acl acl2011 acl2011-215 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Alexis Nasr ; Frederic Bechet ; Jean-Francois Rey ; Benoit Favre ; Joseph Le Roux
Abstract: MACAON is a tool suite for standard NLP tasks developed for French. MACAON has been designed to process both human-produced text and highly ambiguous word-lattices produced by NLP tools. MACAON is made of several native modules for common tasks such as a tokenization, a part-of-speech tagging or syntactic parsing, all communicating with each other through XML files . In addition, exchange protocols with external tools are easily definable. MACAON is a fast, modular and open tool, distributed under GNU Public License.
Reference: text
sentIndex sentText sentNum sentScore
1 i Abstract MACAON is a tool suite for standard NLP tasks developed for French. [sent-8, score-0.099]
2 MACAON has been designed to process both human-produced text and highly ambiguous word-lattices produced by NLP tools. [sent-9, score-0.075]
3 MACAON is made of several native modules for common tasks such as a tokenization, a part-of-speech tagging or syntactic parsing, all communicating with each other through XML files . [sent-10, score-0.254]
4 In addition, exchange protocols with external tools are easily definable. [sent-11, score-0.275]
5 MACAON is a fast, modular and open tool, distributed under GNU Public License. [sent-12, score-0.028]
6 Unlike native texts (texts produced by humans), this new kind of texts is the result of imperfect processors and they are made of several hypotheses, usually weighted with confidence measures. [sent-14, score-0.164]
7 Automatic text production systems can produce these weighted hypotheses as nbest lists, word lattices, or confusion networks. [sent-15, score-0.04]
8 It is crucial for this space of ambiguous solutions to be kept for later processing since the ambiguities of the lower levels can sometimes be resolved during highlevel processing stages. [sent-16, score-0.299]
9 ∗This work has been funded by the French Agence Nationale pour la Recherche, through the projects SEQUOIA (ANR-08EMER-013) and DECODA (2009-CORD-005-01) 86 . [sent-18, score-0.029]
10 fr is a suite of tools developped to process ambiguous input and extend inference of input modules within a global scope. [sent-20, score-0.403]
11 It consists in several modules that perform classical NLP tasks (tokenization, word recognition, part-ofMACAON speech tagging, lemmatization, morphological analysis, partial or full parsing) on either native text or word lattices. [sent-21, score-0.208]
12 MACAON is distributed under GNU public licence and can be downloaded from http : / /www . [sent-22, score-0.055]
13 i From a general point of view, a MACAON module can be seen as an annotation device1 which adds a new level of annotation to its input that generally depends on annotations from preceding modules. [sent-27, score-0.273]
14 The modules communicate through XML files that allow the representation different layers of annotation as well as ambiguities at each layer. [sent-28, score-0.325]
15 Moreover, the initial XML structuring of the processed files (logical structuring of a document, information from the Automatic Speech Recognition module . [sent-29, score-0.226]
16 As already mentioned, one of the main characteristics of MACAON is the ability for each module to accept ambiguous inputs and produce ambiguous outputs, in such a way that ambiguities can be resolved at a later stage of processing. [sent-33, score-0.359]
17 The compact representation of ambiguous structures is at the heart of the MACAON exchange format, described in section 2. [sent-34, score-0.333]
18 Furthermore every module can weight the solutions it produces. [sent-35, score-0.105]
19 such weights can be used to rank solutions or limit their number for later stages 1Annotation must be taken here in a general sense which includes tagging, segmentation or the construction of more complex objets as syntagmatic or dependencies trees. [sent-36, score-0.118]
20 Several processing tools suites alread exist for French among which SXPIPE (Sagot and Boullier, 2008), OUTILEX (Blanc et al. [sent-40, score-0.085]
21 A general comparison of MACAON with these tools is beyond the scope of this paper. [sent-42, score-0.056]
22 Let us just mention that MACAON shares with most of them the use of finite state machines as core data representation. [sent-43, score-0.128]
23 Some modules are implemented as standard operations on finite state machines. [sent-44, score-0.197]
24 MACAON can also be compared to the numerous development frameworks for developping processing tools, such as GATE4, FREELING5, ELLOGON6 or LINGPIPE7 that are usually limited to the processing of native texts. [sent-45, score-0.055]
25 The MACAON exchange format shares a certain number of features with linguistic annotation scheme standards such as the Text Encoding Initia- tive8, XCES9, or EAGLES10. [sent-46, score-0.501]
26 They all aim at defining standards for various types of corpus annotations. [sent-47, score-0.084]
27 The main difference between MACAON and these approaches is that MACAON defines an exchange format between NLP modules and not an annotation format. [sent-48, score-0.513]
28 More precisely, this format is dedicated to the compact representation of ambiguity: some information represented in the exchange format are to be interpreted by MACAON modules and would not be part of an annotation format. [sent-49, score-0.766]
29 Moreover, the MACAON exchange format was defined from the bottom up, originating from the authors’ need to use several existing tools and adapt their input/output formats in order for them to be compatible. [sent-50, score-0.404]
30 Still, MACAON shares several characteristics with the LAF (Ide and Romary, 2004) which aims at defining high level standards for exchanging linguistic data. [sent-52, score-0.244]
31 html 87 2 The MACAON exchange format The MACAON exchange format is based on four concepts: segment, attribute, annotation level and segmentation. [sent-74, score-0.786]
32 A segment refers to a segment of the text or speech signal that is to be processed, as a sentence, a clause, a syntactic constituent, a lexical unit, a named entity . [sent-75, score-0.371]
33 A segment can be equipped with attributes that describe some of its aspects. [sent-78, score-0.202]
34 A syntactic constituent, for example, will define the attribute type which specifies its syntactic type (Noun Phrase, Verb Phrase . [sent-79, score-0.031]
35 A segment is made of one or more smaller segments. [sent-83, score-0.173]
36 A sequence of segments covering a whole sentence for written text, or a spoken utterance for oral data, is called a segmentation. [sent-84, score-0.199]
37 An annotation level groups together segments of a same type, as well as segmentations defined on these segments. [sent-86, score-0.371]
38 Four levels are currently defined: pre-lexical, lexical, morpho-syntactic and syntactic. [sent-87, score-0.039]
39 Two relations are defined on segments: the precedence relation that organises linearly segments of a given level into segmentations and the dominance relation that describes how a segment is decomposed in smaller segments either of the same level or of a lower level. [sent-88, score-0.869]
40 We have represented in figure 2, a schematic representation of the analysis of the reconstructed output a speech recognizer would produce on the input time flies like an arrow11 . [sent-89, score-0.192]
41 Three annotation levels have been represented, lexical, morpho-syntactic and syntactic. [sent-90, score-0.105]
42 Each level is represented by a finitestate automaton which models the precedence relation defined over the segments of this level. [sent-91, score-0.376]
43 The segments are implicitly represented by the labels of the automaton’s arcs. [sent-93, score-0.224]
44 The dominance relations are represented with dashed lines that link segments of different levels. [sent-95, score-0.373]
45 This example illustrates the different ambiguity cases and the way they are represented. [sent-97, score-0.094]
46 NP PP Figure 1: Three annotation levels for a sample sentence. [sent-99, score-0.105]
47 Plain lines represent annotation hypotheses within a level while dashed lines represent links between levels. [sent-100, score-0.261]
48 Triangles with the tip up are “and” nodes and triangles with the tip down are “or” nodes. [sent-101, score-0.352]
49 In the chunking layer, segments that span multiple part-of-speech tags are linked to them through “and” nodes. [sent-103, score-0.174]
50 The most immediate ambiguity phenomenon is the segmentation ambiguity: several segmentations are possible at every level. [sent-104, score-0.205]
51 This ambiguity is represented in a compact way through the factoring of segments that participate in different segmentations, by way of a finite state automaton. [sent-105, score-0.459]
52 The second ambiguity phenomenon is the dominance ambiguity, where a segment can be decomposed in several ways into lower level segments. [sent-106, score-0.462]
53 Such a case appears in the preceding example, where the NN segment appearing in one of the outgoing transition of the initial state of the morpho-syntactic level dominates both thyme and time segments of the lexical level. [sent-107, score-0.564]
54 The triangle with the tip down is an “or” node, modeling the fact that NN corresponds to time or thyme. [sent-108, score-0.16]
55 They model the fact that the PP segment of the syntactic level dominates segments IN, DT and NN of the morpho-syntactic level. [sent-110, score-0.439]
56 1 XML representation The MACAON exchange format is implemented in XML. [sent-112, score-0.346]
wordName wordTfidf (topN-words)
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