emnlp emnlp2012 emnlp2012-136 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Jayant Krishnamurthy ; Tom Mitchell
Abstract: We present a method for training a semantic parser using only a knowledge base and an unlabeled text corpus, without any individually annotated sentences. Our key observation is that multiple forms ofweak supervision can be combined to train an accurate semantic parser: semantic supervision from a knowledge base, and syntactic supervision from dependencyparsed sentences. We apply our approach to train a semantic parser that uses 77 relations from Freebase in its knowledge representation. This semantic parser extracts instances of binary relations with state-of-theart accuracy, while simultaneously recovering much richer semantic structures, such as conjunctions of multiple relations with partially shared arguments. We demonstrate recovery of this richer structure by extracting logical forms from natural language queries against Freebase. On this task, the trained semantic parser achieves 80% precision and 56% recall, despite never having seen an annotated logical form.
Reference: text
sentIndex sentText sentNum sentScore
1 edu Abstract We present a method for training a semantic parser using only a knowledge base and an unlabeled text corpus, without any individually annotated sentences. [sent-3, score-0.627]
2 Our key observation is that multiple forms ofweak supervision can be combined to train an accurate semantic parser: semantic supervision from a knowledge base, and syntactic supervision from dependencyparsed sentences. [sent-4, score-1.386]
3 We apply our approach to train a semantic parser that uses 77 relations from Freebase in its knowledge representation. [sent-5, score-0.586]
4 This semantic parser extracts instances of binary relations with state-of-theart accuracy, while simultaneously recovering much richer semantic structures, such as conjunctions of multiple relations with partially shared arguments. [sent-6, score-1.101]
5 We demonstrate recovery of this richer structure by extracting logical forms from natural language queries against Freebase. [sent-7, score-0.612]
6 On this task, the trained semantic parser achieves 80% precision and 56% recall, despite never having seen an annotated logical form. [sent-8, score-0.817]
7 1 Introduction Semantic parsing converts natural language statements into logical forms in a meaning representation language. [sent-9, score-0.541]
8 The expressivity and utility of semantic parsing is derived from this meaning representation, which is essentially a program that is directly executable by a computer. [sent-12, score-0.371]
9 The best performing semantic parsers are trained using extensive manual annotation: typically, a number of sentences must be annotated with their desired logical form. [sent-18, score-0.675]
10 This paper presents an algorithm for training a semantic parser without per-sentence annotations. [sent-23, score-0.429]
11 The semantic parser is trained to identify relation instances from the knowledge base while simultaneously producing parses that syntactically agree with the dependency parses. [sent-25, score-1.019]
12 Combining these two sources of supervision allows us to train an accurate semantic parser for any knowledge base without annotated training data. [sent-26, score-0.861]
13 We demonstrate our approach by training a Combinatory Categorial Grammar (CCG) (Steedman, 1996) that parses sentences into logical forms containing any of 77 relations from Freebase. [sent-27, score-0.66]
14 The trained semantic parser extracts binary relations with state-of-the-art performance, while recovering considerably richer semantic structure. [sent-29, score-0.895]
15 We demonstrate recovery of this semantic structure using natural language queries PLraoncge uadgineg Lse oafr tnhineg 2,0 p1a2g Jeosin 75t C4–o7n6f5e,re Jnecjue Iosnla Enmd,p Kiroicraela, M 1e2t–h1o4ds Ju ilny N 20a1tu2r. [sent-30, score-0.391]
16 The second stage applies CCG combination rules, in this case both forms of function application, to combine these categories into a semantic parse. [sent-44, score-0.405]
17 Our weakly-supervised semantic parser predicts the correct logical form for 56% of queries, despite never seeing a labeled logical form. [sent-46, score-1.055]
18 Section 3 formulates the weakly supervised training problem for semantic parsers and presents our algorithm. [sent-49, score-0.425]
19 Section 4 describes how we applied our algorithm to construct a semantic parser for Freebase, and Section 5 presents our results. [sent-50, score-0.429]
20 This lexicon contains syntactic and semantic categories for each word. [sent-55, score-0.492]
21 f(y) ∧ g(x) ∧ LOCATEDIN(x, y) Each entry of the lexicon w := s : lmaps a word or short phrase w to a syntactic category s and a logical form l. [sent-62, score-0.55]
22 These logical forms combine during parsing to form a complete logical form for the parsed text. [sent-66, score-0.778]
23 The result of parsing is an ordered pair, containing both a syntactic parse tree and an associated logical form. [sent-69, score-0.595]
24 2 Knowledge Base The main input to our system is a propositional knowledge base K = (E, R, C, ∆), containing entities E, categories C, relations R and relation instances ∆. [sent-74, score-0.665]
25 Categories and relations are predicates which operate on entities and return truth values; categories c ∈ C are one-place predicates (CITY(e)) and r∈elations r ∈ R are twoplace predicates (LOCATEDIN(e1 , e2)). [sent-75, score-0.72]
26 The knowledge base influences the semantic parser in two ways. [sent-84, score-0.586]
27 First, CCG logical forms are constructed by combining categories, relations and entities from the knowledge base with logical connectives; hence, the predicates in the knowledge base determine the expressivity of the parser’s semantic representation. [sent-85, score-1.633]
28 Second, the known relation instances r(e1, e2) ∈ ∆ are used as weak supervision to train the se)m ∈ant ∆ic parser. [sent-86, score-0.636]
29 3 Weakly Supervised Semantic Parsing We define weakly supervised semantic parsing as the following learning problem. [sent-87, score-0.427]
30 A CCG lexicon Λ that produces logical forms containing predicates from K. [sent-93, score-0.663]
31 Parameters for the CCG that produce correct semantic parses ‘ for sentences s ∈ S. [sent-102, score-0.375]
32 This problem is ill-posed without additional assumptions: since the correct logical form for a sen- θ tence is never observed, there is no a priori reason to prefer one semantic parse to another. [sent-103, score-0.692]
33 Our training algorithm makes two assumptions about correct semantic parses, which are encoded as weak supervision constraints. [sent-104, score-0.635]
34 The correct semantic parse of a sentence s contains a subset of the syntactic dependencies contained in a dependency parse of s. [sent-110, score-0.643]
35 Our weakly supervised training uses these constraints as a proxy for labeled semantic parses. [sent-111, score-0.364]
36 First, the algorithm constructs a graphical model that contains both the semantic parser and constant factors encoding the above two constraints. [sent-113, score-0.514]
37 This graphical model is then used to estimate parameters θ for the semantic parser, essentially optimizing θ to produce parses that satisfy the weak supervision constraints. [sent-114, score-0.871]
38 1 Encoding the Weak Supervision Constraints The first step of training constructs a graphical model containing the semantic parser and two weak supervision constraints. [sent-117, score-0.93]
39 However, the first weak supervision constraint couples the semantic parses for every sentence s ∈ S. [sent-118, score-0.863]
40 The semantic constraint couples the extractions for all sentences S(e1,e2), so the graphical model is instantiated once per (e1, e2) tuple. [sent-123, score-0.589]
41 The model has 4 types of random variables and values: Si = si represents a sentence, Li = ‘i represents a semantic parse, Zi = zi represents the satisfaction of the syntactic constraint and Yr = yr repre- sents the truth value of relation r. [sent-124, score-0.86]
42 Γ represents the semantic parser, which is parametrized by θ and produces a semantic parse ‘i for each sentence si. [sent-128, score-0.632]
43 Ψ and are deterministic factors representing the two weak supervision constraints. [sent-129, score-0.416]
44 Φ Semantic Parser The factor Γ represents the semantic parser, which is a log-linear probabilistic CCG using the input lexicon Λ. [sent-131, score-0.37]
45 Given a sentence s and parameters θ, the parser defines an unnormalized probability distribu- tion over semantic parses ‘, each of which includes both a syntactic CCG parse tree and logical form. [sent-132, score-1.076]
46 Γ and weak supervision constraints Ψ and Φ, instantiated for an (e1, e2) tuple occurring in 2 sentences S1 and S2, with corresponding semantic parses L1 and L2. [sent-133, score-0.894]
47 Semantic Constraint The semantic constraint states that, given an entity tuple (e1, e2), every relation instance r(e1, e2) ∈ ∆ must be expressed somewhere in S(e1 ,e2) . [sent-140, score-0.553]
48 Fu)r ∈the ∆rmore, no semantic parse can express a relation instance which is not in the knowledge base. [sent-141, score-0.602]
49 The graphical model contains a semantic constraint factor Ψ and one binary variable Yr for each relation r in the knowledge base. [sent-144, score-0.607]
50 The Ψ factor determines whether each semantic parse in ‘ extracts a relation between e1 and e2. [sent-146, score-0.654]
51 3 describes the features used by our semantic parser for Freebase. [sent-150, score-0.429]
52 EXTRACTS(‘i, r, e1, e2) 0 otherwise The EXTRACTS function determines the relation instances that are asserted by a semantic parse ‘. [sent-153, score-0.636]
53 The syntactic constraint penalizes ungrammatical parses by encouraging the semantic parser to produce parse trees that agree with a dependency parse of the same sentence. [sent-158, score-1.101]
54 Specifically, the syntactic constraint requires the predicate-argument structure of the CCG parse to agree with the predicate-argument structure of the dependency parse. [sent-159, score-0.397]
55 The weak supervision variables, y, z, are the output of the model. [sent-167, score-0.416]
56 This setting trains the sema)n ∈tic parser t o0 extract every true relation instance between (e1, e2) from some sentence in S(e1,e2), while simultaneously avoiding incorrect instances. [sent-169, score-0.378]
57 Training optimizes the semantic parser parameters θ to predict Y = yj , Z = zj given S = sj. [sent-172, score-0.429]
58 Therefore, it is solved by finding the maximum probability assignment ‘, then choosing values for y and z that satisfy the weak supervision constraints. [sent-176, score-0.452]
59 When y and z are given, t,hse; θinference procedure must restrict its search to the parses ‘ which satisfy these weak supervision constraints. [sent-178, score-0.567]
60 We then check the value of for each generated parse and eliminate parses which do not satisfy this syntactic constraint. [sent-183, score-0.37]
61 4 Building a Grammar for Freebase We apply the training algorithm from the previous section to produce a semantic parser for a subset of Freebase. [sent-186, score-0.429]
62 In this section, we assume access to a knowledge base K = (E, C, R, ∆), a corpus of dependencyparsed sentences S and a procedure for identifying mentions of entities in sentences. [sent-188, score-0.369]
63 1 Constructing the Lexicon Λ The first step in constructing the semantic parser is defining a lexicon Λ. [sent-190, score-0.546]
64 e After instantiating lexical categories for each sentence in S, we prune infrequent lexical categories to improve parser efficiency. [sent-199, score-0.474]
65 The instantiated lexicon represents the semantics of words and phrases as conjunctions of predicates from the knowledge base, possibly including existentially quantified variables and λ expressions. [sent-213, score-0.464]
66 2 Extensions to CCG The semantic parser is trained using sentences from a web corpus, which contains many out-of-domain words. [sent-222, score-0.47]
67 5 Evaluation In this section, we evaluate the performance of a semantic parser for Freebase, trained using our weakly-supervised algorithm. [sent-235, score-0.429]
68 Empirical comparison is somewhat difficult because the most comparable previous work weakly-supervised relation extraction uses a shallower semantic representation. [sent-236, score-0.43]
69 The validation set was used to estimate performance during algorithm develop2These relations are defined by a set of MQL queries and potentially traverse multiple relation links. [sent-247, score-0.436]
70 We compare our semantic parser to MULTIR (Hoffmann et al. [sent-260, score-0.429]
71 This method uses the same weak supervision constraint and parameter estimation procedure, but replaces the semantic parser by a linear classifier. [sent-262, score-0.925]
72 Both the semantic parser and MULTIR were trained by running 5 iterations of the structured per4Note that the positive/negative ratio was much lower without the length filter or entity disambiguation, which is partly why filtering was performed. [sent-265, score-0.474]
73 R oTrh NeO parser parses ethe sentence without considering the entities marked in the sentence, then applies the EXTRACTS function defined in Section 3. [sent-272, score-0.414]
74 We compare three versions of the semantic parser: PARSE, which is the basic semantic parser, PARSE+DEP which additionally observes the correct dependency parse at test time, and PARSE-DEP which is trained without the syntactic constraint. [sent-274, score-0.702]
75 The difference between PARSE+DEP’s aggregate and sentential precision stems from the fact that PARSE+DEP extracts each relation instance from more sentences than either MULTIR or PARSE. [sent-289, score-0.525]
76 For example, the semantic parsers learn that “in” often combines with a city to form a prepositional phrase; the parsers can apply this knowledge to identify city arguments of any relation. [sent-295, score-0.572]
77 However, MULTIR is capable of higher recall, since its dependency parse features can represent syntactic dependencies that cannot be represented by our semantic parsers. [sent-296, score-0.483]
78 3 Natural Language Database Queries The second experiment measures our trained parser’s ability to correctly translate natural language queries into logical queries against Freebase. [sent-299, score-0.571]
79 ” Each candidate query was then annotated with a logical form using categories and relations from the knowledge base; candidate queries without satisfactory logical forms were discarded. [sent-304, score-1.177]
80 Example queries with their annotated logical forms are shown in Table 3. [sent-307, score-0.572]
81 Precision is the percentage of successfully parsed queries for which the correct logical form was predicted. [sent-310, score-0.442]
82 Recall is the percentage of all queries for which the correct logical form was predicted. [sent-311, score-0.442]
83 This evaluation demonstrates that the semantic parser successfully interprets common nouns and identifies mul- tiple relations with shared arguments. [sent-312, score-0.531]
84 5c3a62l Table 4: Precision and recall for predicting logical forms of natural language queries against Freebase. [sent-317, score-0.531]
85 Several recent papers have attempted to reduce the amount of human supervision required to train a semantic parser. [sent-331, score-0.453]
86 One line of work eliminates the need for an annotated logical form, instead using only the correct answer for a database query (Liang et al. [sent-332, score-0.428]
87 It is also possible to self-train a semantic parser without any labeled data (Goldwasser et al. [sent-337, score-0.429]
88 This work extends weakly supervised relation extraction to produce richer semantic structure, using only slightly more supervision in the form of dependency parses. [sent-346, score-0.849]
89 7 Discussion This paper presents a method for training a semantic parser using only a knowledge base and a corpus of unlabeled sentences. [sent-347, score-0.586]
90 Our key observation is that multiple forms of weak supervision can be combined to train an accurate semantic parser: semantic supervision from a knowledge base offacts, and syntactic supervision in the form of a standard dependency parser. [sent-348, score-1.672]
91 We presented an algorithm for training a semantic parser in the form of a probabilistic Combinatory Categorial Grammar, using these two types of weak supervision. [sent-349, score-0.611]
92 We used this algorithm to train a semantic parser for an ontology of 77 Freebase predicates, using Freebase itself as the weak semantic supervision. [sent-350, score-0.83]
93 Experimental results show that our trained semantic parser extracts binary relations as well as a state-of-the-art weakly supervised relation extractor (Hoffmann et al. [sent-351, score-0.951]
94 Further experiments 763 tested our trained parser’s ability to extract more complex meanings from sentences, including logical forms involving conjunctions ofmultiple relation and category predicates with shared arguments (e. [sent-353, score-0.87]
95 The semantic parser correctly interpreted 56% of these queries, despite the broad domain and never having seen an annotated logical form. [sent-358, score-0.783]
96 Together, these two experimental analyses suggest that the combination of syntactic and semantic weak supervision is indeed a sufficient basis for training semantic parsers for a diverse range of corpora and predicate ontologies. [sent-359, score-0.974]
97 A statistical semantic parser that integrates syntax and semantics. [sent-403, score-0.429]
98 Knowledgebased weak supervision for information extraction of overlapping relations. [sent-413, score-0.416]
99 Learning to map sentences to logical form: structured classification with probabilistic categorial grammars. [sent-486, score-0.426]
100 Online learning of relaxed ccg grammars for parsing to logical form. [sent-491, score-0.69]
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