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W09-0205 C08-1114 o We adopt a similar approach to the one used in Turney (2008) and consider each question as a separate binary classification problem with one positive training instance and 5 unknown pairs.
W09-0419 C08-1115 o "They are part of an effort to better integrate a linguistic, rule-based system and the statistical correcting layer also illustrated in (Ueffing et al., 2008)."
D09-1079 C08-1125 o "3.5 Domain adaptation in Machine Translation Within MT there has been a variety of approaches dealing with domain adaption (for example (Wu et al., 2008; Koehn and Schroeder, 2007)."
P09-1036 C08-1127 o "This, unfortunately, significantly jeopardizes performance (Koehn et al., 2003; Xiong et al., 2008) because by integrating syntactic constraint into decoding as a hard constraint, it simply prohibits any other useful non-syntactic translations which violate constituent boundaries."
N09-1061 C08-1136 o "Optimal algorithms exist for minimising the size of rules in a Synchronous Context-Free Grammar (SCFG) (Uno and Yagiura, 2000; Zhang et al., 2008)."
P09-1088 C08-1136 o "The machine translation literature is littered with various attempts to learn a phrase-based string transducer directly from aligned sentence pairs, doing away with the separate word alignment step (Marcu and Wong, 2002; Cherry and Lin, 2007; Zhang et al., 2008b; Blunsom et al., 2008)."
P09-1088 C08-1136 o "The sampler reasons over the infinite space of possible translation units without recourse to arbitrary restrictions (e.g., constraints drawn from a wordalignment (Cherry and Lin, 2007; Zhang et al., 2008b) or a grammar fixed a priori (Blunsom et al., 1f and e are the input and output sentences respectively."
P09-1088 C08-1136 o "Following the broad shift in the field from finite state transducers to grammar transducers (Chiang, 2007), recent approaches to phrase-based alignment have used synchronous grammar formalisms permitting polynomial time inference (Wu, 1997; 783 Cherry and Lin, 2007; Zhang et al., 2008b; Blunsom et al., 2008)."
P09-1111 C08-1136 o "Other linear time algorithms for rank reduction are found in the literature (Zhang et al., 2008), but they are restricted to the case of synchronous context-free grammars, a strict subclass of the LCFRS with f = 2."
D09-1108 C08-1138 o "In the SMT research community, the second step has been well studied and many methods have been proposed to speed up the decoding process, such as node-based or span-based beam search with different pruning strategies (Liu et al., 2006; Zhang et al., 2008a, 2008b) and cube pruning (Huang and Chiang, 2007; Mi et al., 2008)."
D09-1108 C08-1138 o "3.1 Exhaustive search by tree fragments This method generates all possible tree fragments rooted by each node in the source parse tree or forest, and then matches all the generated tree fragments against the source parts (left hand side) of translation rules to extract the useful rules (Zhang et al., 2008a)."
D09-1108 C08-1138 p "1 Introduction Recently linguistically-motivated syntax-based translation method has achieved great success in statistical machine translation (SMT) (Galley et al., 2004; Liu et al., 2006, 2007; Zhang et al., 2007, 2008a; Mi et al., 2008; Mi and Huang 2008; Zhang et al., 2009)."
P09-1020 C08-1138 o "4 Training This section discusses how to extract our translation rules given a triple nullnull,null null ,nullnull . As we know, the traditional tree-to-string rules can be easily extracted from nullnull,null null ,nullnull using the algorithm of Mi and Huang (2008) 2 . We would like 2 Mi and Huang (2008) extend the tree-based rule extraction algorithm (Galley et al., 2004) to forest-based by introducing non-deterministic mechanism."
P09-1020 C08-1138 p "Among these advances, forest-based modeling (Mi et al., 2008; Mi and Huang, 2008) and tree sequence-based modeling (Liu et al., 2007; Zhang et al., 2008a) are two interesting modeling methods with promising results reported."
P09-1020 C08-1138 o "Motivated by the fact that non-syntactic phrases make non-trivial contribution to phrase-based SMT, the tree sequencebased translation model is proposed (Liu et al., 2007; Zhang et al., 2008a) that uses tree sequence as the basic translation unit, rather than using single sub-tree as in the STSG."
P09-1020 C08-1138 o (2008a) propose a tree sequence-based tree to tree translation model and Zhang et al.
P09-1020 C08-1138 o "Therefore, structure divergence and parse errors are two of the major issues that may largely compromise the performance of syntax-based SMT (Zhang et al., 2008a; Mi et al., 2008)."
P09-1020 C08-1138 o "A tree sequence to string rule 174 A tree-sequence to string translation rule in a forest is a triple <L, R, A>, where L is the tree sequence in source language, R is the string containing words and variables in target language, and A is the alignment between the leaf nodes of L and R. This definition is similar to that of (Liu et al. 2007, Zhang et al. 2008a) except our treesequence is defined in forest."
P09-1103 C08-1138 o "To address this issue, many syntax-based approaches (Yamada and Knight, 2001; Eisner, 2003; Gildea, 2003; Ding and Palmer, 2005; Quirk et al, 2005; Zhang et al, 2007, 2008a; Bod, 2007; Liu et al, 2006, 2007; Hearne and Way, 2003) tend to integrate more syntactic information to enhance the non-contiguous phrase modeling."
P09-1103 C08-1138 o "Nevertheless, the generated rules are strictly required to be derived from the contiguous translational equivalences (Galley et al, 2006; Marcu et al, 2006; Zhang et al, 2007, 2008a, 2008b; Liu et al, 2006, 2007)."
P09-1103 C08-1138 o "2 We illustrate the rule extraction with an example from the tree-to-tree translation model based on tree sequence alignment (Zhang et al, 2008a) without losing of generality to most syntactic tree based models."
P09-1103 C08-1138 o "The proposed synchronous grammar is able to cover the previous proposed grammar based on tree (STSG, Eisner, 2003; Zhang et al, 2007) and tree sequence (STSSG, Zhang et al, 2008a) alignment."
D09-1024 C08-1139 o "Word alignment is also a required first step in other algorithms such as for learning sub-sentential phrase pairs (Lavie et al., 2008) or the generation of parallel treebanks (Zhechev and Way, 2002)."
E09-1044 C08-1144 o "Previously published approaches to reducing the rule set include: enforcing a minimum span of two words per non-terminal (Lopez, 2008), which would reduce our set to 115M rules; or a minimum count (mincount) threshold (Zollmann et al., 2008), which would reduce our set to 78M (mincount=2) or 57M (mincount=3) rules."
E09-1044 C08-1144 o "(Zollmann et al., 2008)."
E09-1044 C08-1144 o "This is in direct contrast to recent reported results in which other filtering strategies lead to degraded performance (Shen et al., 2008; Zollmann et al., 2008)."
N09-1049 C08-1144 o "Extensions to Hiero Several authors describe extensions to Hiero, to incorporate additional syntactic information (Zollmann and Venugopal, 2006; Zhang and Gildea, 2006; Shen et al., 2008; Marton and Resnik, 2008), or to combine it with discriminative latent models (Blunsom et al., 2008)."
E09-1017 C08-1145 p "The fluency models hold promise for actual improvements in machine translation output quality (Zwarts and Dras, 2008)."
A97-1055 C94-2113 o "(Dolan, 1994) and (Krovetz and Croft, 1992) claim that fine-grained semantic distinctions are unlikely to be of practical value for many applications."
D07-1107 C94-2113 o "Much work has gone into methods for measuring synset similarity; early work in this direction includes (Dolan, 1994), which attempted to discover sense similarities between dictionary senses."
J98-1001 C94-2113 o "Recognizing this, Dolan (1994) proposes a method for ""ambiguating"" dictionary senses by combining them to create grosser sense distinctions."
J98-1003 C94-2113 o "Various approaches to word sense division have been proposed in the literature on WSD, including (1) sense numbers in every-day dictionaries (Lesk 1986; Cowie, Guthrie, and Guthrie 1992), (2) automatic or hand-crafted clusters of dictionary senses (Dolan 1994; Bruce and Wiebe 1995; Luk * Department of Computer Science, National Tsing Hua University, Hsinchu 30043, Taiwan, ROC."
J98-1003 C94-2113 o "Furthermore, as pointed out in Dolan (1994), the sense division in an MRD is frequently too fine-grained for the purpose of WSD."
J98-1003 C94-2113 o "82 Chen and Chang Topical Clustering Dolan (1994) maintains the position that intersense relations are mostly idiosyncratical, thereby making it difficult to characterize them in a general way so as to identify them."
J98-1003 C94-2113 o "However, they do not elaborate on how the comparisons are done, or on how effective the program is. Dolan (1994) describes a heuristic approach to forming unlabeled clusters of closely related senses in an MRD."
J98-1003 C94-2113 o "As noted in Dolan (1994), it is possible to run a sense-clustering algorithm on several MRDs to build an integrated lexical database with more complete coverage of word senses."
J98-1003 C94-2113 o "These relations are then used for various tasks, ranging from the interpretation of a noun sequence (Vanderwende 1994) or a prepositional phrase (Ravin 1990), to resolving structural ambiguity (Jenson and Binot 1987), to merging dictionary senses for WSD (Dolan 1994)."
P06-1014 C94-2113 o "5 Related Work Dolan (1994) describes a method for clustering word senses with the use of information provided in the electronic version of LDOCE (textual definitions, semantic relations, domain labels, etc.)."
W00-0103 C94-2113 p "This approach took inspiration from the pioneering work by (Dolan 1994), but it is also fundamentally different, because instead of grouping similar senses together, the CoreLex approach groups together words according to all of their senses."
W06-2503 C94-2113 o "There is also work on grouping senses of other inventories using information in the inventory (Dolan, 1994) along with information retrieval techniques (Chen and Chang, 1998)."
W96-0305 C94-2113 o "Recently, various approaches (Dolan 1994; Luk 1995; Yarowsky 1992; Dagan et al. 1991 ;Dagan and Itai 1994) to word sense division have been used in WSD research."
W96-0305 C94-2113 o Zero derivation Dolan (1994) pointed out that it is helpful to identify zero-derived noun/verb pairs for such tasks as normalization of the semantics of expressions that are only superficially different.
W96-0305 C94-2113 o Dolan (1994) described a heuristic approach to forming unlabeled clusters of closely related senses in a MRD.
W96-0305 C94-2113 o Dolan (1994) observed that sense division in MRD is frequently too free for the purpose of WSD.
W99-0505 C94-2113 o "Towards a Meaning-Full Comparison of Lexieal Resources Kenneth C Lltkowska CL Research 9208 Gue Road Damascus, MD 20872 ken@clres corn http//www tires tom Abstract The mapping from WordNet to Hector senses m Senseval provides a ""gold standard"" against wluch to judge our ability to compare lexlcal resources The ""gold standard"" is provided through a word overlap analysis (with and without a stop list) for flus mapping, achieving at most a 36 percent correct mapping (inflated by 9 percent from ""empty"" assignments) An alternaUve componenttal analysis of the defimtaons, using syntacUc, collocatmnal, and semantac component and relation identification (through the use ofdefimng patterns integrated seamlessly mto the parsing thclaonary), provides an almost 41 percent correct mapping, with an additaonal 4 percent by recogmzmg semantic components not used in the Senseval mapping Defimtion sets of the Senseval words from three pubhshed thclaonanes and Dorr's lextcal knowledge base were added to WordNet and the Hector database to exanune the nature of the mapping process between defimtton sets of more and less sco\[~e The tecbauques described here consUtute only an maaal implementation of the componenUal analysis approach and suggests that considerable further improvements can be aclueved Introduction The difficulty of companng lemcal resources, long a s~gnfficant challenge in computauonal hnguistlcs (Atlans, 1991), came to the fore in the recent Senseval competatton (IOlgarnff, 1998), when some systems that relied heavily on the WordNet (Miller, et al, 1990) sense inventory were faced with the necessity of using another sense inventory (Hecto0 A hasty solutaon to the problem was the "" development of a map between the two inventories, but some part~cipants expressed concerns that use of flus map may have degraded their performance to an unknown degree Although there were disclaimers about the WordNet-Hector map, it nonetheless stands as a usable gold standard for efforts to compare lexical resources Moreover, we have a usable baseline (a word overlap method suggested m (Lesk, 1986)) against which to compare whether we are able to make improvements m the mapping (since flus method has been shown to perform not as well as expected (Krovetz, 1992)) We first describe the lextcal resources used m the study (Hector, WordNet, other dicUonanes, and a lex~cal knowledge base), first characterizing them in terms ofpolysemy and the types of leracal mformaUon each contmns (syntacUc properties and features, semantac components and relaUons, and collocaUonal properties) We then present results of perfornung the word overlap analysis of the 18 verbs used m Senseval, analyzing the definitions m WordNet and Hector We then expand our analysis to include other dictionaries We describe our methods of analysis, particularly the methods of parsing defimtaons and identff)qng semantic relations (semrels) based on defimng patterns, essentially takang first steps m Implementing the program described by Atkms and focusmg on the use of""meamng"" full mformataon rather than statistical mformaUon We identify the results that have been achieved thus far and outline further steps that may add more ""meanmg"" to the analysis IAll analyses described m this paper were performed automatically using functlonahty incorporated m DIMAP (Dictionary Maintenance Programs) (available for immediate download at (CL Research, 1999a)) This includes automatac extracuon of WordNet reformation for the selected words (mtegrated m DIMAP) Hector defimtlons were uploaded into DIMAP dicUonanes after use of a conversmn program Defimtlons for other 30 The Lexical Resources Tlus analysis focuses on the mmn verb senses used In Senseval (not ichoms and phrases), specifically the followmg AMAZE, BAND, BET, BOTHER, BURY, CALCULATE, CONSUME, DERIVE, FLOAT, HURDLE, INVADE, PROMISE, SACK, SANCTION, SCRAP, SEIZE, SHAKE, SLIGHT The Hector database used In Senseval consists of a tree of senses, each of which contains defimttons, syntactic properties, example usages, and ""clues"" (collocational information about the syntactic and semantic enwronment in wluch a word appears in the spectfic sense) The WordNet database contmns synonyms (synsets), perhaps a defimtton or example usages (gloss), some syntactic mformaUon (verb frames), hypernyms, hyponyms, and some other semrels (ENTAILS, CAUSES) To extend our analysis In order to look at other issues of lexacal resource comparison, we have included the defirauons or leracal information from the following additional sources Webster's 3 ra New International Dictionary (W3) Oxford Advanced l.earners D~ctlonary (OALD) American Hentage DlcUonary (AI-ID) Dorr's Lexacal Knowledge Base (Dorr) We used only the defimuons from W3, OALD, and AHD (which also contmn sample usages and some collocattonal information m the form of usage notes, not used at the present tame) Dorr's database contains thematic grids wluch characterize the thematic roles of obligatory and optional semanuc components, frequently identifying accompanying preposmons (Olsen, et al, 1998) The following table identities the number of senses and average overall polysemy for each of these resources dictionaries were entered by hand Word amaze band bet bother bury calculate consume denve float hurdle invade pronuse sack sanction scrap seize shake shght Average Polysemy o o o 1 2 4 2 3 1 II 4 4 2 5 5 7 6 9 7 12 6 14 5 5 5 10 9 6 6 8 8 6 5 15 5 16 4 41 14 2 1 4 3 6 2 10 5 5 4 7 4 4 4 6 3 2 2 5 2 3 1 3 3 11 6 21 13 8 8 37 17 1 1 6 3 O 1 2 2 4 1 3 4 4 8 1 3 1 3 1 3 2 10 5 1 0 3 1 3 2 2 0 1 1 1 0 7 1 7 12 I 0 57 37 120 62 34 22 Word Overlap Analysis We first estabhsh a baseline for automatic replication of the lexicographer's mappmg from WordNet 1 6 to Hector, using a s~mple word overlap analysis smular to (Lesk, 1986) The lextcographer mapped the 66 WordNet senses (each synset m which a test occurred) Into 102 Hector senses A total of 86 assignments were made, 9 WordNet senses were gwen no assignments, 40 recewed exactly one, and 17 senses received 2 or 3 asssgnments The WordNet senses contained 348 words (about half of wluch were common words appeanng on our stop list, which contained 165 words, mostly preposmons, pronouns, and conjunctions) The Hector senses selected m the word overlap analysis contained about 960 words (all Hector senses contained 1878 words) We performed a strict word overlap analysts (with and wsthout a stop hst) between tile definlUons in WordNet and the Hector senses, that is, we did not attempt to ldenttfy root forms of Inflected words We took each word m a WordNet sense and determined whether ~t appeared in a Hector sense, we selected a Hector sense based on the highest percentage of words over all Hector senses An 31 empty selection was made ff all the words in the WordNet sense did not appear in any Hector sense, only content words were considered when the stop hst was used For example, for bet, WordNet sense 2 (stake (money) on the outcome of an issue) mapped into Hector sense 4 ((of a person) to risk (a sum of money or property) m thts way) In this case, there was an overlap on two words (money, 039 in the Hector defimtlon (0 13 of its 15 words) without the stop list When the stop list was invoked, there was an overlap of only one word (money, 0 07 of the Hector defimtion) In this case, the lexicographer had made three assignments (Hector senses 2, 3, and 4), our scoring method treated flus as only 1 out of 3 correct (not using the relaxed method employed in Senseval of treating flus as completely correct) Without the stop hst, our selections matched the lexicographer's in 28 of 86 cases (32 6%), using the stop list, we were successful in 31 of 86 cases (36 1%) The improvement arising when the stop list was used is deceptive, where 8 cases were due to empty assignments (so that only 23 cases, 26 7%, were due to matching content words) Overall, only 41 content words were involved in these 23 successes when the stop list was used, an average of I 8 content words To summanze the word overlap analysis (1) despite a ncher set of defimtions in Hector, 9 of 66 WordNet senses (13 6%) could not be assigned, (2) despite the greater detail in Hector senses compared to WordNet senses (2 8 times as many words), only 1 8 content words participated in the assignments, and (3) therefore, the defimng vocabulary between these two definition sets seems to be somewhat divergent Although it might appear as if the word overlap analysis does not perform well, this is not the case The analysis provides a broad overview of the defimuon companson process between two definmon sets and frames a deeper analysis of the differences Moreover, it appears that the accuracy of a ""gold standard"" mapping is not crucially important The quality of the mapping may help frame the subsequent analysis more precisely, but it seems sufficient that any reasonable mapping will suffice This will be discussed further after presenting the results of the componentlal analysis of the defimtlons 32 Meaning-Full Analysis of Definitions The deeper analysis of the mapping between two defimtion sets relies primarily on two major steps (1) parsing definitions and using defimng patterns to identify semrels present m the definitions and (2) relaxing values to these relations by allowing ""synonymic"" substitution (using WordNet) Thus, for example, ffwe identify hypernyms or instruments from parsing a defimtion, we would say that the defimtions are ""equal"" not just ffthe hypernym or instrument is the same word, but also Lf the hypernyms or instruments are members of the same synset This approach is based on the finding (Litkowski, 1978) that a dictionary induces a semantic network where nodes represent ""concepts"" that may be lexicahzed and verbalized in more than one way This finding implies, in general, the absence of true synonyms, and instead the kind of ""concept"" embodied in WordNet synsets (with several lexical items and phraseologles) A slmdar approach, parsing defimtlons and relaxing semrel values, was followed in (Dolan, 1994) for clnstenng related senses w~thin a single dictionary The ideal toward which this approach strives is a complete identification of the meamng components included in a defimtion The meaning components can include syntactic features and charactenstlcs (including subcategonzation patterns), semantm components (realized through identification of semrels), selectional restrictions, and coUocational specifications The first stage of the analysis parses the definitions (CL Research, 1999b, Litkowski, to appear) and uses the parse results to extract (via defining patterns) semrels Since definitions have many idiosyncrasies (that do not follow ordinary text), an important first step in this stage is preprocessmg the definition text to put it into a sentence frame that facilitates the extraction of semrels 2 2Note that the stop hst is not applicable to the definition parsing The parser is a full-scale sentence parser, where prepositmns and other words on the stop list are necessary for successful parsing Moreover, inclusion of the prepositions is cmcml to the method, since they are the bearers of much semrel information The extractmn of semrels examines the parse results, a e, a tree whose mtermedaate nodes represent non-ternunals and whose leaves represent the lextcal atems that compnse the defimuons, where any node may also include annotations such as characterizations of number and tense For all noun or verb defimttons, flus includes Identification of the head noun (with recogmtton of""empty"" heads) or verb, for verbs, we signal whether the defimtaon contmned any selecttonal restnctmus (that as, pamcular parenthesazed expressaons) for the subject and object We then exanune preposattonal phrases In the defimUon and deterrmne whether we have a ""defining pattern"" for the preposaUon whach we can use as mdacaUve of a partacular semrel We also identify adverbs m the parse tree and look these up in WordNet to adentffy an adjecuve synset from wluch they are derived (if one is gwen) The defimng pattems are actually part of the dictionary used by the parser That is, we do not have to develop specafic routines to look for specLfic patterns A defimng pattern ~s a regular expressaon that arlaculates a syntactac pattern to be matched Thus, to recograze a ""manner"" semrel, we have the foUowmg entry for ""m"" m(dpat((~ rep0 l(det(0)) adj manner(0) st(manner)))) This allows us to recognize ""m"" as possibly gwmg rise to a ""manner"" component, where we recogmze ""m"" (the tdde, which allows us to specify partacular elements before the ""m"" as well), vath a noun phrase that consasts of 0 or 1 determiner, an adjectwe, and the lateral ""manner"" The '0 after the detenmner and the hteral mdacate that these words are not copied into the value for a ""manner"" role, so that the value to the ""manner"" semrel becomes only the adjectwe that as recogmzed The second stage of the analysis uses the populated lexacal database to compare senses and make the selectaons This process follows the general methodology used m Senseval (Lltkowska, to appear) Specifically, m the defimtaon comparison, we first exanune exclusaon cntena to rule out specific mappings These criteria include syntacUc properUes (e g, a verb sense that Is only transluve cannot map into one that Is only mtransRave) and collocataonal propertaes (e g, a sense that is used with a parUcle cannot map into one that uses a different particle) At the present tune, these are used only rmmmally 33 We next score each viable sense based on rots semrels We increment the score ff the senses have a common hypernym or If a sense's hypernyms belong to the same synset as the other sense's hypernyms If a parUcular sense con~ns a large number of synonyms (that as, no differentiae on the hypernym) and they overlap consaderably m the synsets they evoke, the score can be increased substanUally Currently, we add 5 points for each match 3 We increment the score based on common semrels In tins amtml tmplementaUon, we have defimng patterns (usually qmte nummal) for recogmzmg Instrument, means, location, purpose, source, manner, has-constituents, has-members, is-part-of, locale, and goal 4 We Increment the score by 2 points when we have a common semrel and then by another 5 points when the value Is ~dentacal or m the same synset After all possable increments to the scores have been made, we then select the sense(s) w~th the lughest score Finally, we compare our selecuon with that of the gold standard to assess our mapping over all senses Another way an wluch our methodology follows the Senseval process as that at proceeds incrementally Thus, ~t ms not necessary to have a ""final"" perfect parse and mapping rouUne We can make conUnual refinements at any stage of the process and exarmne the overall effect As m Senseval, we may make changes to deal wath a particular phenomenon with the result that overall performance dechnes, but w~th a sounder basis for making subsequent amprovements Results of Componential Analysis The ""gold standard"" analysis Involves mapping 66 WordNet senses with 348 words into 102 Hector senses with 1878 words Using the method described above, we obtained 35 out of 86 correct 3At the present tame, we use WordNet to adentffy semreis We envaslon usmg the full semanlac network created by parsing all a dlcUonary's defimtaons Thas would include a richer set of semrels than currently included m WordNet 4The defimng patterns are developed by hand We have onlyJust begun this effort, so the current set ms somewhat Impoverished mappmgs (407%), a shght improvement over the 31 correct assignments usmg the stop-last word overlap techmque However, as mentioned above, the stophst techmque had aclueved 8 of its successes by matclung null assignments Consadered on tlus basins, ~t seems that the componentaal analysis techmque provides substantial ~mprovement In addition, our technique ""erred"" on 4 cases by malang assagnments where none were made by the leracographer We suggest that these cases do con~n some common elements of meaning and may conceivably not be construed as errors The mapping from WordNet to Hector had relatavely few empty mappings, senses for wtuch It was not possable to make an assignment These are the cases where at appears that the chetmnanes do not overlap and thus prowde a tentative mdacataon of where two dictionaries may have different coverage The cases of multiple assignments mchcate the degree ofamblgmty m the mapping The average m both darecUons between Hector and WordNet were donunated by the mabdaty to obtain good dascnnunatton for the word ""semze"" Thus, tlus method identifies individual words where the &scnnunatwe ablhty needs to be further refined Perhaps more importantly, the componentml analysis method exploits consaderably more WordNet Hector mformauon than the word overlap methods Whereas the stop-hst word overlap mapping was based on only 41 content words, the componenual ~ approach (In the selected mappings) had 228 hits in ~.~ developing ats scores, with only a small number of ~ .~ ~ defining patterns Comparison of Dictionaries tel O ~3 0'3 We next exanuned the nature of the mterrelalaons between parrs of chctaonanes w~thout use of a ""gold standard"" to assess the process of mapping For t/us purpose, we mapped m both &recttons between the paars {WordNet, Hector}, {W3, OALD}, and {W3, AHD We exanune Dorr's lexacal knowledge base for the amphcatlons It may have m the mapping process Neither WordNet nor Hector are properly v~ewed as chcuonanes, since there was no mtenuon to pubhsh them as such WordNet ""glosses"" are generally smaller (53 words per sense) compared to Hector (184 words per sense), whach contains many words specff3nng selectmnal restnct~ons on the subject and object of the verbs Hector was used primarily for a large-scale sense tagging project The three formal d~ctmnanes were subject to rigorous pubhslung and style standards The average number of words per sense were 87 (OALD), 7 1 (AHD), and 9 9 (W3), w~th an average of 3 4, 62, and 120 senses per word Each table shows the average number of senses being mapped, the average number of assignments m the target dlCtmnary, the average number of senses for which no assagnment could be made, the average number of mulUple assignments per word, and the average score of the assignments that were made WN-Hector 37 47 06 17 119 Hector-WN 57 64 14 22 113 These points are further emphasized m the mapping between W3 and OALD, where the disparity between the empty and mulUple assagnments indicate that we are mapping between dictionaries qmte disparate This tends to be the case not only for the enUre set of words, but also is evident for individual words where there is a considerable d~spanty m the number of senses, wtuch then dominate the overall dlspanty Thus, for example, W3 has 41 defimUons for ""float"", while OALD has 10 We tend to be unable to find the specific sense m going from W3 to OALD, because at is likely that we have many more specific defimtlons that are not present In the other direction, we are hkely to have considerable ambiguity and multiple assignments W3-OALD OALD-W3 W3 OALD 120 78 60 18 99 34 60 07 32 86 34 A Between W3 and AHD, there ss less overall daspanty between the defimtaon sets, although since W3 Is tmabndged, we stall have a relatavely lugh number of senses m W3 that do not appear to be present m AHD Finally, It should be noted that the scores for the published dictaonanes tend to be a little lower than for WordNet and Hector Tlus reflects the hkehhood that we have not extracted as much mformataon as we dad m parsing and analyzmg the defimtaon sets used m Senseval W3 AHD oJ 'q O W3-AHD 120 115 40 36 90 AHD-W3 6 2 9 1 1 2 4 1 9 1 We next considered Dorr's lexacal database We first transformed her theta grids to syntactic spectflcataons (transttave or lntransmttve) and identtficataon of semreis (e g, where she Identified an instr component, we added such a semrel to the DIMAP sense) We were able to identify a mappmg from WordNet to her senses for two words (""float"" and ""shake"") for wluch Dorr has several entries However, smce she has considerably more semanuc components than we are currently able to recogmze, we dad not pursue this avenue any further at flus time More important than just mappmg between two words, Dorr's data mdacates the posstbday of further exploitation of a richer set of semanUc components Spectfically, as reported m (Olsen, et al, 1998), m descnbmg procedures for automatically acqumng thematic grids for Mandann Chinese, ~t was noted that ""verbs that incorporate themaUc elements m their meamng would not allow that element to appear m the complement structure"" Thus, by usmg Dorr's thematic grids when verb are parsed m defimtaons, it ~s possible to ~dentffy where partacular semantac components are lexicahzed and which others are transnutted through to the themaUc grid (complement or subcategonzataon pattern) for the defimendum The transmiss~on of semantic components to the thematic gnd ~s also reflected overtly m many defimtlons For example, shake has one definition, ""to bnng to a specified condatton by or as ffby repeated qmck jerky movements"" We would thus expect that the thematac grid for this defimtaon should include a ""goal"" And, deed, Dorr's database has two senses whch reqmre a ""goal"" as part of their thematic grid Smularly, for many defimtaons m the sample set, we ~dentLfied a source defimng pattern based on the word ""from,"" frequently, the object of the preposmon was the word ""source"" ttseff, mdacatmg that the subcategonzaUon, properties of the defimendum should elude a source component Discussion Wlule the improvement m mapping by using the componentaal analysis techmque (over the word overlap methods) is modest, we consider these results qmte slgmficant m wew of the very small number of defimng patterns we have Implemented Most of the improvement stems from the word substatuUon pnnclple described earlier (as ewdenced by the preponderance of 5 point scores) This techmque also provides a mechamsm for bnngmg back the stop words, wz, the preposmons, wluch are the careers of mformatmn about semrels (the 2 point scores) The more general conclusion (from the word subsutuuon) is that the success arises from no longer considenng a defimtmn m ~solation The proper context for a word and its defimtions consists not .lUSt of the words that make up the definition, but also the total semantac network represented by the dictaonary We have aclueved our results by explomng only a small part of that network We have moved only a few steps to that network beyond the mdawdual words and their definitions We would expect that further expansmn, first by the addon of further and ~mproved semrel defining patterns, and second, through the identaficataon of more pnmmve semanuc components, will add considerably to our abflay to map between lexacal resources We also expect ~mprovements from consideration of other techniques, such as attempts at ontology ahgnment (Hovy, 1998) Although tile definition analysis provlded here was performed on definmons with a stogie language, the vanous meamng components m m m m m m m m 35 correspond to those used in an Interhngua The use of the exUncuon method (developed m order to charactenze verbs m another language, Clunese) can frmtfully be applied here as well Two further observaUons about tlus process can be made The first is that rchance on a wellestablished semantic network such as WordNet,s not necessary The componenUal analysis method rehes on the local neighborhood of words m the defimUons, not on the completeness of the network Indeed, the network ~tsel can be bootstrapped based on the parsing results The method can work vath any semanUc network or ontology and may be used to refine or flesh out the network or ontology The second observation is that it is not necessary to have a well-estabhshed ""gold standard"" Any mapping vail do All that Is necessary is for any mvesugator (lemcographer or not) to create a judgmental mappmg The methods employed here can then quanufy ttus mapping based on a word overlap analysis and then further examine tt based on the componenaal analysis The componenUal analysis method can then be used to exanune underlying subtleUes and nuances tn the defimUous, wluch a lemcographer or analyst can then examine m further detail to assess the mapping Future Work Tlus work has marked the first ume that all the necessary mfrastructure has been combmed tn a rudimentary form Because of its rudimentary status, the opportumUes for improvement are quite extensive In addlUon, there are many opportumUes for using the techmques descnbed here m further NLP apphcatlons First, the techmques described here have immediate apphcabtllty as part of a lexicographer's workstaUon When defimUons are parsed and semrels are zdenttfied, the resulUng data structures can be apphed against a corpus of instances for parUcular words (as m Senseval) for improving word-sense disamblguaUon The techmques will also permit comparing an entry vath Itself to deternune the mterrelattonshtps among ~ts defimUons and of companng the defimUons of two ""synonyms"" to deternune the amount of overlap between them on a defimtlon by defimUon bas~s Although the analys,s here has focused on the parsing of defimUous, the development of defimng patterns clearly extends to generalized text parsing since the defimng patterns have been incorporated mto the same chcttonary used for parsing free text, the patterns can be used threctly to identify the presence of parUcular semrels among sentenual consUtuents We are working to integrate th~s funcUonahty into our word-sense &sambiguaUon techruques (both the defimng patterns and the semrels) Even further, mt seems that matclung defimng patterns in free text can be used for lextcal acquisition Textual matenal that contains these patterns could concewably be flagged as providing defimUonal matenal which can then be compared to emstmg defimUons to assess whether their use ts cous,stent vath these defimUons, and ff not, at least to flag the inconsistency The tecluuques descnbed here can be apphed directly to the fields of ontology development and analysis of ternunologlcal databases For ontoiogles, vath or w~thout defimuons, the methods employed can be used to compare entries m dai'erent ontologles based pnmanly on the relattous m the ontology, both luerarclucal and other For ternunologlcal databases, the methods descnbed here can be used to exanune the set of conceptual relaUons lmphed by the defimtmus The defimuon parsing wall facd~tate the development of the termmolog~ca I network tn the pamcular field covered by the database The componenUal analysts methods result m a richer semantic network that can be used m other apphcattous Thus, for example, ~t ts possible to extend the leracal chatmng methods described m (Green, 1997), which are based on the semrels used m WordNet The semrels developed with the componenttal analysis method would provide additional detad available for apphcauon of lexlcal cohesion methods In particular, addtUonal relattous would penmt some structunng wmthm the individual leracal chams, rather than just consldenng each cham as an amorphous set (Green, 1999) Finally, we are currently investigating the use of the componenUal analysts techmque for mformauon extracUon The techmque identifies (from defimtlous) slots that can be used as slots or fields m template generataon Once these slots are identified, we wall be attemptmg to extract slot values from Items m large catalog databases (mdhons of items) 36 In conclusion, it would seem that, instead of a paucity of tnformation allovang us to compare lexmal resources, by bnngmg m the full semantic network of the lexicon, we are overwhelmed with a plethora of data Acknowledgments I would like to thank Bonnie Dorr, Chnstiane Fellbaum, Steve Green, Ed Hovy, Ramesh Knshnamurthy, Bob Krovetz, Thomas Potter, Lucy Vanderwende, and an anonymous reviewer for their comments on an earlier draft of this paper References Atlans, B T S (1991) Bmldmga lexicon The contribution of lexicography lnternattonal Journal of Lextcography, 4(3), 167-204 CL Research (1999a) CL Research Demos http//www clres com/Demo html CL Research (1999b) Dmtlonary Parsing Project http//www clres com/dpp html Dolan, W B (1994, 5-9 Aug) Word Sense Amblguation Chistenng Related Senses COLING-94, The 15th International Conference on Computational Linguistics Kyoto, Japan Green, S J (1997) Automatically generating hypertext by computing semantic smulanty \[Dlss\], Toronto, Canada Umverstty of Toronto Green, S J (Sjgreen@mn mq edu au) (1999, 1 June) (Rich semantic networks) Hovy, E (1998, May) Combining and Standardizing Large-Scale, Practical Ontologms for Machine Translation and Other Uses Language Resources and Evaluation Conference Granada, Spam Kalgarnff, A (1998) SENSEVAL Home Page http//www itn bton ac uk/events/senseval/ Krovetz, R (1992, June) Sense-Linking m a Machine Readable Dictionary 30th Annual Meeting of the Association for Computational Lmgu~stics Newark, Delaware Association for Computational Lmgtustics Lesk, M (1986) Automatic Sense Dlsamblguation Using Machine Readable Dmttonanes How to Tell a Pine Cone from an Ice Cream Cone Proceechngs of SIGDOC Lttkowski, K C (1978) Models of the semantic structure of dictionaries American Journal of Computattonal Lmgutsttcs, Atf 81, 25-74 Lttkowskl, K C (to appear) SENSEVAL The CL Research Expenence Computers and the Humamttes Mtller, G A, Beckwlth, R, Fellbaum, C, Gross, D, & Miller, K J (1990) Introduction to WordNet An on-hne lexical database lnternatwnal Journal of Lexicography, 3(4), 235-244 Olsen, M B, Dorr, B J, & Thomas, S C (1998, 28-31 October) Enhancmg Automatic Acqulsmon of Thematic Structure in a Large-Scale Lexacon for Mandann Chinese Tlurd Conference of the Association for Machine Translation m the Americas, AMTA-98 Langhorne, PA"
C08-1009 C98-2122 o "On the British National Corpus (BNC), using Lins (1998) similarity method, we retrieve the following neighbors for the first and second sense, respectively: 1."
C08-1009 C98-2122 o "As described in Section 3 we retrieved neighbors using Lins (1998) similarity measure on a RASP parsed (Briscoe and Carroll, 2002) version of the BNC."
C08-1009 C98-2122 o The best accuracies are observed when the labelsarecreatedfromdistributionallysimilarwords using Lins (1998) dependency-based similarity measure (Depend).
C08-1009 C98-2122 p "Lins (1998) information-theoretic similarity measure is commonly used in lexicon acquisition tasks and has demonstrated good performance in unsupervised WSD (McCarthy et al., 2004)."
C08-1009 C98-2122 n A potential caveat with Lins (1998) distributional similarity measure is its reliance on syntactic information for obtaining dependency relations.
C08-1029 C98-2122 p "Point-wise mutual information (Lin, 1998) and Relative Feature Focus (Geffet and Dagan, 2004) are well-known examples."
C08-1029 C98-2122 o "Feature comparison measures: to convert two feature sets into a scalar value, several measures have been proposed, such as cosine, Lins measure (Lin, 1998), Kullback-Leibler (KL) divergence and its variants."
C08-1029 C98-2122 o "Lins measure Lin (1998) proposed a symmetrical measure: Par Lin (s t)= summationtext fF s F t (w(s,f)+w(t,f)) summationtext fF s w(s,f)+ summationtext fF t w(t,f) , where F s and F t denote sets of features with positive weights for words s and t, respectively."
C08-1051 C98-2122 o " Three K-means algorithms using different distributional similarity or dissimilarity measures: cosine, -skew divergence (Lee, 1999) 4 , and Lins similarity (Lin, 1998)."
C08-1051 C98-2122 o "Others proposed distributional similarity measures between words (Hindle, 1990; Lin, 1998; Lee, 1999; Weeds et al., 2004)."
C08-1051 C98-2122 o "405 PRF 1 proposed .383 .437 .408 multinomial mixture .360 .374 .367 Newman (2004) .318 .353 .334 cosine .603 .114 .192 -skew divergence (Lee, 1999) .730 .155 .255 Lins similarity (Lin, 1998) .691 .096 .169 CBC (Lin and Pantel, 2002) .981 .060 .114 Table 3: Precision, recall, and F-measure."
C08-1051 C98-2122 o "Applications of word clustering include language modeling (Brown et al., 1992), text classification (Baker and McCallum, 1998), thesaurus construction (Lin, 1998) and so on."
C08-1054 C98-2122 o (2005) applied the distributional similarity proposed by Lin (1998) to coordination disambiguation.
C08-1058 C98-2122 o "One is automatic thesaurus acquisition, that is, to identify synonyms or topically related words from corpora based on various measures of similarity (e.g. Riloff and Shepherd, 1997; Lin, 1998; Caraballo, 1999; Thelen and Riloff, 2002; You and Chen, 2006)."
C08-1086 C98-2122 o "By no means an exhaustive list, the most commonly cited ranking and scoring algorithms are HITS (Kleinberg 1998) and PageRank (Page et al. 1998), which rank hyperlinked documents using the concepts of hubs and authorities."
C08-1086 C98-2122 o "Within the NLP community, n-best list ranking has been looked at carefully in parsing, extractive summarization (Barzilay et al. 1999; Hovy and Lin 1998), and machine translation (Zhang et al. 2006), to name a few."
C08-1086 C98-2122 o "Following Lin (1998), we use syntactic dependencies between words to model their semantic properties."
C08-1100 C98-2122 o "For each word in the LDV, we consulted three existing thesauri: Rogets Thesaurus (Roget, 1995), Collins COBUILD Thesaurus (Collins, 2002), and WordNet (Fellbaum, 1998)."
C08-1100 C98-2122 o "Various methods (Hindle, 1990; Lin, 1998) of automatically acquiring synonyms have been proposed."
C08-1100 C98-2122 p "4.1 Features We used a dependency structure as the context for words because it is the most widely used and one of the best performing contextual information in the past studies (Ruge, 1997; Lin, 1998)."
C08-1107 C98-2122 o "Given a wordq, its set of featuresFq and feature weightswq(f) for f Fq, a common symmetric similarity measure is Lin similarity (Lin, 1998a): Lin(u,v) = summationtext fFuFv[wu(f)+wv(f)]summationtext fFu wu(f)+ summationtext fFv wv(f) where the weight of each feature is the pointwise mutual information (pmi) between the word and the feature: wq(f) =log[Pr(f|q)Pr(f) ]."
C08-1107 C98-2122 o "Texts are represented by dependency parse trees (using the Minipar parser (Lin, 1998b)) and templates by parse sub-trees."
C08-1117 C98-2122 p "Among these measures, the most important are Wu & Palmers (Wu and Palmer, 1994), Resniks (Resnik, 1995) and Lins (Lin, 1998)."
C08-1117 C98-2122 o "Where Pantel and Lin use Lins (1998) measure, we use Wu and Palmers (1994) measure."
C08-1117 C98-2122 p One of the most important is Lins (1998).
D08-1007 C98-2122 o "4 Experiments and Results 4.1 Set up We parsed the 3 GB AQUAINT corpus (Voorhees, 2002) using Minipar (Lin, 1998b), and collected verb-object and verb-subject frequencies, building an empirical MI model from this data."
D08-1007 C98-2122 o "Lin (1998a)s similar word list for eat misses these but includes sleep (ranked 6) and sit (ranked 14), because these have similar subjects to eat."
D08-1007 C98-2122 o "Discriminative, context-specific training seems to yield a better set of similar predicates, e.g. the highest-ranked contexts for DSPcooc on the verb join,3 lead 1.42, rejoin 1.39, form 1.34, belong to 1.31, found 1.31, quit 1.29, guide 1.19, induct 1.19, launch (subj) 1.18, work at 1.14 give a better SIMS(join) for Equation (1) than the top similarities returned by (Lin, 1998a): participate 0.164, lead 0.150, return to 0.148, say 0.143, rejoin 0.142, sign 0.142, meet 0.142, include 0.141, leave 0.140, work 0.137 Other features are also weighted intuitively."
D08-1007 C98-2122 o "We also test an MI model inspired by Erk (2007): MISIM(n,v) = log summationdisplay nSIMS(n) Sim(n,n) Pr(v,n ) Pr(v)Pr(n) We gather similar words using Lin (1998a), mining similar verbs from a comparable-sized parsed corpus, and collecting similar nouns from a broader 10 GB corpus of English text.4 We also use Keller and Lapata (2003)s approach to obtaining web-counts."
D08-1007 C98-2122 p Erk (2007) compared a number of techniques for creating similar-word sets and found that both the Jaccard coefficient and Lin (1998a)s information-theoretic metric work best.
D08-1048 C98-2122 o "They have been successfully applied in several tasks, such as information retrieval (Salton et al., 1975) and harvesting thesauri (Lin, 1998)."
D08-1048 C98-2122 o "Two LUs close in the space are likely to be in a paradigmatic relation, i.e. to be close in a is-a hierarchy (Budanitsky and Hirst, 2006; Lin, 1998; Pado, 2007)."
D08-1084 C98-2122 p "This similarity score is computed as a max over a number of component scoring functions, some based on external lexical resources, including: various string similarity functions, of which most are applied to word lemmas measures of synonymy, hypernymy, antonymy, and semantic relatedness, including a widelyused measure due to Jiang and Conrath (1997), based on manually constructed lexical resources such as WordNet and NomBank a function based on the well-known distributional similarity metric of Lin (1998), which automatically infers similarity of words and phrases from their distributions in a very large corpus of English text The ability to leverage external lexical resources both manually and automatically constructedis critical to the success of MANLI."
D08-1103 C98-2122 o "Distributional measures of distance, such as those proposed by Lin (1998), quantify how similar the two sets of contexts of a target word pair are."
D08-1103 C98-2122 o "For each word pair from the antonym set, we calculated the distributional distance between each of their senses using Mohammad and Hirsts (2006) method of concept distance along with the modified form of Lins (1998) distributional measure (equation 2)."
D08-1103 C98-2122 o Again we used Mohammad and Hirsts (2006) method along with Lins (1998) distributional measure to determine the distributional closeness of two thesaurus concepts.
D09-1028 C98-2122 o Curran (2002) and Lin (1998) use syntactic features in the vector definition.
D09-1084 C98-2122 o "Accurate measurement of semantic similarity between lexical units such as words or phrases is important for numerous tasks in natural language processing such as word sense disambiguation (Resnik, 1995), synonym extraction (Lin, 1998a), and automatic thesauri generation (Curran, 2002)."
D09-1084 C98-2122 o Method Correlation Edge-counting 0.664 Jiang & Conrath (1998) 0.848 Lin (1998a) 0.822 Resnik (1995) 0.745 Li et al.
D09-1084 C98-2122 o "(Strube and Ponzetto, 2006) 0.19-0.48 Leacock & Chodrow (1998) 0.36 Lin (1998b) 0.36 Resnik (1995) 0.37 Proposed 0.504 7 Conclusion We proposed a relational model to measure the semantic similarity between two words."
D09-1084 C98-2122 o Lin (1998b) defined the similarity between two concepts as the information that is in common to both concepts and the information contained in each individual concept.
D09-1089 C98-2122 o "Pereira et al.(1993), Curran and Moens (2002) and Lin (1998) use syntactic features in the vector definition."
E09-1077 C98-2122 o Wiebe (2000) uses Lin (1998a) style distributionally similar adjectives in a cluster-and-label process to generate sentiment lexicon of adjectives.
E09-1077 C98-2122 o "3http://www.openoffice.org Another corpora based method due to Turney and Littman (2003) tries to measure the semantic orientation O(t) for a term t by O(t) = summationdisplay tiS+ PMI(t,ti) summationdisplay tjS PMI(t,tj) where S+ and S are minimal sets of polar terms that contain prototypical positive and negative terms respectively, and PMI(t,ti) is the pointwise mutual information (Lin, 1998b) between the terms t and ti."
I08-1021 C98-2122 o "Our approach to STC uses a thesaurus based on corpus statistics (Lin, 1998) for real-valued similarity calculation."
I08-1060 C98-2122 o "Some researchers (Hindle, 1990; Grefenstette, 1994; Lin, 1998) classify terms by similarities based on their distributional syntactic patterns."
I08-1072 C98-2122 o "A wide range of contextual information, such as surrounding words (Lowe and McDonald, 2000; Curran and Moens, 2002a), dependency or case structure (Hindle, 1990; Ruge, 1997; Lin, 1998), and dependency path (Lin and Pantel, 2001; Pado and Lapata, 2007), has been utilized for similarity calculation, and achieved considerable success."
I08-1072 C98-2122 o "3.1 Context Extraction We adopted dependency structure as the context of words since it is the most widely used and wellperforming contextual information in the past studies (Ruge, 1997; Lin, 1998)."
I08-1072 C98-2122 o "For each word in LDV, three existing thesauri are consulted: Rogets Thesaurus (Roget, 1995), Collins COBUILD Thesaurus (Collins, 2002), and WordNet (Fellbaum, 1998)."
I08-1073 C98-2122 o "We propose using distributional similarity (using (Lin, 1998)) as an approximation of semantic distancebetweenthewordsinthetwoglosses,rather than requiring an exact match."
I08-1073 C98-2122 o We adopt the similarity score proposed by Lin (1998) as the distributional similarity score and use 50 nearest neighbours in line with McCarthy et al. For the random baseline we select one word sense at random for each word token and average the precision over 100 trials.
I08-1073 C98-2122 o "2 Related Work ThisworkbuildsuponthatofMcCarthyetal.(2004) which acquires predominant senses for target words from a large sample of text using distributional similarity (Lin, 1998) to provide evidence for predominance."
I08-1073 C98-2122 o "In this approach we extend the denition overlap by considering the distributional similarity (Lin, 1998) rather than identify of the words in the two denitions."
I08-1073 C98-2122 o McCarthy et al. use a distributional similarity thesaurus acquired from corpus data using the method of Lin (1998) for nding the predominant sense of a word where the senses are dened by WordNet.
I08-1073 C98-2122 o "Let w be a target word and Nw = fn1,n2nkg be the ordered set of the top scoring k neighbours of w from the thesaurus with associated distributional similarity scores fdss(w,n1),dss(w,n2),dss(w,nk)g using (Lin, 1998)."