l~ckZ ) ( shoo~l, shoot2 ) ) 0", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Lexicon Application", "sec_num": "4.2" }, { "text": "The last step in the process reduces this result. If some of the word-sense combinations are also found in an alternative of higher weight, the \"dominated\" combinations are deleted. And if all word sense combinations are so dominated, the alternative is deleted. In this way alternative c23 is reduced to its final form.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Lexicon Application", "sec_num": "4.2" }, { "text": "After the semantic choice point list is completed, the search algorithm is applied as described above.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Lexicon Application", "sec_num": "4.2" }, { "text": "In the example above, the preparation of syntactic choice points from parser output was very simple.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Preparing Syntactic Choices", "sec_num": "5." }, { "text": "Assuming an input choice point for a constituent to be a list of (one or more) parser-provided alternative relationships with an immediately containing constituent, the process consisted of obtaining the headwords of each constituent, and of substituting literal prepositions for the general syntactic function \"ppmod'.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Preparing Syntactic Choices", "sec_num": "5." }, { "text": "i iowever, in other cases this step is a more significant one. In the lexicon, selectional preferences are expressed in terms of the syntactic functions of some basic constituent types. For example, verb preferences are expressed in terms of the syntactic functions of active-voice, declarative main clauses, with dependents in unmarked positions. Adjective preferences are expressed in terms of classes of nouns occurring in the relationship \"adjective noun'. But there are many other syntactic relationships whose dlsambiguation depends on this information. The major function of the syntactic choice identification step is to re-express, or \"normalize\" input syntacfic relationsips in terms of the relationships assumed by the lexicon. For example, passive constructions such as:", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Preparing Syntactic Choices", "sec_num": "5." }, { "text": "(7)", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Preparing Syntactic Choices", "sec_num": "5." }, { "text": "The bucks were shot with a rifle are normalized by replacing the choice \"subj buck shoot\" with \"object buck shoot'. (A lexicon condition barring or lowering preference for the \"gambling\" interpretation in the passive voice is also needed here.)", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Preparing Syntactic Choices", "sec_num": "5." }, { "text": "In ditransitive cases both indirect and direct object functions are used as allernatives.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Preparing Syntactic Choices", "sec_num": "5." }, { "text": "Thus the sequence of deriving semantic choice points consists of two siguilicant steps, which may be depicted in terms of results as shown in Figure 5 .", "cite_spans": [], "ref_spans": [ { "start": 142, "end": 150, "text": "Figure 5", "ref_id": "FIGREF8" } ], "eq_spans": [], "section": "Preparing Syntactic Choices", "sec_num": "5." }, { "text": "The transformation of input syntactic choice points to normalized synlactic choice poinls is governed by declarative specifications indicaling, for each syntactic function, how its choice points are to be transformed. The changes are specified as replacements for one or more positions of the choice triple. For example, some of the \"subj\" rules are: slating that for tile input fimction \"subj', if the specified test (voicepassive) succeeds, then \"obj\" is used for the synfun part of tile normalized choice. The additional rule is used to ensure that tile ~subject\" fimclion is retained only for ttle aclive voice.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Preparing Syntactic Choices", "sec_num": "5." }, { "text": "Additional applications of these transformations include those for modifiers of nomlnalized verbs, attributive clauses, and relative clauses. the alternative \"nounmod bucks shooting\" is expanded to include-the alternatives \"subj bucks shooting\" and \"obj bucks shooting'. Then, during lexical processing, rules for word-senses of the noun \"shooting\" having an associated verb are understood as expanded to include the expected verb arguments.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Preparing Syntactic Choices", "sec_num": "5." }, { "text": "Attributive clauses such as:", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Preparing Syntactic Choices", "sec_num": "5." }, { "text": "(9)", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Preparing Syntactic Choices", "sec_num": "5." }, { "text": "The bucks were handsome.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Preparing Syntactic Choices", "sec_num": "5." }, { "text": "are transformed to allow the application of adjective information, tlere'obj handsome were\" is transformed to *adjmod handsome bucks'.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Preparing Syntactic Choices", "sec_num": "5." }, { "text": "For relative clauses, the core of the transformation expresses alternative attachments of the relative clause as alternative connections between the head of the relative clause, and the possible fillers of the gap position. (Relative clauses with multiple gaps are generally handled in separate parses.) Thus for:", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Preparing Syntactic Choices", "sec_num": "5." }, { "text": "(10) The rifle above the mantle that the bucks were shot with... transformations produce the alternatives \"with shoot rifle\" and \"with shoot mantle'.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Preparing Syntactic Choices", "sec_num": "5." }, { "text": "The handling of relative clauses, however, is more complicated than this, as it is desirable to also use information from the relative pronoun (if present) for the disambiguation. Two initial choice points are involved, one attaching the relative clause to its higher level head, and one attaching the gap to its head. The first is expanded to to obtain relationships *relp that rifle\", \"relp that mantle*, and the other to obtain the \"with .... \" relationships. And an additional consistency check is made during the tree search, beyond wordsense consistency, to keep the choices consistent.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Preparing Syntactic Choices", "sec_num": "5." }, { "text": "It should be noted that the transformation rules for syntactic choice points also include \"fixup specifications\" (not shown above) indicating how result semantic functions and attachments are to be modified if the transformed alternatives are used in the final interpretation. For example, to \"fixup\" the results of transforming attributive clauses, the noun-modifier semantic role is replaced with one suitable to a direct role in the clause.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Preparing Syntactic Choices", "sec_num": "5." }, { "text": "This paper has summarized a three-step method for optimized combinatorial preference based disambiguadon:", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Concluding Remarks", "sec_num": "6." }, { "text": "1. obtaining syntactic choice points, with alternatives stated as syntactic functions relating words. 2. transformation into semantic choice points with alternatives stated as weighted semantic functions relating word-senses, via lexicon search. 3. application of A* to search alternative combinations.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Concluding Remarks", "sec_num": "6." }, { "text": "This method, currently being implemented in the context of a multi-target machine translation system, is more powerful and systematic than approaches using isolated or interacting decision procedures, and thus is easier to modify and extend with new heuristics as desired.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Concluding Remarks", "sec_num": "6." }, { "text": "The method is applicable to any post-parse disambigualion situation, and can be taken as an argument for that approach. To demonstrate this, we first note that aspects of the method are useful for within-parse disambigualion. In any realistic scheme, decisions must of[en be deferred, making two aspects of the method relevant: (a) the unified way of representing word sense and attachment alternatives and their interdependency, and (b) the explicit, additive weights. F.xplicit additive weights substitute for elaborate, case-specific rules, and also make possible a systematic treatment of alternative parses which differ in more than word-senses and attachments.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Concluding Remarks", "sec_num": "6." }, { "text": "! lowever, using weighted attachments for within-parse disambiguation requires calculating the summed weights of, and examining the consistency of, all combinations encountered whose elements cannot be discarded (assuming some good criteria for discarding can be found). Deferring disambiguation until after the parse allows for optimized searching of alternatives, as described above, to significantly limit tile number of combinations examined.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Concluding Remarks", "sec_num": "6." }, { "text": "Future work in this direction will include refining tile weighting criteria, extending the method It deal with anaphoric references (using consideralions developed by, for example, Jensen and Zadrozny (1987) , and integrating a treatment of non-frozen metaphor.", "cite_spans": [ { "start": 181, "end": 207, "text": "Jensen and Zadrozny (1987)", "ref_id": "BIBREF9" } ], "ref_spans": [], "eq_spans": [], "section": "Concluding Remarks", "sec_num": "6." }, { "text": "! thank John Sowa, Maria Fuenmayor, and Shelley Smith for their careful reviews and many helpful suggestions. Also, I thank Peter Woon for his patient managerial support of this project.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "7.Acknowledgements", "sec_num": null }, { "text": "However, the weighting scheme is different, and rather interesting. The reference does not discuss the selection of a particular combination of alternatives in any detail, but it appears to be based on the presence in a combination of one (or more?) highly weighted alternative (or alternatives.'?).", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null } ], "back_matter": [ { "text": " Figure 6 describes Ihe step by step application of A* to searching semantic choices.Assume an \"open list\" containing, for each node n with an unexamined child, the following information:1. The list of choices made on the path up to and including n. Then the following algorithm is used to search the tree.1. Put an entry for the dummy \"top\" node in the open list, with path=self, index of first child, and its potential Fc. 2. Find the node n in the \"open list\" Nhich has the highest potential Fc for a child node. 3. If a full path has already been found, and Fc is lower than the total ~leight for that path, the search is over. ", "cite_spans": [], "ref_spans": [ { "start": 1, "end": 9, "text": "Figure 6", "ref_id": null } ], "eq_spans": [], "section": "Appendix: Search Algorithm", "sec_num": "9." } ], "bib_entries": { "BIBREF0": { "ref_id": "b0", "title": "Syntax in Automatic Speech Understanding", "authors": [ { "first": "Madeleine", "middle": [], "last": "Bates", "suffix": "" } ], "year": 1976, "venue": "Am. J. Comp. Ling. Microfiche", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Bates, Madeleine 1976. \"Syntax in Automatic Speech Understanding', Am. J. Comp. Ling. 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AAAI-86, 1053-1058", "links": null } }, "ref_entries": { "FIGREF1": { "uris": null, "text": "Choice points and alternatives", "num": null, "type_str": "figure" }, "FIGREF2": { "uris": null, "text": "Time flies like an arrow", "num": null, "type_str": "figure" }, "FIGREF3": { "uris": null, "text": "Red.ced Tree Search!", "num": null, "type_str": "figure" }, "FIGREF4": { "uris": null, "text": "Syntactic Choice Poin~", "num": null, "type_str": "figure" }, "FIGREF5": { "uris": null, "text": ".", "num": null, "type_str": "figure" }, "FIGREF6": { "uris": null, "text": ". The \"classes\" are lisls of superordinale concepts. The synlactic function rules have the form: synfun dependent head semfun weight Thus tile first rule under \"shootl\" indicates that wordsenses falling into tile class \"animate\" are its preferred objects, with tile weight 3, and the combination is given tile semantic fimclion \"goal'. The concept entry shoo~l classes I humanac~, ~ransv) Izxieon Entries \"humanact\" contains other rules applicable to shootl and other verb-senses taking human actors.", "num": null, "type_str": "figure" }, "FIGREF8": { "uris": null, "text": "Steps in Semantic Choice Point DerivationNoun phrases whose heads are nominalized verbs are addressed by adding choice points corresponding to verb arguments. Thus for (8) 1\"he bucks' shooting .....", "num": null, "type_str": "figure" }, "TABREF1": { "type_str": "table", "num": null, "html": null, "text": "..................................", "content": "
Input Syn Chp~lNormalized Syn Chp~lSeman~ ic Chp~l
Choice CllChoice ClllChoice C1111
Choice Cl112
Choice CllZChoice C1121
Choice Cl12Z
" } } } }