acl acl2013 acl2013-358 knowledge-graph by maker-knowledge-mining

358 acl-2013-Transition-based Dependency Parsing with Selectional Branching

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Author: Jinho D. Choi ; Andrew McCallum

Abstract: We present a novel approach, called selectional branching, which uses confidence estimates to decide when to employ a beam, providing the accuracy of beam search at speeds close to a greedy transition-based dependency parsing approach. Selectional branching is guaranteed to perform a fewer number of transitions than beam search yet performs as accurately. We also present a new transition-based dependency parsing algorithm that gives a complexity of O(n) for projective parsing and an expected linear time speed for non-projective parsing. With the standard setup, our parser shows an unlabeled attachment score of 92.96% and a parsing speed of 9 milliseconds per sentence, which is faster and more accurate than the current state-of-the-art transitionbased parser that uses beam search.

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Summary: the most important sentenses genereted by tfidf model

sentIndex sentText sentNum sentScore

1 edu @ Abstract We present a novel approach, called selectional branching, which uses confidence estimates to decide when to employ a beam, providing the accuracy of beam search at speeds close to a greedy transition-based dependency parsing approach. [sent-4, score-1.203]

2 Selectional branching is guaranteed to perform a fewer number of transitions than beam search yet performs as accurately. [sent-5, score-0.982]

3 We also present a new transition-based dependency parsing algorithm that gives a complexity of O(n) for projective parsing and an expected linear time speed for non-projective parsing. [sent-6, score-1.088]

4 96% and a parsing speed of 9 milliseconds per sentence, which is faster and more accurate than the current state-of-the-art transitionbased parser that uses beam search. [sent-8, score-0.911]

5 1 Introduction Transition-based dependency parsing has gained considerable interest because it runs fast and performs accurately. [sent-9, score-0.481]

6 Transition-based parsing gives complexities as low as O(n) and O(n2) for projective and non-projective parsing, respectively (Nivre, 2008). [sent-10, score-0.49]

7 1 The complexity is lower for projective parsing because a parser can deterministically skip tokens violating projectivity, while this property is not assumed for non-projective parsing. [sent-11, score-0.66]

8 1We refer parsing approaches that produce only projective dependency trees as projective parsing and both projective and non-projective dependency trees as non-projective parsing. [sent-13, score-1.503]

9 edu Greedy transition-based dependency parsing has been widely deployed because of its speed (Cer et al. [sent-16, score-0.494]

10 , 2010); however, state-of-the-art accuracies have been achieved by globally optimized parsers using beam search (Zhang and Clark, 2008; Huang and Sagae, 2010; Zhang and Nivre, 2011; Bohnet and Nivre, 2012). [sent-17, score-0.561]

11 These approaches generate multiple transition sequences given a sentence, and pick one with the highest confidence. [sent-18, score-0.366]

12 Coupled with dynamic programming, transition-based dependency parsing with beam search can be done very efficiently and gives significant improvement to parsing accuracy. [sent-19, score-1.196]

13 One downside of beam search is that it always uses a fixed size of beam even when a smaller size of beam is sufficient for good results. [sent-20, score-1.253]

14 In our experiments, a greedy parser performs as accurately as a parser that uses beam search for about 64% of time. [sent-21, score-0.835]

15 Thus, it is preferred if the beam size is not fixed but proportional to the number of low confidence predictions that a greedy parser makes, in which case, fewer transition sequences need to be explored to produce the same or similar parse output. [sent-22, score-1.187]

16 We first present a new transition-based parsing algorithm that gives a complexity of O(n) for projective parsing and an expected linear time speed for non-projective parsing. [sent-23, score-0.927]

17 We then introduce selectional branching that uses confidence estimates to decide when to employ a beam. [sent-24, score-0.433]

18 With our new approach, we achieve a higher parsing accuracy than the current state-of-the-art transition-based parser that uses beam search and a much faster speed. [sent-25, score-0.848]

19 2 Transition-based dependency parsing We introduce a transition-based dependency parsing algorithm that is a hybrid between Nivre’s arceager and list-based algorithms (Nivre, 2003; Nivre, 2008). [sent-26, score-0.897]

20 Nivre’s arc-eager is a projective parsing al- gorithm showing a complexity of O(n). [sent-27, score-0.489]

21 Nivre’s list-based algorithm is a non-projective parsing algorithm showing a complexity of O(n2). [sent-28, score-0.38]

22 the bottom 3 transitions are inherited from Nivre’s arc-eager and list-based algorithms, respectively. [sent-43, score-0.288]

23 2 Each parsing state is represented as a tuple (σ, δ, β, A), where σ is a stack containing processed tokens, δ is a deque containing tokens popped out of σ but will be pushed back into σ in later parsing states to handle non-projectivity, and β is a buffer containing unprocessed tokens. [sent-44, score-0.753]

24 At any parsing state, a decision is made by comparing the top of σ, wi, and the first element of β, wj. [sent-51, score-0.268]

25 LEFTl-∗ and RIGHTl-∗ are performed when wj is the head-∗ ∗o afn wi Rwith a dependency olarmbeeld l, wanhedn nv iwce versa. [sent-53, score-0.319]

26 ∗-SHIFT is performed when no dependency is fou∗nd for wj and any token in σ other than wi. [sent-56, score-0.268]

27 After ∗-PASS, wi is m∗oved to the front of δ so it 2The parsing complexity of a transition-based dependency parsing algorithm is determined by the number of transitions performed with respect to the number of tokens in a sentence, say n (Kübler et al. [sent-61, score-1.191]

28 Each transition needs to satisfy certain preconditions to ensure the properties of a well-formed dependency graph (Nivre, 2008); they are described in Table 2. [sent-64, score-0.454]

29 When a parser is trained on only projective trees, our algorithm learns only the top 4 transitions and produces only projective trees during decoding. [sent-66, score-0.825]

30 In this case, it performs at most 2n − 1 transitions per sentence so the complexity ins O(n). [sent-67, score-0.331]

31 When a parser is trained on a mixture of projective and nonprojective trees, our algorithm learns all transitions and produces both kinds of trees during decoding. [sent-68, score-0.695]

32 In this case, it performs at most transitions so the complexity is O(n2). [sent-69, score-0.331]

33 However, because of the presence of ∗-SHIFT and ∗-REDUCE, our algorithm is capable of skipping or removing tokens during non-projective parsing, which allows it to show a linear time parsing speed in practice. [sent-70, score-0.449]

34 n(n2+1) isn sTrtoai1 0864230 0 0 102 304 506 70 Sentence length Figure 1: The # of transitions performed during training with respect to sentence lengths for Dutch. [sent-71, score-0.359]

35 Figure 1 shows the total number of transitions performed during training with respect to sentence lengths for Dutch. [sent-73, score-0.359]

36 Even with such a high number of nonprojective dependencies, our parsing algorithm still shows a linear growth in transitions. [sent-77, score-0.395]

37 Table 3 shows a transition sequence generated by our parsing algorithm using gold-standard decisions. [sent-78, score-0.631]

38 After w6 and w7 are compared, w6 is popped out of σ (state 12) because it is not needed for later parsing states. [sent-82, score-0.328]

39 Despite all the benefits, there is one downside of this approach; it generates a fixed number of transition sequences no matter how confident the onebest sequence is. [sent-86, score-0.488]

40 Thus, it is preferred if the beam size is not fixed but proportional to the number of low confidence predictions made for the one-best sequence. [sent-88, score-0.548]

41 selectional branching, our parser shows slightly 3The ‘one-best sequence’ is a transition sequence generated by taking only the best prediction at each parsing state. [sent-91, score-0.939]

42 1054 higher parsing accuracy than the current state-ofthe-art transition-based parser using beam search, and performs about 3 times faster. [sent-92, score-0.858]

43 sij represents a parsing state, where iis the index of the current transition sequence and j is the index of the current parsing state (e. [sent-95, score-0.966]

44 , s12 represents the 2nd parsing state in the 1st transition sequence). [sent-97, score-0.59]

45 4 Then, new transition sequences are generated by using the b highest scoring predictions in λ, where b is the beam size. [sent-111, score-0.833]

46 The same greedy parser is used to generate these new sequences although it now starts with s1j instead of an initial parsing state, applies pkj to s1j, and performs further transitions. [sent-113, score-0.706]

47 Once all transition sequences are generated, a parse tree is built from a sequence with the highest score. [sent-114, score-0.436]

48 Note that assigning a greater k may increase |λ| but not the total number of transition sequences generated, which is restricted by the beam size, b. [sent-117, score-0.747]

49 Since each sequence Ti>1 branches from T1, selectional branching performs fewer transitions than beam search: at least transitions are inherited from T1, d(d2−1) 4 4λ is initially empty, which is hidden in Figure 2. [sent-118, score-1.46]

50 where d = min(b, |λ| + 1) ; thus, it performs that many tdra =nsi mtiionn(sb l,e|λss| +th 1an) beam search (see the left lower triangle in Figure 2). [sent-119, score-0.475]

51 Furthermore, selectional branching generates a d number of sequences, where d is proportional to the number of low confidence predictions made by T1. [sent-120, score-0.519]

52 To sum up, selectional branching generates the same or fewer transition sequences than beam search and each sequence Ti>1 performs fewer transitions than T1; thus, it performs faster than beam search in general given the same beam size. [sent-121, score-2.474]

53 3 Finding low confidence predictions For each parsing state sij, a prediction is made by generating a feature vector xij ∈ X, feeding it into a classifier C1 that uses a featur∈e map Φ(x, y) and a weight vector w to measure a score for each label y ∈ Y, and choosing a label with the highest score. [sent-123, score-0.529]

54 4 Finding the best transition sequence Let Pi be a list of all predictions that lead to generate a transition sequence Ti. [sent-134, score-0.734]

55 This is because our algorithm does not guarantee the same number of transitions for every sequence, so the sum of all scores would weigh more on sequences with more transitions. [sent-141, score-0.396]

56 We experimented with both the sum and the average, and taking the average led to slightly higher parsing accuracy. [sent-142, score-0.268]

57 5 Bootstrapping transition sequences During training, a training instance is generated for each parsing state sij by taking a feature vector xij and its true label yij. [sent-144, score-0.74]

58 To generate multiple transition sequences during training, the bootstrapping technique of Choi and Palmer (201 1) is used, which is described in Algorithm 1. [sent-145, score-0.454]

59 Then, the next model Mr is trained on all data but this time, Mr−1 is used to generate multiple transition sequences (line 7-8). [sent-149, score-0.366]

60 Among all transition sequences generated by Mr−1, training instances from only T1 and Tg are used to train Mr, where T1 is the one-best sequence and Tg is a sequence giving the most accurate parse output compared to the gold-standard tree. [sent-150, score-0.506]

61 5Alternatively, the dynamic oracle approach of Goldberg and Nivre (2012) can be used to generate multiple transition sequences, which is expected to show similar results. [sent-152, score-0.295]

62 1 Corpora For projective parsing experiments, the Penn English Treebank (Marcus et al. [sent-192, score-0.455]

63 2 Feature engineering For English, we mostly adapt features from Zhang and Nivre (201 1) who have shown state-of-the-art parsing accuracy for transition-based dependency parsing. [sent-199, score-0.461]

64 For selectional branching, the margin threshold m and the beam size b need to be tuned (Section 3. [sent-211, score-0.614]

65 For this development set, the beam sizeb o=f 6644| 8a0nd 80 gave the exact same result, so we kept the one with a larger beam size (b = 80). [sent-230, score-0.829]

66 9 b1362 4=|80 Margin Figure 3: Parsing accuracies with respect to margins and beam sizes on the English development set. [sent-237, score-0.654]

67 Figure 3 shows parsing accuracies with respect to different margins and beam sizes on the English development set. [sent-239, score-0.922]

68 These parameters need to be tuned jointly because different margins prefer different beam sizes. [sent-240, score-0.514]

69 Figure 4 shows parsing accuracies with respect to ADAGRAD and bootstrap iterations on the English development set. [sent-250, score-0.481]

70 Trsnatsio1,620840 0 0 , 0 0 0 0 0 102304506708 Beam size = 1, 2, 4, 8, 16, 32, 64, 80 Figure 5: The total number oftransitions performed during decoding with respect to beam sizes on the English development set. [sent-255, score-0.645]

71 Figure 5 shows the total number of transitions performed during decoding with respect to beam sizes on the English development set (1,700 sentences, 40,1 17 tokens). [sent-256, score-0.817]

72 With selectional branching, the number of transitions grows logarithmically as the beam size increases whereas it would have grown linearly if beam search were used. [sent-257, score-1.245]

73 This implies that about 64% of time, our greedy parser performs as accurately as our non-greedy parser using selectional branching. [sent-260, score-0.574]

74 The first block shows results from transition-based dependency parsers using beam search. [sent-268, score-0.653]

75 The second block shows results from other kinds of parsing approaches (e. [sent-269, score-0.303]

76 bt and bd indicate the beam sizes used during training and decoding, respectively. [sent-276, score-0.617]

77 Our parser shows higher accuracy than Zhang and Nivre (201 1), which is the current state-of-the-art transition-based parser that uses beam search. [sent-280, score-0.663]

78 Our parser gives a comparative accuracy to Koo and Collins (2010) that is a 3rdorder graph-based parsing approach. [sent-282, score-0.46]

79 For a proof of concept, we run the same model, trained with bt = 80, but decode with different beam sizes using the same margin. [sent-303, score-0.479]

80 More importantly, bd = 16 shows about the same parsing speed as bd = 80, which indicates that selectional branching automatically reduced down the beam size by estimating low confidence predictions, so even if we assigned a larger beam size for decoding, it would have performed as efficiently. [sent-306, score-1.909]

81 This implies that we no longer need to be so conscious about the beam size during decoding. [sent-307, score-0.415]

82 Another interesting part is that (bt = 80, bd = 1) shows higher accuracy than (bt = 1, bd = 1) ; this implies that our training method of bootstrapping transition sequences can improve even a greedy parser. [sent-308, score-0.872]

83 Notice that our greedy parser shows higher accuracy than many other greedy parsers (Hall et al. [sent-309, score-0.453]

84 , 2006; Goldberg and Elhadad, 2010) because it uses the non-local features of Zhang and Nivre (201 1) and the bootstrapping technique of Choi and Palmer (201 1) that had not been used for most other greedy parsing approaches. [sent-310, score-0.466]

85 5 Non-projective parsing experiments Table 5 shows comparison between state-of-the-art parsers and our approach for four languages with non-projective dependencies. [sent-312, score-0.344]

86 (2010) uses integer linear programming for the optimization of their parsing model. [sent-320, score-0.34]

87 88), which finds only the one-best sequences during decoding although it is trained on multiple transition sequences (see Section 4. [sent-322, score-0.517]

88 Our greedy approach outperforms both Nivre (2009) and Fernández-González and Gómez-Rodríguez (2012) who use different nonprojective parsing algorithms. [sent-325, score-0.435]

89 tsnirnTiaso1 3086420 0 0 0 102030405060 Sentence length Figure 6: The # of transitions performed during decoding with respect to sentence lengths for Dutch. [sent-326, score-0.398]

90 Figure 6 shows the number oftransitions performed during decoding with respect to sentence lengths for Dutch using bd = 1. [sent-327, score-0.33]

91 Our parser still shows a linear growth in transition during decoding. [sent-328, score-0.41]

92 5 Related work Our parsing algorithm is most similar to Choi and Palmer (201 1) who integrated our LEFT-REDUCE transition into Nivre’s list-based algorithm. [sent-329, score-0.561]

93 Our selectional branching method is most relevant to Zhang and Clark (2008) who introduced a transition-based dependency parsing model that uses beam search. [sent-332, score-1.196]

94 Our work is distinguished from theirs because we use selectional branching instead. [sent-336, score-0.386]

95 6 Conclusion We present selectional branching that uses confidence estimates to decide when to employ a beam. [sent-337, score-0.433]

96 Coupled with our new hybrid parsing algorithm, ADAGRAD, rich non-local features, and bootstrapping, our parser gives higher parsing accuracy than most other transition-based dependency parsers in multiple languages and shows faster parsing speed. [sent-338, score-1.233]

97 It is interesting to see that our greedy parser outperformed most other greedy dependency parsers. [sent-339, score-0.506]

98 This is because our parser used both bootstrapping and Zhang and Nivre (201 1)’s non-local features, which had not been used by other greedy parsers. [sent-340, score-0.323]

99 We also plan to implement the typical beam search approach to make a direct comparison to our selectional branching. [sent-343, score-0.585]

100 A Tale of Two Parsers: investigating and combining graphbased and transition-based dependency parsing using beam-search. [sent-534, score-0.429]

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3 0.87667882 245 acl-2013-Modeling Human Inference Process for Textual Entailment Recognition

Author: Hen-Hsen Huang ; Kai-Chun Chang ; Hsin-Hsi Chen

Abstract: This paper aims at understanding what human think in textual entailment (TE) recognition process and modeling their thinking process to deal with this problem. We first analyze a labeled RTE-5 test set and find that the negative entailment phenomena are very effective features for TE recognition. Then, a method is proposed to extract this kind of phenomena from text-hypothesis pairs automatically. We evaluate the performance of using the negative entailment phenomena on both the English RTE-5 dataset and Chinese NTCIR-9 RITE dataset, and conclude the same findings.

4 0.87439317 61 acl-2013-Automatic Interpretation of the English Possessive

Author: Stephen Tratz ; Eduard Hovy

Abstract: The English ’s possessive construction occurs frequently in text and can encode several different semantic relations; however, it has received limited attention from the computational linguistics community. This paper describes the creation of a semantic relation inventory covering the use of ’s, an inter-annotator agreement study to calculate how well humans can agree on the relations, a large collection of possessives annotated according to the relations, and an accurate automatic annotation system for labeling new examples. Our 21,938 example dataset is by far the largest annotated possessives dataset we are aware of, and both our automatic classification system, which achieves 87.4% accuracy in our classification experiment, and our annotation data are publicly available.

5 0.87111253 286 acl-2013-Psycholinguistically Motivated Computational Models on the Organization and Processing of Morphologically Complex Words

Author: Tirthankar Dasgupta

Abstract: In this work we present psycholinguistically motivated computational models for the organization and processing of Bangla morphologically complex words in the mental lexicon. Our goal is to identify whether morphologically complex words are stored as a whole or are they organized along the morphological line. For this, we have conducted a series of psycholinguistic experiments to build up hypothesis on the possible organizational structure of the mental lexicon. Next, we develop computational models based on the collected dataset. We observed that derivationally suffixed Bangla words are in general decomposed during processing and compositionality between the stem . and the suffix plays an important role in the decomposition process. We observed the same phenomena for Bangla verb sequences where experiments showed noncompositional verb sequences are in general stored as a whole in the ML and low traces of compositional verbs are found in the mental lexicon. 1 IInnttrroodduuccttiioonn Mental lexicon is the representation of the words in the human mind and their associations that help fast retrieval and comprehension (Aitchison, 1987). Words are known to be associated with each other in terms of, orthography, phonology, morphology and semantics. However, the precise nature of these relations is unknown. An important issue that has been a subject of study for a long time is to identify the fundamental units in terms of which the mental lexicon is i itkgp .ernet . in organized. That is, whether lexical representations in the mental lexicon are word based or are they organized along morphological lines. For example, whether a word such as “unimaginable” is stored in the mental lexicon as a whole word or do we break it up “un-” , “imagine” and “able”, understand the meaning of each of these constituent and then recombine the units to comprehend the whole word. Such questions are typically answered by designing appropriate priming experiments (Marslen-Wilson et al., 1994) or other lexical decision tasks. The reaction time of the subjects for recognizing various lexical items under appropriate conditions reveals important facts about their organization in the brain. (See Sec. 2 for models of morphological organization and access and related experiments). A clear understanding of the structure and the processing mechanism of the mental lexicon will further our knowledge of how the human brain processes language. Further, these linguistically important and interesting questions are also highly significant for computational linguistics (CL) and natural language processing (NLP) applications. Their computational significance arises from the issue of their storage in lexical resources like WordNet (Fellbaum, 1998) and raises the questions like, how to store morphologically complex words, in a lexical resource like WordNet keeping in mind the storage and access efficiency. There is a rich literature on organization and lexical access of morphologically complex words where experiments have been conducted mainly for derivational suffixed words of English, Hebrew, Italian, French, Dutch, and few other languages (Marslen-Wilson et al., 2008; Frost et al., 1997; Grainger, et al., 1991 ; Drews and Zwitserlood, 1995). However, we do not know of any such investigations for Indian languages, which 123 Sofia, BuPrlgoacreiead, iAngusgu osft 4h-e9 A 2C01L3 S.tu ?c d2en0t1 3Re Ases aorc hiat Wio nrk fsohro Cp,om papguesta 1ti2o3n–a1l2 L9in,guistics are morphologically richer than many of their Indo-European cousins. Moreover, Indian languages show some distinct phenomena like, compound and composite verbs for which no such investigations have been conducted yet. On the other hand, experiments indicate that mental representation and processing of morphologically complex words are not quite language independent (Taft, 2004). Therefore, the findings from experiments in one language cannot be generalized to all languages making it important to conduct similar experimentations in other languages. This work aims to design cognitively motivated computational models that can explain the organization and processing of Bangla morphologically complex words in the mental lexicon. Presently we will concentrate on the following two aspects:   OOrrggaanniizzaattiioonn aanndd pprroocceessssiinngg ooff BBaannggllaa PPo o l yy-mmoorrpphheemmiicc wwoorrddss:: our objective here is to determine whether the mental lexicon decomposes morphologically complex words into its constituent morphemes or does it represent the unanalyzed surface form of a word. OOrrggaanniizzaattiioonn aanndd pprroocceessssiinngg ooff BBaannggllaa ccoomm-ppoouunndd vveerrbbss ((CCVV)) :: compound verbs are the subject of much debate in linguistic theory. No consensus has been reached yet with respect to the issue that whether to consider them as unitary lexical units or are they syntactically assembled combinations of two independent lexical units. As linguistic arguments have so far not led to a consensus, we here use cognitive experiments to probe the brain signatures of verb-verb combinations and propose cognitive as well as computational models regarding the possible organization and processing of Bangla CVs in the mental lexicon (ML). With respect to this, we apply the different priming and other lexical decision experiments, described in literature (Marslen-Wilson et al., 1994; Bentin, S. and Feldman, 1990) specifically for derivationally suffixed polymorphemic words and compound verbs of Bangla. Our cross-modal and masked priming experiment on Bangla derivationally suffixed words shows that morphological relatedness between lexical items triggers a significant priming effect, even when the forms are phonologically/orthographically unrelated. These observations are similar to those reported for English and indicate that derivationally suffixed words in Bangla are in general accessed through decomposition of the word into its constituent morphemes. Further, based on the experimental data we have developed a series of computational models that can be used to predict the decomposition of Bangla polymorphemic words. Our evaluation result shows that decom- position of a polymorphemic word depends on several factors like, frequency, productivity of the suffix and the compositionality between the stem and the suffix. The organization of the paper is as follows: Sec. 2 presents related works; Sec. 3 describes experiment design and procedure; Sec. 4 presents the processing of CVs; and finally, Sec. 5 concludes the paper by presenting the future direction of the work. 2 RReellaatteedd WWoorrkkss 2. . 11 RReepprreesseennttaattiioonn ooff ppoollyymmoorrpphheemmiicc wwoorrddss Over the last few decades many studies have attempted to understand the representation and processing of morphologically complex words in the brain for various languages. Most of the studies are designed to support one of the two mutually exclusive paradigms: the full-listing and the morphemic model. The full-listing model claims that polymorphic words are represented as a whole in the human mental lexicon (Bradley, 1980; Butterworth, 1983). On the other hand, morphemic model argues that morphologically complex words are decomposed and represented in terms of the smaller morphemic units. The affixes are stripped away from the root form, which in turn are used to access the mental lexicon (Taft and Forster, 1975; Taft, 1981 ; MacKay, 1978). Intermediate to these two paradigms is the partial decomposition model that argues that different types of morphological forms are processed separately. For instance, the derived morphological forms are believed to be represented as a whole, whereas the representation of the inflected forms follows the morphemic model (Caramazza et al., 1988). Traditionally, priming experiments have been used to study the effects of morphology in language processing. Priming is a process that results in increase in speed or accuracy of response to a stimulus, called the target, based on the occurrence of a prior exposure of another stimulus, called the prime (Tulving et al., 1982). Here, subjects are exposed to a prime word for a short duration, and are subsequently shown a target word. The prime and target words may be morphologically, phonologically or semantically re124 lated. An analysis of the effect of the reaction time of subjects reveals the actual organization and representation of the lexicon at the relevant level. See Pulvermüller (2002) for a detailed account of such phenomena. It has been argued that frequency of a word influences the speed of lexical processing and thus, can serve as a diagnostic tool to observe the nature and organization of lexical representations. (Taft, 1975) with his experiment on English inflected words, argued that lexical decision responses of polymorphemic words depends upon the base word frequency. Similar observation for surface word frequency was also observed by (Bertram et al., 2000;Bradley, 1980;Burani et al., 1987;Burani et al., 1984;Schreuder et al., 1997; Taft 1975;Taft, 2004) where it has been claimed that words having low surface frequency tends to decompose. Later, Baayen(2000) proposed the dual processing race model that proposes that a specific morphologically complex form is accessed via its parts if the frequency of that word is above a certain threshold of frequency, then the direct route will win, and the word will be accessed as a whole. If it is below that same threshold of frequency, the parsing route will win, and the word will be accessed via its parts. 2. . 22 RReepprreesseennttaattiioonn ooff CCoommppoouunndd A compound verb (CV) consists of two verbs (V1 and V2) acting as and expresses a single expression For example, in the sentence VVeerrbbss a sequence of a single verb of meaning. রুটিগুল ো খেল খেল ো (/ruTigulo kheYe phela/) ―bread-plural-the eat and drop-pres. Imp‖ ―Eat the breads‖ the verb sequence “খেল খেল ো (eat drop)” is an example of CV. Compound verbs are a special phenomena that are abundantly found in IndoEuropean languages like Indian languages. A plethora of works has been done to provide linguistic explanations on the formation of such word, yet none so far has led to any consensus. Hook (1981) considers the second verb V2 as an aspectual complex comparable to the auxiliaries. Butt (1993) argues CV formations in Hindi and Urdu are either morphological or syntactical and their formation take place at the argument struc- ture. Bashir (1993) tried to construct a semantic analysis based on “prepared” and “unprepared mind”. Similar findings have been proposed by Pandharipande (1993) that points out V1 and V2 are paired on the basis of their semantic compatibility, which is subject to syntactic constraints. Paul (2004) tried to represent Bangla CVs in terms of HPSG formalism. She proposes that the selection of a V2 by a V1 is determined at the semantic level because the two verbs will unify if and only if they are semantically compatible. Since none of the linguistic formalism could satisfactorily explain the unique phenomena of CV formation, we here for the first time drew our attention towards psycholinguistic and neurolinguistic studies to model the processing of verb-verb combinations in the ML and compare these responses with that of the existing models. 3 TThhee PPrrooppoosseedd AApppprrooaacchheess 3. . 11 TThhee ppssyycchhoolliinngguuiissttiicc eexxppeerriimmeennttss We apply two different priming experiments namely, the cross modal priming and masked priming experiment discussed in (Forster and Davis, 1984; Rastle et al., 2000;Marslen-Wilson et al., 1994; Marslen-Wilson et al., 2008) for Bangla morphologically complex words. Here, the prime is morphologically derived form of the target presented auditorily (for cross modal priming) or visually (for masked priming). The subjects were asked to make a lexical decision whether the given target is a valid word in that language. The same target word is again probed but with a different audio or visual probe called the control word. The control shows no relationship with the target. For example, baYaska (aged) and baYasa (age) is a prime-target pair, for which the corresponding control-target pair could be naYana (eye) and baYasa (age). Similar to (Marslen-Wilson et al., 2008) the masked priming has been conducted for three different SOA (Stimulus Onset Asynchrony), 48ms, 72ms and 120ms. The SOA is measured as the amount of time between the start the first stimulus till the start of the next stimulus. TCM abl-’+ Sse-+ O1 +:-DatjdgnmAshielbatArDu)f(osiAMrawnteihmsgcdaoe)lEx-npgmAchebamr)iD-gnatmprhdiYlbeaA(n ftrTsli,ae(+gnrmdisc)phroielctn)osrelated, and - implies unrelated. There were 500 prime-target and controltarget pairs classified into five classes. Depending on the class, the prime is related to the target 125 either in terms of morphology, semantics, orthography and/or Phonology (See Table 1). The experiments were conducted on 24 highly educated native Bangla speakers. Nineteen of them have a graduate degree and five hold a post graduate degree. The age of the subjects varies between 22 to 35 years. RReessuullttss:: The RTs with extreme values and incorrect decisions were excluded from the data. The data has been analyzed using two ways ANOVA with three factors: priming (prime and control), conditions (five classes) and prime durations (three different SOA). We observe strong priming effects (p<0.05) when the target word is morphologically derived and has a recognizable suffix, semantically and orthographically related with respect to the prime; no priming effects are observed when the prime and target words are orthographically related but share no morphological or semantic relationship; although not statistically significant (p>0.07), but weak priming is observed for prime target pairs that are only semantically related. We see no significant difference between the prime and control RTs for other classes. We also looked at the RTs for each of the 500 target words. We observe that maximum priming occurs for words in [M+S+O+](69%), some priming is evident in [M+S+O-](51%) and [M'+S-O+](48%), but for most of the words in [M-S+O-](86%) and [M-S-O+](92%) no priming effect was observed. 3. . 22 FFrreeqquueennccyy DDiissttrriibbuuttiioonn MMooddeellss ooff MMoo rrpphhoo-llooggiiccaall PPrroocceessssiinngg From the above results we saw that not all polymorphemic words tend to decompose during processing, thus we need to further investigate the processing phenomena of Bangla derived words. One notable means is to identify whether the stem or suffix frequency is involved in the processing stage of that word. For this, we apply different frequency based models to the Bangla polymorphemic words and try to evaluate their performance by comparing their predicted results with the result obtained through the priming experiment. MMooddeell --11:: BBaassee aanndd SSuurrffaaccee wwoorrdd ffrreeqquueennccyy ee ff-ffeecctt -- It states that the probability of decomposition of a Bangla polymorphemic word depends upon the frequency of its base word. Thus, if the stem frequency of a polymorphemic word crosses a given threshold value, then the word will decomposed into its constituent morpheme. Similar claim has been made for surface word frequency model where decomposition depends upon the frequency of the surface word itself. We have evaluated both the models with the 500 words used in the priming experiments discussed above. We have achieved an accuracy of 62% and 49% respectively for base and surface word frequency models. MMooddeell --22:: CCoommbbiinniinngg tthhee bbaassee aanndd ssuurrffaaccee wwoorrdd ffrreeq quueennccyy -- In a pursuit towards an extended model, we combine model 1 and 2 together. We took the log frequencies of both the base and the derived words and plotted the best-fit regression curve over the given dataset. The evaluation of this model over the same set of 500 target words returns an accuracy of 68% which is better than the base and surface word frequency models. However, the proposed model still fails to predict processing of around 32% of words. This led us to further enhance the model. For this, we analyze the role of suffixes in morphological processing. MMooddeell -- 33:: DDeeggrreeee ooff AAffffiixxaattiioonn aanndd SSuuffffiixx PPrroodd-uuccttiivviittyy:: we examine whether the regression analysis between base and derived frequency of Bangla words varies between suffixes and how these variations affect morphological decomposition. With respect to this, we try to compute the degree of affixation between the suffix and the base word. For this, we perform regression analysis on sixteen different Bangla suffixes with varying degree of type and token frequencies. For each suffix, we choose 100 different derived words. We observe that those suffixes having high value of intercept are forming derived words whose base frequencies are substantially high as compared to their derived forms. Moreover we also observe that high intercept value for a given suffix indicates higher inclination towards decomposition. Next, we try to analyze the role of suffix type/token ratio and compare them with the base/derived frequency ratio model. This has been done by regression analysis between the suffix type-token ratios with the base-surface frequency ratio. We further tried to observe the role of suffix productivity in morphological processing. For this, we computed the three components of productivity P, P* and V as discussed in (Hay and Plag, 2004). P is the “conditioned degree of productivity” and is the probability that we are encountering a word with an affix and it is representing a new type. P* is the “hapaxedconditioned degree of productivity”. It expresses the probability that when an entirely new word is 126 encountered it will contain the suffix. V is the “type frequency”. Finally, we computed the productivity of a suffix through its P, P* and V values. We found that decomposition of Bangla polymorphemic word is directly proportional to the productivity of the suffix. Therefore, words that are composed of productive suffixes (P value ranges between 0.6 and 0.9) like “-oYAlA”, “-giri”, “-tba” and “-panA” are highly decomposable than low productive suffixes like “-Ani”, “-lA”, “-k”, and “-tama”. The evaluation of the proposed model returns an accuracy of 76% which comes to be 8% better than the preceding models. CCoommbbiinniinngg MMooddeell --22 aanndd MMooddeell -- 33:: One important observation that can be made from the above results is that, model-3 performs best in determining the true negative values. It also possesses a high recall value of (85%) but having a low precision of (50%). In other words, the model can predict those words for which decomposition will not take place. On the other hand, results of Model-2 posses a high precision of 70%. Thus, we argue that combining the above two models can better predict the decomposition of Bangla polymorphemic words. Hence, we combine the two models together and finally achieved an overall accuracy of 80% with a precision of 87% and a recall of 78%. This surpasses the performance of the other models discussed earlier. However, around 22% of the test words were wrongly classified which the model fails to justify. Thus, a more rigorous set of experiments and data analysis are required to predict access mechanisms of such Bangla polymorphemic words. 3. . 33 SStteemm- -SSuuffffiixx CCoommppoossiittiioonnaalliittyy Compositionality refers to the fact that meaning of a complex expression is inferred from the meaning of its constituents. Therefore, the cost of retrieving a word from the secondary memory is directly proportional to the cost of retrieving the individual parts (i.e the stem and the suffix). Thus, following the work of (Milin et al., 2009) we define the compositionality of a morphologically complex word (We) as: C(We)=α 1H(We)+α α2H(e)+α α3H(W|e)+ α4H(e|W) Where, H(x) is entropy of an expression x, H(W|e) is the conditional entropy between the stem W and suffix e and is the proportionality factor whose value is computed through regression analysis. Next, we tried to compute the compositionality of the stem and suffixes in terms of relative entropy D(W||e) and Point wise mutual information (PMI). The relative entropy is the measure of the distance between the probability distribution of the stem W and the suffix e. The PMI measures the amount of information that one random variable (the stem) contains about the other (the suffix). We have compared the above three techniques with the actual reaction time data collected through the priming and lexical decision experiment. We observed that all the three information theoretic models perform much better than the frequency based models discussed in the earlier section, for predicting the decomposability of Bangla polymorphemic words. However, we think it is still premature to claim anything concrete at this stage of our work. We believe much more rigorous experiments are needed to be per- formed in order to validate our proposed models. Further, the present paper does not consider factors related to age of acquisition, and word familiarity effects that plays important role in the processing of morphologically complex words. Moreover, it is also very interesting to see how stacking of multiple suffixes in a word are processed by the human brain. 44 OOrrggaanniizzaattiioonn aanndd PPrroocceessssiinngg ooff CCoomm-ppoouunndd VVeerrbbss iinn tthhee MMeennttaall LLeexxiiccoonn Compound verbs, as discussed above, are special type of verb sequences consisting of two or more verbs acting as a single verb and express a single expression of meaning. The verb V1 is known as pole and V2 is called as vector. For example, “ওঠে পড়া ” (getting up) is a compound verb where individual words do not entirely reflects the meaning of the whole expression. However, not all V1+V2 combinations are CVs. For example, expressions like, “নিঠে য়াও ”(take and then go) and “ নিঠে আঠ ়া” (return back) are the examples of verb sequences where meaning of the whole expression can be derived from the mean- ing of the individual component and thus, these verb sequences are not considered as CV. The key question linguists are trying to identify for a long time and debating a lot is whether to consider CVs as a single lexical units or consider them as two separate units. Since linguistic rules fails to explain the process, we for the first time tried to perform cognitive experiments to understand the organization and processing of such verb sequences in the human mind. A clear understanding about these phenomena may help us to classify or extract actual CVs from other verb 127 sequences. In order to do so, presently we have applied three different techniques to collect user data. In the first technique, we annotated 4500 V1+V2 sequences, along with their example sentences, using a group of three linguists (the expert subjects). We asked the experts to classify the verb sequences into three classes namely, CV, not a CV and not sure. Each linguist has received 2000 verb pairs along with their respective example sentences. Out of this, 1500 verb sequences are unique to each of them and rest 500 are overlapping. We measure the inter annotator agreement using the Fleiss Kappa (Fleiss et al., 1981) measure (κ) where the agreement lies around 0.79. Next, out of the 500 common verb sequences that were annotated by all the three linguists, we randomly choose 300 V1+V2 pairs and presented them to 36 native Bangla speakers. We ask each subjects to give a compositionality score of each verb sequences under 1-10 point scale, 10 being highly compositional and 1 for noncompositional. We found an agreement of κ=0.69 among the subjects. We also observe a continuum of compositionality score among the verb sequences. This reflects that it is difficult to classify Bangla verb sequences discretely into the classes of CV and not a CV. We then, compare the compositionality score with that of the expert user’s annotation. We found a significant correlation between the expert annotation and the compositionality score. We observe verb sequences that are annotated as CVs (like, খেঠে খিল )কঠে খি ,ওঠে পড ,have got low compositionality score (average score ranges between 1-4) on the other hand high compositional values are in general tagged as not a cv (নিঠে য়া (come and get), নিঠে আে (return back), তুঠল খেঠেনি (kept), গনিঠে পিল (roll on floor)). This reflects that verb sequences which are not CV shows high degree of compositionality. In other words non CV verbs can directly interpret from their constituent verbs. This leads us to the possibility that compositional verb sequences requires individual verbs to be recognized separately and thus the time to recognize such expressions must be greater than the non-compositional verbs which maps to a single expression of meaning. In order to validate such claim we perform a lexical decision experiment using 32 native Bangla speakers with 92 different verb sequences. We followed the same experimental procedure as discussed in (Taft, 2004) for English polymorphemic words. However, rather than derived words, the subjects were shown a verb sequence and asked whether they recognize them as a valid combination. The reaction time (RT) of each subject is recorded. Our preliminarily observation from the RT analysis shows that as per our claim, RT of verb sequences having high compositionality value is significantly higher than the RTs for low or noncompositional verbs. This proves our hypothesis that Bangla compound verbs that show less compositionality are stored as a hole in the mental lexicon and thus follows the full-listing model whereas compositional verb phrases are individually parsed. However, we do believe that our experiment is composed of a very small set of data and it is premature to conclude anything concrete based only on the current experimental results. 5 FFuuttuurree DDiirreeccttiioonnss In the next phase of our work we will focus on the following aspects of Bangla morphologically complex words: TThhee WWoorrdd FFaammiilliiaarriittyy EEffffeecctt:: Here, our aim is to study the role of familiarity of a word during its processing. We define the familiarity of a word in terms of corpus frequency, Age of acquisition, the level of language exposure of a person, and RT of the word etc. RRoollee ooff ssuuffffiixx ttyyppeess iinn mmoorrpphhoollooggiiccaall ddeeccoo mm ppoo-ssiittiioonn:: For native Bangla speakers which morphological suffixes are internalized and which are just learnt in school, but never internalized. We can compare the representation of Native, Sanskrit derived and foreign suffixes in Bangla words. CCoommppuuttaattiioonnaall mmooddeellss ooff oorrggaanniizzaattiioonn aanndd pprroocceessssiinngg ooff BBaannggllaa ccoommppoouunndd vveerrbbss :: presently we have performed some small set of experiments to study processing of compound verbs in the mental lexicon. In the next phase of our work we will extend the existing experiments and also apply some more techniques like, crowd sourcing and language games to collect more relevant RT and compositionality data. 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